Robert Kosowski

Can Mutual Fund 'Stars' Really Pick Stocks? New Evidence from a Bootstrap Analysis

We apply an innovative bootstrap statistical technique to examine the performance of the U.S. equity mutual fund industry during the 1962 to 1994 period. Using this new method, we bootstrap the distribution of the performance measure (the “alpha”) across mutual funds to determine whether funds with the best alphas are simply lucky, or whether managers of these funds possess genuine stockpicking skills—this bootstrap technique is necessary because of the complicated form of the distribution of alphas across funds and the non-normal nature of individual funds’ alphas. Our results indicate that, controlling for luck, fund managers that pick stocks well enough to more than cover their costs do exist. That is, the distribution of alphas computed from bootstrapped fund returns (and assuming that no stockpicking talent exists) has a much smaller right tail than the distribution of alphas computed from actual fund returns. Unfortunately for investors, our bootstrap results also show strong evidence of funds with significant inferior performance. Further, our evidence suggests that stockpicking skills are most clearly evident among growth-fund managers. In general, our study supports the value of active mutual fund management, although it also highlights the drawbacks of funds actively managed by those who cannot pick stocks.

Momentum Strategies in Futures Markets and Trend-following Funds

In this paper we rigorously establish a relationship between time-series momentum strategies in futures markets and commodity trading advisors (CTAs), a subgroup of the hedge fund universe that has grown to USD 300 billion and has attracted a lot of attention during the financial crisis. Building on this relationship, we carry out the first rigorous test of the hypothesis that capacity constraints exist in trend-following investing. Using a cross-section of 71 futures contracts over the period 1974-2012, we first construct one of the most comprehensive sets of time-series momentum portfolios across various trading frequencies. Second, we provide evidence that CTAs follow time-series momentum strategies, by showing that such benchmark strategies have high explanatory power in the time-series of CTA returns. Third, based on this result, we investigate whether there exist capacity constraints in time-series momentum strategies. Consistent with the view that futures markets are relatively liquid, we do not find evidence of statistically significant capacity constraints when using two different methodologies and several robustness tests. Our results have important implications for hedge fund studies and investors. Motivated by the fact that CTA funds differ in their forecast horizons and trading activity (e.g. Hayes 2011) we extend the work of Moskowitz, Ooi and Pedersen (2012) and evaluate time-series momentum strategies in futures markets over a broad grid of lookback periods, investment horizons and frequencies of portfolio rebalancing. We find strong time-series momentum patterns in monthly, weekly

Hedge Fund Performance: What Do We Know?

This paper proposes a novel database merging approach and re-examines the fundamental questions regarding hedge fund performance. Before drawing conclusions about fund performance, we form an aggregate database by exploiting all available information across and within seven commercial databases so that widest possible data coverage is obtained and the effect of data biases is mitigated. Average performance is significantly lower but more persistent when these conclusions are inferred from aggregate database than from some of the individual commercial databases. Although hedge funds deliver performance persistence, an average fund or industry as a whole do not deliver significant risk-adjusted net-of-fee returns while the gross-of-fee returns remain significantly positive. Consistent with previous literature, we find a significant association between fund-characteristics related to share restrictions as well as compensation structure and risk-adjusted returns.

Demystifying Time-Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations

Motivated by studies of the impact of frictions on asset prices, we examine the effect of key components of time-series momentum strategies on their turnover and performance from 1984 until 2013. We show that more efficient volatility estimation and price trend detection can significantly reduce portfolio turnover by more than one third, without causing a statistically significant performance penalty. We shed light on the post-2008 underperformance of the strategy by linking it to the increased level of asset co-movement. We propose a novel implementation of the strategy that incorporates the pairwise signed correlations by means of a dynamic leverage mechanism. The correlation-adjusted variant outperforms the naive implementation of the strategy and the outperformance is more pronounced in the post-2008 period. Finally, using a transaction costs model for futures-based strategies that separates costs into roll-over and rebalancing costs, we show that our findings remain robust to the inclusion of transaction costs.

The Effect of Investment Constraints on Hedge Fund Investor Returns

This paper examines the effect of investor-level real-world investment constraints, including several which had not been studied before, on hedge fund performance and its persistence. Using a large consolidated database, we demonstrate that hedge fund performance persistence is significantly reduced when rebalancing rules reflect fund size restrictions and liquidity constraints, but remains statistically significant at higher rebalancing frequencies. Hypothetical investor portfolios that incorporate additional minimum diversification constraints, minimum investment requirements, and focus on open funds suggest that the performance and its persistence documented in earlier studies of hedge funds is not easily exploitable, especially by large investors.

Improving Time-Series Momentum Strategies: The Role of Trading Signals and Volatility Estimators

Motivated by studies of the impact of frictions on asset prices, we examine the effect of key components of time-series momentum strategies on their turnover and performance from 1984 until 2013. We show that more efficient volatility estimation and price trend detection can significantly reduce portfolio turnover by more than one third, without causing a statistically significant performance penalty. We shed light on the post-2008 underperformance of the strategy by linking it to the increased level of asset co-movement. We propose a novel implementation of the strategy that incorporates the pairwise signed correlations by means of a dynamic leverage mechanism. The correlation-adjusted variant outperforms the naive implementation of the strategy and the outperformance is more pronounced in the post-2008 period. Finally, using a transaction costs model for futures-based strategies that separates costs into roll-over and rebalancing costs, we show that our findings remain robust to the inclusion of transaction costs.

Improving Time-Series Momentum Strategies: The Role of Volatility Estimators and Trading Signals

Motivated by studies of the impact of frictions on asset prices, we examine the effect of key components of time-series momentum strategies on their turnover and performance from 1984 until 2013. We show that more efficient volatility estimation and price trend detection can significantly reduce portfolio turnover by more than one third, without causing a statistically significant performance penalty. We shed light on the post-2008 underperformance of the strategy by linking it to the increased level of asset co-movement. We propose a novel implementation of the strategy that incorporates the pairwise signed correlations by means of a dynamic leverage mechanism. The correlation-adjusted variant outperforms the naive implementation of the strategy and the outperformance is more pronounced in the post-2008 period. Finally, using a transaction costs model for futures-based strategies that separates costs into roll-over and rebalancing costs, we show that our findings remain robust to the inclusion of transaction costs.

Effect of Regulatory Constraints on Fund Performance: New Evidence from UCITS Hedge Funds

We economically motivate and then test a range of hypotheses regarding performance and risk differences between UCITS-compliant and other hedge funds. The latter exhibit more suspicious return patterns than do absolute return UCITS (ARUs), but ARUs exhibit higher levels of operational risk. We find evidence of a strong liquidity premium: hedge funds offer investors less liquidity than do ARUs yet exhibit better risk-adjusted performance. Our findings are substantially unchanged under various robustness tests and adjustments for possible selection bias. The liquidity premium for ARUs and their lack of performance persistence have implications for both investors and policy makers.