Doctoral thesis

Stock market momentum, investor sentiment, and the accruals anomaly

Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strate ...

Author(s) :

Seong-Han Kim, PhD

Managing Director at Singularion Asset Management (UK)

Abstract :

Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

Date : 20/01/2014
Thesis Committee :

Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

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Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

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Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

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Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Supervisor: Raman Uppal, EDHEC Business School

External reviewer: Allan Timmermann, University of California San Diego

Other committee member: René Garcia, EDHEC Business School

 

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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Dissecting Momentum witDissecting with Accruals Information: This paper examines whether excess returns from momentum-based strategies and excess returns from accruals-based strategies are to a significant extent manifestations of the same underlying phenomenon or whether they are separate. It makes three main contributions: First, it shows that the returns from the accruals-based strategies materialize to a significant extent as gradual, medium term price changes in such a way that they are also captured to a measurable degree by medium term momentum strategies. That is, part of the accruals strategy returns constitutes part of the momentum returns—and vice versa (as opposed to the strategies being independent). Second, it demonstrates that when momentum portfolios are subclassed into subgroups based on accruals measures, the subgroups show dramatic return differences, and that the accruals measure is a significant predictor of the future performance among the subgroups. Third, it documents that over longer holding periods the excess returns are dominated by accruals-based anomalies rather than by momentum-based anomalies. The implication is that momentum is an aggregate phenomenon, which can be subdivided into individual momentum components with different individual characteristics (e.g., momentum strength, evolution over time, reversal tendency, underlying heuristic bias, etc.), and that one significant element is underreaction to accruals-based information. This helps to explain the existing diversity of empirical momentum characteristics and to improve strategies that aim to take advantage of the anomalies.

Momentum Profit Volatility: The Predictive Power of Investor Sentiment Indicators:  While the overall profitability of medium-term momentum strategies is a stylized fact in finance, the returns from zero-cost momentum portfolios display significant volatility. This paper makes two main contributions: First, it examines whether market data-based investor sentiment indicator proxies can predict momentum strategy returns, to analyze which indicators are most useful and during which phase of the momentum portfolio event-time. Second, to conduct this analysis, this paper proposes two structural changes to the original Jegadeesh and Titman (1993, 2001) methodology and provides evidence for the significantly increased effectiveness of the modified framework when examining momentum portfolio returns and their correlation with external factors. The empirical evidence shows that, first, some of the analyzed sentiment indicators have indeed significant predictive power (adjusted-R2 regression values up to 0.5431) for forecasting momentum returns. Second, it is mainly the month before portfolio formation2 (and not the holding period) that displays the highest explanatory power,3 and, third, the sentiment indicators typically affect both long and short portfolio positions significantly but in different ways.

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