Hedge Fund Alpha Research Articles

Hedge fund alpha and beta corrected for stale pricing

Hedge fund alpha and beta corrected for stale pricing

Hedge Fund Alpha: What about Drawdowns?

The author reviews the relative risks of hedge fund investing using various commonly used measures, including market betas, correlations, and portfolio drawdowns. Historically, the data show that hedge funds have hedged a fair degree of systematic market risk, especially in the early years, offering meaningful diversification benefits to traditional stock/bond portfolios. However, the diversification benefits for investors in hedge funds have since declined, though they have not altogether been eliminated. Most recently, during the 2020 pandemic, modest drawdown benefits bore out for hedge fund investors, although again much less so than in the earlier years. <b>Key Findings</b> ▪ This article extends “Hedge Fund Alpha: Cycle or Sunset” (Sullivan 2021) with a focus on the relative risk of hedge funds versus a stock-only as well as more diversified portfolios that seek to blend hedge funds, stocks, and bonds. ▪ The hedging away of systematic risk by hedge funds appears to be diminishing in recent years, in part due to rising correlations between stocks and hedge funds, now approaching 90%. ▪ Drawdowns for various hedge fund composite portfolios during the turbulent periods of 2008 and 2020 have been more in line with those seen by a traditional 50/50 stock/bond portfolio. ▪ However, within the universe of hedge funds, some strategies have delivered more meaningful diversification benefits.

Systematic 13F Hedge Fund Alpha

Institutions holding greater than $100 million in securities are required to disclose their holdings in US listed stocks to the Securities and Exchange Commission (SEC) no later than 45 days after the quarter-end, in a form known as 13F. We show that the best ideas of hedge funds produce economically meaningful and statistically significant risk-adjusted returns that outperform the S&P500, following tests identified in Cohen, Polk, and Silli (2010). We construct alternative measures that are more suitable for hedge funds, rather than mutual funds: conviction and consensus. We find that to systematically identify hedge fund alpha in the 13F filings, one must select the right group of managers that have longer-term views on stock picks. We construct a trading strategy that combines conviction and consensus of such managers that outperforms the S&P500 by 3.80% on average and delivers a Sharpe ratio of 0.75 over the period Q1 2004 to Q2 2019.

Is Stellar Hedge Fund Performance for Real?

We apply a robust bootstrap to evaluate the performance of a large universe of hedge funds. Our bootstrap estimates indicate that the performance of the top hedge funds cannot be attributed to chance alone. This is true even after adjusting for back fill bias, serial correlation, and structural breaks. Also, we find that hedge fund alpha differences persist over three year horizons. However, an investment strategy designed around this will run into difficulties as the persistence is often confined to small funds that are effectively closed to new inflows. Moreover, Bayesian estimates suggest that standard alphas may be overestimated by 41% for the average top fund.

All Style Hedge Fund Analysis with Constant- and Time-Varying Factor Loading Models

Driven by vast historical growth and the recent crises, the hedge fund industry has undergone several changes. This thesis presents studies on the analysis of hedge fund returns within changing market states by applying different constant, asymmetric and time-varying factor loading models. Considered models include the CAPM, Fama-French 3-factor model, Carhart 4-factor model, Fama-French 5-Factor model, Agarwal-Naik 8-factor model and the Fung-Hsieh 7- and 8-factor models. In addition, and unlike previous research, 94 hedge fund strategy styles have been analysed individually to test whether the model performances differ among approaches. The first full-sample analysis exhibits generally low explanatory power whereby the more sophisticated models perform superiorly. Equity strategies, especially long-only funds, exhibit high adjusted R-Squared among all models, while fixed income, fundamental and technical hedge funds result in low significance. The CUSUM control chart based crisis/non-crisis dummy cannot substantially improve the explanatory power of the models. Hedge fund alpha and factor significance varies considerably among strategies and the power of the models remains similarly poor. Asymmetric up/down models exhibit slightly improved explanatory power while the significance of alpha diminishes. Replacing the conditional up/down variable by the crisis/non-crisis setting resulted in inferior results. Empirical analysis with asymmetric higher-moment models approves the asymmetries in hedge fund returns partially. Moreover, a time-varying approach substantially improves the explanatory power of all models while hedge fund alpha further diminishes. All dynamic models exhibit significant exposures on macro state variables for a high proportion of funds. To summarise, it has been shown how simple models can be fitted to increase the explanatory power. As a result, the adjusted R-Squared were improved by 73%. On a strategy level, equity funds are explained the best while fixed income, fundamental and technical hedge funds are the most difficult to analyse.

Is It Possible to OD on Alpha?

It is wellknown that combining multiple hedge fund alpha streams yields diversification benefits to the resultant portfolio. Additionally, crossing trades between different alpha streams reduces transaction costs: As the number of alpha streams increases, the relative turnover of the portfolio decreases with the crossing of more trades. However, Kakushadze and Liewargue that under reasonable assumptions, as the number of alphas increases, turnover does not decrease indefinitely; instead, the turnover approaches a nonvanishing limit related to the correlation structure of the portfolio’s alphas. The authors also point out that, more generally, computational simplifications can be employed when the number of alphas is large.

Incentives and Endogenous Risk Taking: A Structural View on Hedge Fund Alphas

ABSTRACTHedge fund managers are subject to several nonlinear incentives: performance fee options (call); equity investors' redemption options (put); and prime broker contracts allowing for forced deleverage (put). The interaction of these option‐like incentives affects optimal leverage ex ante, depending on the distance of fund‐value from the high‐water mark. We study how these endogenous effects influence performance measures used in the literature. We show that reduced‐form measures that do not account for these features are subject to economically significant false discovery biases. The result is stronger for low‐quality funds. We propose an alternative structural methodology for conducting performance attribution in hedge funds.

Flows, Price Pressure, and Hedge Fund Returns

The authors studied how capital flows affect hedge fund returns and found that funds with high inflows outperform funds with high outflows during the month of the flows. This immediate reaction, combined with feedback trading, gives rise to a cycle: Flows exert price pressure, this effect on returns induces more flows, and these flows cause further price pressure. The cycle is so strong that it takes two years for a full return reversal, and it contributes to the observed persistence in hedge fund performance. The impact of flows on returns has clear implications for performance evaluation: One-third of estimated hedge fund alphas are due to flows.

Is it Possible to OD on Alpha?

It is well known that combining multiple hedge fund alpha streams yields diversification benefits to the resultant portfolio. Additionally, crossing trades between different alpha streams reduces transaction costs. As the number of alpha streams increases, the relative turnover of the portfolio decreases as more trades are crossed. However, we argue, under reasonable assumptions, that as the number of alphas increases, the turnover does not decrease indefinitely; instead, the turnover approaches a non-vanishing limit related to the correlation structure of the portfolio’s alphas. We also point out that, more generally, computational simplifications can arise when the number of alphas is large.

Long/Short Equity Hedge Funds and Systematic Ambiguity

This study presents a hedge fund portfolio choice model for an investor facing ambiguity. In the empirical section, we measure ambiguity as the cross-sectional dispersion in Industrial Production growth and in stock market return forecasts, and we construct the systematic ambiguity factors from the universe of S&P 500 stocks. We estimate ambiguity betas for long/short equity hedge funds strategies and document signi cant ambiguity exposures for directional L/S equity hedge funds. We compare the out-of-sample performance of portfolios constructed according to the L/S hedge fund alphas' ranking with and without systematic ambiguity exposures and nd that the former outperform.

Leverage and Alpha: The Case of Funds of Hedge Funds

In this paper, we develop a theoretical model of fund of hedge fund net leverage and alpha where the cost of borrowing is increasing with net leverage, thereby impacting the performance. We use this model to determine the conditions under which the leverage has a negative or a positive impact on investors' alpha. Moreover, we show that the manager of a fund of hedge fund has an incentive to take a leverage that hurts the investors' alpha. Next, we develop a statistical method combining the Sharpe style analysis and a time-varying coefficient model. This method allows the weights of the regression to vary over time while being constrained to sum up to one. Subsequently, we use this method to get estimates of the leverages of a sample of funds of hedge funds. The estimates of leverages are then used in predictive regression analyses to confirm the negative impact of leverage on fund of hedge fund alphas and appraisal ratios. Finally, our results being robust to various robustness checks, we argue that this effect may be an explanation for the disappointing alpha delivered by funds of hedge funds.

The procyclicality of hedge fund alpha and beta

Traditional financial institutions as banks follow procyclical risk strategies, that is, they increase their leverage in economic expansions and reduce it in contractions, which leads to a procyclical behaviour for their betas and other risk and financial performance measures. We study the cyclical aspects of hedge fund strategies, an issue quite overlooked in the literature. We rely on two procedures: conditional modelling and Kalman filtering of hedge funds’ alpha and beta. We find that hedge fund betas are usually procyclical. Our results also show that the alpha is often high at the beginning of a market upside cycle but, as the demand pressure increases, it progressively shrinks, which suggests that the alpha puzzle ought to be analysed in a dynamic setting.

Hedge Fund Benchmarking: Equity CorrelationRegimes and Alpha

The authors analyze the effect of realized cross-sectional correlations of the S&P 500 on hedge fund alpha by proposing a unified model that extends the Fama–French five-factor model by adding a momentum factor and a dichotomous cross-sectional equity-market correlation factor. The use of dichotomous variables, to isolate cross-sectional equity-market correlation regimes, reveals factor exposures that would otherwise not be detectable using a continuous-time period analysis. The model achieves a robust forecasting efficacy rate. They find strong empirical evidence of idiosyncratic manager-based investment decisions (e.g., security selection, sector rotation, leveraging skills) during low correlation regimes, as evidenced by materially higher alpha, with the converse effect during high correlation regimes. The proposed model can be used as a practical benchmarking tool for hedge fund allocators, particularly when applied to equity-oriented hedge fund strategies.

Hedge Fund Alpha under Different Equity Correlation Regimes: An Extension of the Fama-French Five-Factor Model

This paper investigates the effect of S&P 500 cross-sectional correlation on the alpha characteristics of hedge fund strategies. It decomposes the returns of the four major hedge fund trading strategies in the Hedge Fund Research, Inc. (“HFR”) database using Fama and French’s [1993] five-factor model plus an additional equity correlation factor that allows for the isolation of three unique equity-market correlation regimes. The empirical analysis centers on the use of newly introduced dichotomous variables to isolate correlation regimes using panel data regressions. Over the period of 1990-2010, we find that equity-oriented hedge fund strategies generate approximately twice the alpha during low correlation regimes relative to normal correlation regimes. We present this finding as evidence to support our hypothesis that “stock-picking” skills are enhanced during low equity-market correlation environments.

Evidence in Support of Shorter-TermMarket Timing

In this article, the authors provide evidence that market timing is possible over the shorter time periods that institutional asset allocation approaches typically consider. The evidence is based on the performance of components typically used in quantitative market timing approaches as well as the finding that hedge funds have successfully practiced market timing over the past 20 years and that market timing is the source of at least half of hedge fund alpha.

The Effect of S&amp;P 500 Correlation on Hedge Fund Alpha

This article investigates the effect of S&amp;P 500 Index correlation on the performance of hedge funds. Specifically, the authors separate the alpha and beta components of the four major hedge fund trading strategies in the Hedge Fund Research (HFR) database across three mutually exclusive equity–market correlation regimes. Using panel data regression, they isolate correlation regimes using dichotomous variables that add or detract from the alphas. They find that equity–oriented hedge fund strategies generate over twice the alpha during low–correlation regimes relative to normalcorrelation regimes. Their analysis provides a practical approach for optimizing allocations to hedge fund strategies that are expected to benefit from “stock–picking”skills during low equity-correlation environments. Furthermore, the proposed framework can be useful in identifying individual managers likely to benefit from this effect. <b>TOPICS:</b>Real assets/alternative investments/private equity, statistical methods, performance measurement, manager selection

Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

This paper analyzes the contribution of hedge funds to optimal asset allocations between 1993 and 2010. The preferences of specific institutional investors are captured by implementing a Bayesian asset allocation framework that incorporates heterogeneous expectations regarding hedge fund alpha. Mean-variance spanning tests are used to infer the ability of hedge funds to significantly enhance the mean-variance efficient frontier. Further, a novel democratic variance decomposition procedure sheds light on the dynamics in the co-movement of hedge fund returns with a set of common benchmark assets. The empirical findings indicate that portfolio benefits of hedge funds are time-varying and strongly depend on investor optimism regarding hedge funds’ ability to generate alpha. In general, allocations to hedge funds improve the global minimum variance portfolio even after controlling for short-selling restrictions and minimum diversification constraints. However, due to dynamics underlying the composition of the aggregate hedge fund universe, the factor structure of hedge fund returns has become more similar to the benchmark assets over time.

Long/Short Equity Hedge Funds and Systematic Ambiguity

This study presents a hedge fund portfolio choice model for an investor facing ambiguity. In the empirical section, we measure ambiguity as the cross-sectional dispersion in Industrial Production growth and in stock market return forecasts, and we construct the systematic ambiguity factors from the universe of S&P 500 stocks. We estimate ambiguity betas for long/short equity hedge funds strategies and document signi cant ambiguity exposures for directional L/S equity hedge funds. We compare the out-of-sample performance of portfolios constructed according to the L/S hedge fund alphas' ranking with and without systematic ambiguity exposures and nd that the former outperform.

Hedge Funds and Optimal Asset Allocation: Bayesian Expectations, Time-Varying Investment Opportunities and Mean-Variance Spanning

In this paper we analyze the contribution of hedge funds in optimal asset allocations for different investor clienteles. The preferences of specific institutional investors are captured by implementing a Bayesian asset allocation framework that incorporates heterogeneous expectations regarding alpha. Mean-variance spanning tests are used to draw inferences on the ability of hedge funds to enhance the efficient frontier. A novel variance decomposition procedure is employed for analyzing the co-movement of hedge fund returns with the benchmark assets. The empirical findings strongly indicate that portfolio benefits of hedge funds are time-varying and crucially depend on investor optimism regarding hedge fund alpha. Allocations to hedge funds improve the global minimum variance portfolio even after controlling for short-selling restrictions and minimum diversification constraints. However, the factor structure of hedge fund returns has become more similar to the benchmark assets due to dynamics underlying the composition of the aggregate hedge fund universe.

Flows, Price Pressure, and Hedge Fund Returns

We study how capital flows affect hedge fund returns. The contemporaneous relation is positive: funds with high flows outperform funds with low flows during the month of the flows. This immediate reaction, combined with feedback trading, gives rise to a cycle: flows exert price pressure, this effect on returns induces more flows, and these flows cause further price pressure. The cycle is so strong that it takes almost two years before a full return reversal is witnessed. This flow-return cycle also contributes to the observed persistence in hedge fund performance. The impact of flows on returns also has implications for performance evaluation: roughly one third of the estimated hedge fund alphas are due to flows.