Indirect Incentives of Hedge Fund Managers

Indirect incentives exist in the money management industry when good current performance increases future inflows of capital, leading to higher future fees. For the average hedge fund, indirect incentives are at least 1.4 times as large as direct incentives from incentive fees and managers’ personal stakes in the fund. Combining direct and indirect incentives, manager wealth increases by at least $0.39 for a $1 increase in investor wealth. Younger and more scalable hedge funds have stronger flow‐performance relations, leading to stronger indirect incentives. These results have a number of implications for our understanding of incentives in the asset management industry.     

Role of Managerial Incentives and Discretion in Hedge Fund Performance

Using a comprehensive hedge fund database, we examine the role of managerial incentives and discretion in hedge fund performance. Hedge funds with greater managerial incentives, proxied by the delta of the option-like incentive fee contracts, higher levels of managerial ownership, and the inclusion of high-water mark provisions in the incentive contracts, are associated with superior performance. The incentive fee percentage rate by itself does not explain performance. We also find that funds with a higher degree of managerial discretion, proxied by longer lockup, notice, and redemption periods, deliver superior performance. These results are robust to using alternative performance measures and controlling for different data-related biases.     

Hedge Fund Risk Dynamics: Implications for Performance Appraisal

Accurate appraisal of hedge fund performance must recognize the freedom with which managers shift asset classes, strategies, and leverage in response to changing market conditions and arbitrage opportunities. The standard measure of performance is the abnormal return defined by a hedge fund's exposure to risk factors. If exposures are assumed constant when, in fact, they vary through time, estimated abnormal returns may be incorrect. We employ an optimal changepoint regression that allows risk exposures to shift, and illustrate the impact on performance appraisal using a sample of live and dead funds during the period January 1994 through December 2005.     

Bayesian Alphas and Mutual Fund Persistence

We use daily returns to compare the performance predictability of Bayesian estimates of mutual fund performance with standard frequentist measures. When the returns on passive nonbenchmark assets are correlated with fund holdings, incorporating histories of these returns produces a performance measure that predicts future performance better than standard measures do. Bayesian alphas based on the Capital Asset Pricing Model (CAPM) are particularly useful for predicting future standard CAPM alphas. Over our sample period, priors consistent with moderate to diffuse beliefs in managerial skill dominate more skeptical prior beliefs, a result that is consistent with investor cash flows.     

Mandatory Disclosure and Operational Risk: Evidence from Hedge Fund Registration

Mandatory disclosure is a regulatory tool intended to allow market participants to assess operational risk. We examine the value of disclosure through the controversial SEC requirement, since overturned, which required major hedge funds to register as investment advisors and file Form ADV disclosures. Leverage and ownership structures suggest that lenders and equity investors were already aware of operational risk. However, operational risk does not mediate flow‐performance relationships. Investors either lack this information or regard it as immaterial. These findings suggest that regulators should account for the endogenous production of information and the marginal benefit of disclosure to different investment clienteles.     

The Performance of Hedge Funds: Risk, Return, and Incentives

Hedge funds display several interesting characteristics that may influence performance, including: flexible investment strategies, strong managerial incentives, substantial managerial investment, sophisticated investors, and limited government oversight. Using a large sample of hedge fund data from 1988–1995, we find that hedge funds consistently outperform mutual funds, but not standard market indices. Hedge funds, however, are more volatile than both mutual funds and market indices. Incentive fees explain some of the higher performance, but not the increased total risk. The impact of six data-conditioning biases is explored. We find evidence that positive and negative survival-related biases offset each other.     

A Time to Scatter Stones,and a Time to Gather Them: The Annual Cycle in Hedge Fund Risk Taking

Analyzing a sample of hedge fund daily returns from Bloomberg, we find a seasonal pattern in their risk taking. During earlier months of a year, poorly performing funds reduce risk. The reduction is stronger for funds with higher management fees, shorter redemption periods, and recently deteriorating performance, consistent with a managerial aversion to early fund liquidation. Toward the end of a year, poorly performing funds gamble for resurrection by increasing risk. It is largely achieved by increasing exposure to market factors, and can be linked to stronger indirect managerial incentives during the second half of a year.     

Careers and Survival: Competition and Risk in the Hedge Fund and CTA Industry

Investors in hedge funds and commodity trading advisors (CTAs) are concerned with risk as well as return. We investigate the volatility of hedge funds and CTAs in light of managerial career concerns. We find an association between past performance and risk levels consistent with previous findings for mutual fund managers. Variance shifts depend upon relative rather than absolute fund performance. The importance of relative rankings points to the importance of reputation costs in the investment industry. Our analysis of factors contributing to fund disappearance shows that survival depends on absolute and relative performance, excess volatility, and on fund age.     

Improved Forecasting of Mutual Fund Alphas and Betas

This paper proposes a simple back testing procedure that isshown to dramatically improve a panel data model's ability toproduce out of sample forecasts. Here the procedure is usedto forecast mutual fund alphas. Using monthly data with an OLSmodel it has been difficult to consistently predict which portfoliomanagers will produce above market returns for their investors.This paper provides empirical evidence that sorting on the estimatedalphas populates the top and bottom deciles not with the bestand worst funds, but with those having the greatest estimationerror. This problem can be attenuated by back testing the statisticalmodel fund by fund. The back test used here requires a statisticalmodel to exhibit some past predictive success for a particularfund before it is allowed to make predictions about that fundin the current period. Another estimation problem concerns theuse of a single statistical model for all available mutual funds.Since no one statistical model is likely to fit every fund,the result is a great deal of misspecification error. This papershows that the combined use of an OLS and Kalman filter modelincreases the number of funds with predictable out of samplealphas by about 60%. Overall, a strategy that uses very modestex-ante filters to eliminate funds whose parameters likely deriveprimarily from estimation error produces an out of sample risk-adjustedreturn of over 4% per annum.     

Estimating the Dynamics of Mutual Fund Alphas and Betas

This article develops a Kalman filter model to track dynamicmutual fund factor loadings. It then uses the estimates to analyzewhether managers with market-timing ability can be identifiedex ante. The primary findings are as follows: (i) Ordinary leastsquares (OLS) timing models produce false positives (nonzeroalphas) at too high a rate with either daily or monthly data.In contrast, the Kalman filter model produces them at approximatelythe correct rate with monthly data; (ii) In monthly data, thoughthe OLS models fail to detect any timing among fund managers,the Kalman filter does; (iii) The alpha and beta forecasts fromthe Kalman model are more accurate than those from the OLS timingmodels; (iv) The Kalman filter model tracks most fund alphasand betas better than OLS models that employ macroeconomic variablesin addition to fund returns.     

Failure Risk and the Cross‐Section of Hedge Fund Returns

Modeling a hedge fund's probability of failure with a dynamic logit regression, I find that the probability of a fund's failure has a significantly negative effect on the fund's future returns. A quintile portfolio with the highest failure probability underperforms a quintile portfolio with the lowest failure probability by 5% to 6% per year from 1997 to 2012. The results are robust to the definition of hedge fund failure and controlling for a large set of risk factors and fund characteristics. Moreover, the negative effect of failure probability on future fund returns is stronger for funds with weak share restrictions.     

Hedge Fund Indices: Reconciling Investability and Representativity

Following a growing concern among investors about the quality of hedge fund index return data, this paper addresses the question of whether designing hedge fund indices that fulfil the usual requirements (in particular representative and investable) is or not a feasible task, given a variety of features that are specific to that industry. To test whether or not investability should necessarily come at the cost of representativity, we use a well‐known methodology in the asset pricing literature based on the concept of factor replicating portfolios. Our results suggest that it is actually possible to construct representative indices based on a limited number of funds that are open to new investments, except perhaps in the case of equity market neutral strategies, provided that: i) these funds are suitably selected and ii) a portfolio is constructed with the objective of replicating the common trend in hedge fund returns for a given strategy. A range of robustness tests are performed that show that high correlation of the factor replicating portfolios with the common factor of returns for each strategy is remarkably stable with respect to modifying the number of funds in the replicating portfolio or changing the frequency of rebalancing.     

Hedge Fund Contagion and Liquidity Shocks

Defining contagion as correlation over and above that expected from economic fundamentals, we find strong evidence of worst return contagion across hedge fund styles for 1990 to 2008. Large adverse shocks to asset and hedge fund liquidity strongly increase the probability of contagion. Specifically, large adverse shocks to credit spreads, the TED spread, prime broker and bank stock prices, stock market liquidity, and hedge fund flows are associated with a significant increase in the probability of hedge fund contagion. While shocks to liquidity are important determinants of performance, these shocks are not captured by commonly used models of hedge fund returns.     

Hedge Fund Regulation and Misreported Returns

This paper introduces a cross‐country law and finance analysis of the misreporting behaviour in the hedge fund industry in terms of smoothing returns so that a fund consistently generates positive returns. We find strong evidence that international differences in hedge fund regulation are significantly associated with the propensity of fund managers to misreport monthly returns. We find a positive association between wrappers and misreporting, particularly for funds that do not have a lockup provision. Also, we find some evidence that misreporting is less common among funds in jurisdictions with minimum capitalisation requirements and restrictions on the location of key service providers. We assess the robustness of our finds to a number of specifications, including, different specifications of misreporting bin widths, subsets of the data by fund type, as well as specifications controlling for collinearity and selection effects and other robustness checks. We show misreporting significantly affects capital allocation, and calculate the wealth transfer effects of misreporting and relate this wealth transfer to differences in hedge fund regulation.     

On the High‐Frequency Dynamics of Hedge Fund Risk Exposures

We propose a new method to model hedge fund risk exposures using relatively high‐frequency conditioning variables. In a large sample of funds, we find substantial evidence that hedge fund risk exposures vary across and within months, and that capturing within‐month variation is more important for hedge funds than for mutual funds. We consider different within‐month functional forms, and uncover patterns such as day‐of‐the‐month variation in risk exposures. We also find that changes in portfolio allocations, rather than in the risk exposures of the underlying assets, are the main drivers of hedge funds' risk exposure variation.     

Hedge Fund Regulation and Fund Governance: Evidence on the Effects of Mandatory Disclosure Rules

This paper uses three alternating changes in hedge fund regulation to study whether regulation reduces hedge funds’ misreporting, and, if so, why regulation is effective. Relative to public companies, hedge fund regulation is relatively light. Much of the regime is a “comply‐or‐explain” framework that allows funds to forego compliance with governance rules, providing that they disclose their lack of compliance. The results show that regulation reduces misreporting at hedge funds. Further analysis suggests that the disclosure requirements led funds to make changes in their internal governance, such as hiring or switching the fund's auditor, and that these changes induced funds to report their financial performance more accurately.     

False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas

This paper develops a simple technique that controls for "false discoveries," or mutual funds that exhibit significant alphas by luck alone. Our approach precisely separates funds into (1) unskilled, (2) zero-alpha, and (3) skilled funds, even with dependencies in cross-fund estimated alphas. We find that 75% of funds exhibit zero alpha (net of expenses), consistent with the Berk and Green equilibrium. Further, we find a significant proportion of skilled (positive alpha) funds prior to 1996, but almost none by 2006. We also show that controlling for false discoveries substantially improves the ability to find the few funds with persistent performance.