Hedge Fund Return Predictability Under the Magnifying Glass

Abstract

This paper develops a unified approach to comprehensively analyze individual hedge fund return predictability, both in and out of sample. In sample, we find that variation in hedge fund performance across changing market conditions is widespread and economically significant. The predictability pattern is consistent with economic rationale, and largely reflects differences in key hedge fund characteristics, such as leverage or capacity constraints. Out of sample, we show that a simple strategy that combines the funds’ return forecasts obtained from individual predictors delivers superior performance. We exploit this simplicity to highlight the drivers of this performance, and find that in- and out-of-sample predictability are closely related.

The relation between fees and return predictability in the mutual fund industry

“…Although our predictive regression is useful to determine potential predictable returns in fund performance, there are several sources of predictability: the expected returns of the mutual fund benchmark (risk premia) can fluctuate with changing economic conditions, and fund managers may have skills in stock selection that depends on the state of the economy, making fund alphas predictable. To take into account this intuition, we follow the work of Christopherson et al (1998), andAvramov et al (2012), and model the dynamics of mutual fund returns using:…”

The Cross-Sectional Distribution of Fund Skill Measures

“…Equation (1) is appealing because of its simplicity in capturing the two skill dimen-sions   and    As such, it leaves aside additional predictors that potentially affect the fund gross alpha, such as business cycle indicators, aggregate industry size, and other fund specific variables (age, family size). 4 We can extend our baseline framework to accommodate richer alpha dynamics. To this end, we simply rewrite Equation 1as…”

“…Here, we apply a simple, nonparametric approach to multiple skill measures. Several studies apply the False Discovery Rate approach to measure the proportions of funds with non-zero performance (e.g., Avramov, Barras, and Kosowski (2013), Barras, Scaillet, and Wemers (2010), Ferson and Chen (2015)). This paper focuses on skill and estimates the entire distribution (not just the proportions), as well as its moments and quantiles.…”

Recent advances in explaining hedge fund returns: Implicit factors and exposures

“…Analogous to that study but emphasizing forecasting more was the study by Avramov, Barras, and Kosowski (2013). They developed a unified methodological framework to asses both in-sample and out-of-sample hedge fund returns predictability based on macroeconomic variables, using the Barclayhedge, TASS, HFR, CISDM, and MSCI databases from 1994 to 2008.…”

“…For other fund styles the proportions of non-neutral funds are from 50% for fund of funds to 85% for equity non-hedge style. Market neutral style funds are more neutral to market returns than other categories such as equity hedge, non-equity hedge, or event driven funds Avramov, Barras, and Kosowski (2013) Barclayhedge, TASS, HFR, CISDM, MSCI, 1994MSCI, -2008 Up/Bottom, Regression based, portfolio construction Approximately 63% of the sample funds have expected returns that change according to business conditions. Out-ofsample, a simple strategy that combines the fund's return forecasts obtained from individual investors produces superior performance.…”