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Purpose – This research pinpoints the limitations of conventional models for evaluating the performance of hedge funds and attempts to provide a new framework for modeling the dynamics of risk structures of hedge funds.

Methodology/approach – This chapter aims to explore how the systematic risk exposures of hedge funds vary over time and depend on exogenous variables that managers are supposed to use in their dynamic investment strategies. To achieve this, we used a Bayesian time-varying CAPM-based beta model within a state space technology.

Findings – The results showed that the volatility, term spread rate, and shocks in liquidity influence significantly on the time variation of hedge funds. Besides, the dynamics of beta indicates that the transmission channels of systematic risk are mainly the leverage levels of hedge funds and liquidity shocks.

Originality/value of chapter – These results are original because they help to explain how expected and unexpected hedge fund returns are correlated with the systematic risk factors via the beta dynamics.

The purpose of this paper is to determine if hedge fund variables (HFVs) are associated with short-run daily buy and hold abnormal returns (BHARs) for a 30-day window around announcement dates for seasoned equity offerings (SEOs).

This paper utilizes the event study metric that computes BHARs. These BHARs are used in a regression model as dependent variables with HFVs and nonhedge fund variables (NFVs) as independent variables. For regression tests, standard errors are clustered at the month level.

This paper offers three new findings. First, HFVs are significantly associated with SEO BHARs. Second, HFVs are capable being associated with stronger statistical significance compared to NFVs. Third, not using HFVs can produce an omitted-variable bias.

This paper does not have information on which individual hedge funds use a strategy during the month of the offering but only the proportion of hedge funds that do. A research implication is the proportion can be associated with SEO BHARs in a fashion predicted based on a long or short position.

Hedge funds can use trading strategies to capitalize on established patterns of price behavior.

Hedge funds enjoy a trading advantage over smaller investors.

This paper is the first study to document the association between hedge fund stratagems and stock returns around a major corporate event. It shows researchers should consider institutional trading strategies when studying the market response to a major corporate event.

The purpose of this paper is to test the new Fama and French (2015) five-factor model relying on a thorough sample of hedge fund strategies drawn from the Barclay’s Global hedge fund database.

The authors use a stepwise regression to identify the factors of the q-factor model which are relevant for the hedge fund strategy analysis. Doing so, the authors account for the Fung and Hsieh seven factors which prove very useful in the explanation of the hedge fund strategies. The authors introduce interaction terms to depict any interaction of the traditional Fama and French factors with the factors associated with the q-factor model. The authors also examine the dynamic dimensions of the risk-taking behavior of hedge funds using a BEKK procedure and the Kalman filter algorithm.

The results show that hedge funds seem to prefer stocks of firms with a high investment-to-assets ratio (low conservative minus aggressive (CMA)), on the one hand, and weak firms’ stocks (low robust minus weak (RMW)), on the other hand. This combination is not associated with the conventional properties of growth stocks – i.e., low high minus low (HML) stocks – which are related to firms which invest more (low CMA) and which are more profitable (high RMW). Finally, small minus big (SMB) interacts more with RMW while HML is more correlated with CMA. The conditional correlations between SMB and CMA, on the one hand, and HML and RMW, on the other hand, are less tight and may change sign over time.

To the best of the authors’ knowledge, the authors are the first to cast the new Fama and French five-factor model in a hedge fund setting which account for the Fung and Hsieh option-like trading strategies. This approach allows the authors to better understand hedge fund strategies because q-factors are useful to study the dynamic behavior of hedge funds.

The purpose of this paper is to empirically analyze the “market-neutral” characteristics of the market-neutral strategy hedge funds in Chinese A-share market.

The analyses in the paper are conducted to study the market-neutral characteristics by means of index analysis, correlation analysis, β-neutral analysis and the three-factor model analysis.

The results show that the performance advantage of the market-neutral strategy hedge funds is obvious. Most market-neutral strategy funds are exposed to market risks and the α strategy funds also have obvious style factor exposure; strictly speaking, all of the market-neutral strategies have not reached the “market-neutral” requirements. This paper also finds that Chinese trading restrictions on stock index futures in September 2015 have a significant impact on Chinese market-neutral strategy hedge funds.

The conclusion of this paper has a certain reference value for understanding the risk characteristics and possible problems of hedge funds in emerging markets, and also has important reference value for investors.

The purpose of this paper is to examine the ability of hedge funds and funds of hedge funds to generate absolute returns using fund level data.

The absolute return profiles are identified using properties of the empirical distributions of fund returns. The authors use both Bayesian multinomial probit and frequentist multinomial logit regressions to examine the relationship between the return profiles and fund characteristics.

Some evidence is found that only some hedge funds strategies, but not all of them, demonstrate higher tendency to produce absolute returns. Also identified are some investment provisions and fund characteristics that can influence the chance of generating absolute returns. Finally, no evidence was found for performance persistence in terms of absolute returns for hedge funds but some limited evidence for funds of funds.

This paper is the first attempt to examine the hedge fund return profiles based on the notion of absolute return in great details. Investors and managers of funds of funds can utilize the identification method in this paper to evaluate the performance of their interested hedge funds from a new angle.

Using the properties of the empirical distribution of the hedge fund returns to classify them into different absolute return profiles is the unique contribution of this paper. The application of the multinomial probit and multinomial logit models in the fund performance and fund characteristics literature is also new since the dependent variable in the authors' regressions is multinomial.

Academic research has shown that diversification today may not only include stocks and bonds but also alternative investments like hedge funds. However, practical and effective methods to identify the hedge fund styles that really enhance the risk return characteristics of a traditional portfolio as well as optimal allocation sizes are not available. The aim of the paper is to try to close this gap by proposing a portfolio optimization approach based upon the traditional market exposures of the different hedge fund strategies.

For this purpose, the paper first measures the bull and bear market betas of the main hedge fund strategies (equity market neutral, event driven, global macro, relative value, and managed futures). Based on the strategy characteristics, the authors then develop a systematic framework that calculates what percentage of each basic asset should be substituted for by hedge fund strategies to achieve the maximum results. The paper uses hedge fund index data from Hedge Fund Research and Barclay Hedge for the January 1999‐April 2011 sample period.

The empirical results show that this approach leads to an improvement in the annualized return of the optimized portfolio.

The paper adds to the existing literature by showing that it is possible to substitute traditional assets with hedge fund indices based on their exposures (beta) in varying market environments as a way to optimize the overall portfolio.

The purpose of this paper is to investigate the return performance of different investment strategies in the hedge fund sector, with a particular emphasis on the recent US financial crisis of 2007‐2010. Additionally, the paper aims to investigate the comovement of hedge fund index returns.

The paper identifies broad hedge fund investment strategies using data from the Dow Jones Credit Suisse Hedge Fund Database. It examines the return comovement using the cross‐sectional volatility, covariance, and correlation metrics proposed in Adrian (2007). In addition, the paper examines whether correlations and covariance are important determinants of future volatility via traditional time‐series regressions.

The paper finds that the majority of the broad hedge fund investment strategies incurred record level losses and gains during the 2007‐2010 period. In addition, it finds that the crisis period was preceded by high correlations, attributed primarily to a rise in cross‐sectional hedge fund covariances. However, during the crisis period, a decrease in average correlations, stemming from an increase in hedge fund volatility, is documented. The time‐series regressions are supportive of a strong relationship between cross‐sectional covariances and subsequent volatility, suggesting that systemic risk occurs in the hedge fund sector when returns move significantly in dollar terms.

This study is one of the first investigations that focus on the comovement and volatility of hedge fund index returns during the US financial crisis of 2007‐2010.

The purpose of this paper is to explore if hedge fund variables (HFVs) are associated with long-run compounded raw returns (CRRs) for seasoned equity offering (SEO) firms for a six-year window around the offering month for firms undergoing SEOs.

The event study methodology is used to calculate long-run CRRs that are used in a regression model as dependent variables. Independent variables include HFVs and nonhedge fund variables (NFVs) with standard errors clustered at the month level.

Three new long-run findings, consistent with recent short-run findings, are offered. First, HFVs are significantly associated with long-run CRRs for SEO firms. Second, HFVs perform competitively compared to NFVs. Third, a potential omitted-variable bias results if HFVs are not used.

This research assumes that hedge fund managers can identify good (poor) performing SEO firm that allow for profitable long (short) positions. The proportion of hedge funds using a strategy will change in the hypothesized manner needed to make profit.

Hedge fund managers can use long-run strategies to capitalize on price movements around significant corporate events.

Larger institutional traders have investment advantages due to superior knowledge and greater ability to manipulate prices.

This research is the first study to detail the significant association between hedge fund stratagems and long-run stock returns for firms undergoing key corporate events. This study demonstrates the need to consider hedge fund strategies when trying to understand stock price movements.

The purpose of this paper is to examine the possibility of creating hedge funds “clones” using liquid exchange traded instruments.

Authors analyze the performance of fixed weight and extended Kalman filter generated clone portfolios (EKF) for 14 hedge fund strategies from February 2004 to September 2009. EKF approach does not indeed impose any normality constraints on the error terms which allow the filter to find the optimal recursive process by itself. Such models could adjust even faster to sudden shifts in market conditions vs a standard Kalman filter.

For five strategies out of 14, this work finds that EKF clones outperform their corresponding indices. Thus, for certain strategies, the possibility of cloning hedge fund returns is indeed real. Results should be however considered with caution.

This paper suggests that the most important benefits of clones are to serve as benchmarks and to help investors to better understand the various risk factors that impact hedge fund returns.

Rather than using fixed‐weight and rolling windows approaches (as Hasanhodzic and Lo), this work considers an extended version of the Kalman filter, a computational algorithm that better captures the time changing dynamics of hedge fund returns. Also, in order to be practical, this research considers investable factors and that the models themselves could not be constant over time.