Hedge Fund Performance Research Articles

Hedge Fund Database 'Deconstruction': Are Hedge Fund Databases Half Full or Half Empty?

While the impact of backfill bias, survivor bias other database construction issues (e.g., onshore versus offshore) on hedge fund performance have received considerable research attention, the impact on hedge fund performance of differences in the underlying quality or number of reporting managers in various hedge fund databases has often been under reported. Most major hedge fund databases are based on ‘manager’ based reporting. As a result database quality is dependent on managers updating requested data. In addition, hedge fund databases were created and grew at different times. Thus large differences in the number of managers reporting as well as differences in other fund platform characteristics may exist between various databases. If these databases contained enough manager breadth and depth, results at the portfolio level could be similar across various databases, however, differences may still exist at the average manager level especially for small subsets of analysis (e.g., strategy, fee level, etc.). In this analysis we compare performance characteristics of two major databases often used in hedge fund analysis (CSFB/Tremont and CISDM). More specifically, we compare performance results at the strategy level for 1) all reporting funds, 2) funds denominated only in U.S. dollars and 3) a cleaned set of funds with duplicates removed (e.g. multiple share classes or currencies). Results show some return and risk differences between the two databases at the portfolio and average manager level. These differences, however, are often relatively small. In the end, the impact of database usage and the necessity to ‘deconstruct’ one’s database depends, in part, on whether one views the glass as half full or half empty.

Carry Trades and the Performance of Currency Hedge Funds

We investigate the performance and risk of currency hedge funds using a large and unique consolidated currency hedge fund dataset. We find that a substantial number of hedge funds generate returns that exceed foreign exchange risk premia obtained through carry trades. The best alpha-generating funds exhibit a performance that persists over a one-year horizon. This performance persistence is mostly due to compensation for currency risk-taking as there is no strong evidence of remuneration for active management. The results are robust to biases affecting hedge fund returns, alternative carry trade benchmarks and different methodologies used to correct for sample variability.

The Impact of Portfolio Disclosure on Hedge Fund Performance

Consistent with the argument that portfolio disclosure reveals trade secrets, a difference-in-differences estimation suggests that there is a drop in fund performance after a hedge fund begins filing Form 13F, as well as an increase in return correlations with other funds in the same investment style. The drop in performance is concentrated among funds with larger expected proprietary costs of disclosure — for instance, funds that disclose a greater fraction of their assets or hold more illiquid stocks. The drop in performance cannot be fully explained by alternative explanations such as decreasing returns to scale or mean reversion in fund returns.

The Impact of Portfolio Disclosure on Hedge Fund Performance

The Impact of Portfolio Disclosure on Hedge Fund Performance

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 and minimum investment requirements suggest that the performance and its persistence documented in earlier studies of hedge funds is not easily exploitable, especially by large investors.

The analysis of relative performance of London hedge funds during the financial crisis

The hedge funds industry received part of the blame, for the global financial crisis of 2007. An analysis of hedge funds during the crisis shows that the hedge fund industry had disastrous results. In spite of the difficulties, however, some funds did perform quite well. To address the issue of hedge fund performance during the financial crisis period, a study was undertaken to quantitatively determine the performance outcomes of the hedge funds in the London market. Cluster analysis was used to identify different fund managers’ various strategies and to understand their patterns of risk-return during periods of financial panic – periods that increase market volatility. Our analysis was able to identify the hedge fund manager’s allocation strategies that were the best performers during the financial crisis period in the London hedge fund market. The analysis shows that the overall best performers appear to be long short equities, diversified debt, and bottom-up.

Market Conditions and Fund Flows: Evidence from Hedge Funds

This paper investigates whether market conditions affect fund investor behaviour in the hedge fund industry, especially the volatility in the up and down markets. Using a sample of 5,254 individual hedge funds from January 1994 to December 2009, we find that hedge fund investors tend to invest less during up and down-volatile markets. They also adopt different investment strategies in these two market conditions. When market is calm and relatively predictable, there is almost no difference in their behaviors between up and down markets. We also find that smart money effect exists over both 3- and 12-month periods under all market conditions except volatile markets. A further investigation suggests that the observed smart money effect is largely driven by hedge fund performance persistence, which is present and significant is quiet markets only. The findings are relevant to portfolio theories concerning investor recognition of upside and downside volatilities.

Evaluating Hedge Funds with Pooled Benchmarks

The evaluation of hedge fund performance is challenging given the flexible nature of hedge funds' strategies and their lack of operational transparency. As a result inference about skill is inevitably contaminated by the error in the benchmark model. To address this concern, we propose a model pooling approach to develop a fund-specific benchmark obtained by pooling a set of diverse attribution models. We illustrate the advantages of a pooled benchmark over alternative approaches, including the Fung and Hsieh (2004) model and stepwise regression methods, in the contexts of a real-time investment strategy, hedge fund replication, and fund failure prediction.

Robust evidence on the similarity of Sharpe ratio and drawdown-based hedge fund performance rankings

In this article, we analyse whether the class of adequately defined drawdown-based performance measures produces hedge fund rankings similar to the one that can be obtained using the Sharpe ratio. Supported by a series of robustness checks, we find that the choice of performance measure does not matter if investors are simply interested in identifying the best hedge funds and if a sufficient return history is used to calculate performance measure estimates. In small time series sample sizes typically used to evaluate hedge funds, the rankings cannot be regarded as strictly identical. However, with an increasing time series dimension, the ranking differences fall considerably.

Framework for Hedge Fund Returnand Risk Attribution

This article provides a framework for hedge fund return and risk attribution through the construction of a relevant benchmark. It is shown that volatility is a source of systematic risk, volatility measures based on equity market returns are more robust, fees have averaged between one-half and one-third of total gross returns, and high explanatory power can be achieved without the use of exotic systematic risk factors. Finally, the article suggests that alpha and systematic risk loadings are best estimated when regressed on gross returns and that systematic risk exposures are multi-dimensional and effectively modeled using a four-factor model. Hedge fund performance is determined by exposure, skill, and cost. The framework presented here provides robust attribution to exposure, skill, and cost. <b>TOPICS:</b>Derivatives, volatility measures, factor-based models

Hedge fund liquidity and performance: Evidence from the financial crisis

We investigate how share restrictions affect hedge fund performance in crisis and non-crisis periods. Consistent with prior research, we find that in the pre-crisis period more illiquid funds generate a share illiquidity premium compensating investors for limited liquidity. In the crisis period, this share illiquidity premium turns into an illiquidity discount. Hedge funds with more stringent share restrictions invest more heavily in illiquid assets. While share restrictions enable funds to manage illiquid assets effectively in the pre-crisis period, they seem insufficient to ensure effective management of illiquid portfolios in the crisis. In a crisis period, funds holding illiquid portfolios experience lower returns and alphas, also when share restrictions are controlled for. Funds with an asset–liability mismatch perform particularly poorly and experience the strongest outflows. Share restrictions are also a proxy for incentives as investors cannot immediately withdraw their money after poor performance. We show that higher incentive fees can offset the share illiquidity discount in the crisis period.

Capacity constraints, investor information, and hedge fund returns

To identify capacity constraints in hedge funds and simultaneously gauge how well-informed hedge fund investors are, we need measures of investor demand that do not affect deployed hedge fund assets. Using new data on investor interest from a secondary market for hedge funds, this paper verifies the existence of capacity constraints in hedge funds. There is more mixed evidence on the information available to hedge fund investors. Buy and sell indications arrive following fund outperformance. While buy indications have little incremental power to predict hedge fund performance over and above well-known forecasting variables, sell indications do somewhat better.

Dynamic Exposure of Hedge Funds to the Changes in the Risk Factors

This paper models exposure of hedge fund to risk factors and examines time-varying performance of hedge funds. From existing models such as ABS-factor model, SAC-factor model, and four-factor model, we extract the best six factors for each hedge fund portfolio by investment strategy. Then, we find combinations of risk factors that most explain variance in performance of each hedge fund portfolio by investment strategy. The results show instability of coefficients in the performance attribution regression. Incorporating time-varying factor exposure feature would be the best way to appropriately measure hedge fund performance. Furthermore, the optimal models with fewer factors exhibit greater explanatory power than existing models. Time-varying model customized by investment strategy of hedge funds would clearly show how sensitive to risk factors managements of hedge funds are according to market conditions.

For Whom Hurdle Rate and High-Watermark Exist?

In this study, we first conduct multinomial logistic regression analysis to see how hedge fund attributes affect hedge fund managers’ decision of whether to offer a hurdle rate and/or high-watermark. Hedge funds taking more risky position and collecting high performance fee are more likely to offer hurdle rate and/or high-watermark. Second, we conduct cross-sectional regression analysis to see how hedge fund attributes affect hedge fund performance. Our results indicate that hurdle rate and high-watermark are restrictions for hedge fund managers on collecting fee and that hurdle rate and high-watermark cannot be considered to be incentives. We also find that hedge funds collecting high performance fee and having large amount of funds are more likely to outperform those collecting low performance fee and having small amount of funds. While conducting cross-sectional regression analysis, we use three different measures of hedge fund performance: alpha, palpha and Sharpe ratio. Alpha and palpha are obtained from the optimal model by investment strategy controlling for hedge fund risk associated with risk factors different by its investment strategy. In addition, we control for survivorship and instant history biases. So, our results from alpha and palpha are more credible than those of Soydemir et al. (2012) which employs only Sharpe ratio.

Qualitative Hedge Fund Characteristics and Fund Performance: Changes Over Time

In this article, the authors examine three hedge fund governance characteristics—restrictions on investor liquidity, the presence of a high-water mark, and the level of fees charged to investors—that previously have been shown to be associated with superior hedge fund performance. Investor liquidity and high-water mark provisions, which were found to predict performance in earlier time periods, no longer do so. High fund fees are associated with better fund performance—but only because better managers are able to charge higher fees, not because higher fees make managers perform better. The authors conclude that simple, readily observable characteristics do not reveal superior performance.

Out of the Dark: Hedge Fund Reporting Biases and Commercial Databases

We examine the potential for selection bias in voluntarily reported hedge fund performance data. We construct a set of hedge fund returns that have never been reported to a commercial hedge fund database. These returns allow a direct comparison of performance between funds that choose to report to commercial databases and funds that do not. We find that funds that report their performance to commercial databases significantly outperform nonreporting funds. Our results suggest that the voluntarily reported performance in commercial databases suffers from a selection bias that may exaggerate the average skill of the universe of hedge fund managers. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. , Oxford University Press.

Portfolio Optimization for Hedge Funds Through Time-Varying Coefficients

In this paper, we show the interest of the time-varying coefficient model in hedge fund performance assessment and selection. We argue that the alpha of hedge funds is dynamic and that the time-varying alpha captures this dynamic behavior. Therefore, forming portfolios based on their time-varying alpha should lead to outperforming portfolios. Using a persistence analysis, we check this conjecture and show that contrary to top performers in terms of OLS alpha, the top performers in terms of past time-varying alpha generate superior and significant ex-post performance. Additionally, this analysis shows that persistence exists in the hedge fund industry and can be as long as 3 years. Secondly, building on the conclusion that the time-varying analysis gives a better picture of the alpha of the manager at a certain point in time, we use the time-varying analysis to obtain estimates of the expected returns of hedge funds. Using those estimates to construct a mean-variance optimal portfolio enhances the performance of this portfolio, suggesting that in terms of hedge fund performance detection, the time-varying model is superior to the OLS analysis.

Hedge Fund Liquidity and Performance: Evidence from the Financial Crisis

In this paper, we investigate how share restrictions affect hedge fund performance in crisis and non-crisis periods. Consistent with prior research, we find that more illiquid funds produce both higher returns and alphas in the pre-crisis period. Hence, funds generate a share illiquidity premium for investors as a compensation for limited liquidity. In contrast, in the crisis period, this share illiquidity premium turns into an illiquidity discount. Hedge funds with more stringent share restrictions invest more heavily in illiquid assets. While share restrictions enable funds to manage these illiquid assets effectively in the pre-crisis period, they do not seem to be sufficient to ensure effective management of illiquid portfolios in a crisis. In a crisis period, funds holding illiquid portfolios experience lower returns and alphas, also when share restrictions are controlled for. Funds with an asset-liability mismatch, i.e., funds holding illiquid asset portfolios combined with weak share restrictions, perform particularly poorly and experience the strongest outflows in a crisis. However, share restrictions are not only a proxy for asset portfolio liquidity but also for incentives. Funds with stronger share restrictions and greater managerial discretion have fewer incentives to perform better because investors cannot immediately withdraw their money after poor performance. We show that hedge funds with stricter share restrictions and higher incentive fees do not experience a share illiquidity discount in the crisis period.

Short-Term Hedge Fund Performance

Hedge fund returns are often explained using linear factor models such as Fung and Hsieh (2002). However, since most hedge funds live only for 3 years, these linear regressions are subject to over-parameterization. I improve the out-of-sample accuracy of the linear factor model by combining cross-sectional and time series information for groups of hedge funds with similar investment strategies. The additional cross-sectional information allows more accurate estimates of risk exposures. I also propose a trading strategy based on this methodology for extracting substantially larger risk-adjusted returns.

The Role of Remuneration Structures in Hedge Fund Performance

In this paper, we rationalize the persistent abnormal performance of hedge funds. We show how the commitment to deliver an absolute return, the decreasing returns to scale to which hedge fund strategies are subject, and the performance-linked compensation combine with the incomeaximizing behavior of managers to effectively align the interests of investors and managers. Thanks to the coexistence of these elements, managers have an incentive to control the size of the funds. Therefore, performance-diluting flows do not occur and abnormal performance persists. The model can quantitatively reproduce many empirical facts about hedge funds.