Essays on Conditional Inflation Hedging and Manager Performance Persistence

Persistence of luck drives funds-of-funds outperformance: This paper shows there is a third way in the debate between proponents of passive versus active management. In contrast to a common finding of underperformance in the mutual fund industry, we show that fund-of-funds composed of active managers of asset allocation funds can outperform due to the persistence of luck; such a portfolio, formed with annual sorts on ex post alpha, can outperform by an economically significant 2% p.a., on a total return  basis, net of cost. This outperformance occurs despite little timing or picking skill can be attributed to the presence of lucky periodic winners, who do not have sufficient consistency of of skill, nor magnitude of periodic alpha, to emerge as sustained winners; yet they get lucky with sufficient frequency to contibute to the persistence of funds-of-funds outperformance on an annual horizon. Among winners, only picking skill persists and there is a reversal of timing skills; consequently, to identify top managers, investors are better off using a simple alpha persistence model versus a more complicated timing model.  This study is the first to highlight the benefits of periodic winners in a funds-of-funds context.  Also, most prior mutual fund research has focused on evaluating single asset equity funds with parametric empirical procedures; this study has wider scope and relevance, since it evaluates multi-asset  managers for both picking and timing skill, and with a more comprehensive set of parametric and no-parametric procedures, over a sample period which includes two major economic booms and busts. The findings tilt the active versus passive management debate in favor of the former. While acknowledging it is too difficult for investors to identify winners funds ex ante, a funds-of-funds can beat the passive index by an economically valuable margin.

Optimal real portfolios with inflation predictability: The paper shows how ambitious retail investors can simultaneously hedge inflation and maximize risk-adjusted real returns. Unlike other researchers who solve this unconditionally, our conditional dynimic strategy crucially relies on inflation forecasts. We perform a conditional mean variance optimization on real returns to identify portfolios that are most likely to rise with inflation that is forcasted by the Survey of Professional Forecasters (SPF), which is known to be a better predictor of inflation than popular forecasting models. To evaluate this conditional 'SPF stratgy" we perform an out of sample test with quaterly rebalancing. In addition, we propose a simple new metric, "HSR" the Hedge Success Rate, to measure how often we conditionally beat inflation when inflation is high, across multiple horizons. HSR's appeal is two-fold. First, it encapsulates two factors essential to beating inflation on a gibe horizon: positive correlation with inflation when inflation is at high levels, and expected return that exceeds inflation over a specific horizon. Second, it is scale-free and simplifies comparison across horizons, allowing for a HSR term structure. To illustrate this metric's value-add, we first evaluate passive real return strategies. Both the HSR and return moments confirm the conditional SPF strategy is superior to both passive and unconditional active strategies.