Doctoral thesis

Essays on the Predictability of Asset Returns

Can Pricing Factors Aid Industry Rotation?  

This paper explores within-asset-class predictability specifically, in the cross-section of industry returns by lev ...

Author(s) :

Navneesh Malhan, PhD

Quantitative Researcher at T.Rowe Price, USA

Abstract :

Can Pricing Factors Aid Industry Rotation?  

This paper explores within-asset-class predictability specifically, in the cross-section of industry returns by leveraging factors in standard asset pricing models. Industry returns exhibit the interplay between cross-section and time-series dynamics since they are at a level of aggregation between stocks and the broad market. By studying the time-series dynamics of industry returns, we develop an industry rotation trading strategy which is based on a signal that is generated by exploiting the fact that factors in standard asset pricing models do not predict aggregate industry returns. The underlying mechanism demonstrates the rationale for employing weakly persistent pricing factors as instruments to derive a sufficiently persistent signal to forecast industry excess return. A key result of the paper links the parameters of the return data generating process by deriving condition(s) under which the signal dominates the historical moving average as a predictor of industry returns. Under various specifications of the signal and by employing several asset pricing models, we exhibit that dynamic trading strategies based on exchange traded funds as test assets can be utilized to effectively harness industry risk-premia at the least or as a source of portable alpha at the most.

Keywords: Return predictability; industry rotation; risk premium; pricing factors; asset pricing models; pricing-error; prediction-error.


Recession Concern and Aggregate Return Predictability: Cross-Asset Evidence

This paper studies cross-asset predictability specifically, in the aggregate time-series of returns by highlighting the impact of recession concern on future returns. We introduce a measure of economic tail risk which captures the anxiety of forecasters related to impending economic downturn since expert forecasters’ views that are indicative of future economic conditions should also provide insight into asset returns. Using various predictors of cross-asset aggregate returns as controls, we study the impact of economic tail risk on return predictability. In a multivariate conditional setting, the return forecast which incorporates economic tail risk exhibits significant in-sample predictability and outperforms out-of-sample relative to the baseline forecast based on various predictors of aggregate returns. Finally, under model uncertainty, we exhibit the significant influence of economic tail risk on aggregate returns. This establishes the impact of the flight-to-safety channel on return forecasts that incorporate recession concern and thus the superiority of such forecast relative to the corresponding baseline forecast.

Keywords: Return predictability; risk premium; survey forecast; recession risk; model uncertainty; Bayesian model averaging.


Date : 01/03/2022
Thesis Committee :

Supervisor: Laurent Calvet, EDHEC Business School 

External reviewer: Michael Brandt, Duke University

Other committee members: Emmanuel Jurczenko and Enrique Schroth, EDHEC Business School

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