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The purpose of this paper is to investigate to what extent hedge funds are subject to irrationality in their investment decisions. The authors advance the hypothesis that irrational behavior affects hedge fund returns despite their sophistication and active management style.

The irrational component may follow a pattern consistent with the observed hedge fund returns yet far distant from market fundamentals. The authors include factors beyond the original version of capital asset pricing model such as Fama and French and Carhart models, as well as less stringent models, such as APT and Fung and Hsieh, to test whether these models are able to capture the irrational nature of the residuals.

After finding that institutional irrational sentiments play a role in hedge fund returns, we note that the returns are not completely shielded against irrational trading; however, hedge fund returns appear to be affected only by the irrational component derived from institutional trading rather than that emanated from individuals.

Different sources of irrationality may have asymmetric effects on hedge fund returns. Using a different set of sophisticated investors along with different market sentiment proxies may yield different results.

The authors argue that investors can use irrational beta to gauge the extent of institutional irrational sentiments prevailing in markets for the purpose of re-adjusting their portfolios and therefore use the betas as an early warning sign. It can also guide investors in avoiding funds and strategies that display greater irrational behavior.

The study advance the idea that the unexpected, hereafter irrational, component may follow a pattern consistent with the observed hedge fund returns, yet different from market fundamentals.

The paper's aim is to explore the impact of statistical arbitrage and high-frequency trading as hedge fund investment strategies that have a significant impact on the environment of corporations.

The paper is a meta-analysis of the role of investment strategies within complex systems.

The growth of hedge fund investment activity based on statistical arbitrage tends to produce a vulnerability; more funds using the strategy helps to create the profitable outcomes that the strategy relies upon. However, the growth also reduces the time lines of profitability and produces an underlying instability based on overlapping holdings and the use of leverage. The shortened timelines also create a further impetus towards technological competition and promotes high frequency trading, which then introduces further vulnerabilities based on “stop-loss cascades”.

Much of the trading creates a superficial form of liquidity, which gives a limited sense of market vulnerabilities. The basis of complex interactions between high frequency traders is also not clearly understood. Researchers and agents of policy ought to pay greater attention to the issues than is currently the case.

The area is one that is under-researched.

In December 2008, as the financial and economic crisis continued on its devastating course, a new scandal erupted. After the 1998s failure of Long-Term Capital Management, Madoff's fraud once again discredits the hedge funds industry. This scandal is, however, of a different kind. Indeed, Madoff's firm is not a standard hedge fund but a developed Ponzi scheme. By explaining Madoff's system and exploring the reasons for its collapse, this paper draws risk management lessons from this fraud, especially for operational risk management, due diligence processes, and the use of quantitative replication, regulatory, and standardizing approaches of the hedge fund industry.

Purpose – This chapter aimed to investigate hedge funds market risk. One aims to go further the traditional measures of risk that underestimates it by introducing a more appropriate method to hedge funds. One demonstrates that daily hedge fund return distributions are asymmetric and leptokurtic. Furthermore, volatility clustering phenomenon and the existence of ARCH effects demonstrate that hedge funds volatility varies through time. These features suggest the modelisation of their volatility using symmetric (GARCH) and asymmetric (EGARCH and TGARCH) models used to evaluate a 1-day-ahead value at risk (VaR).

Methodology/Approach – The conditional variances were estimated under the assumption that residuals t follow the normal and the student law. The knowledge of the conditional variance was used to forecast 1-day-ahead VaR. The estimations are compared with the Gaussian, the student and the modified VaR. To sum up, 12 VaRs are computed; those based on standard deviation and computed with normal, student and cornish fisher quantile and those based on conditional volatility models (GARCH, TGARCH and EGARCH) computed with the same quantiles.

Findings – The results demonstrate that VaR models based on normal quantile underestimate risk while those based on student and cornish fisher quantiles seem to be more relevant measurements. GARCH-type VaRs are very sensitive to changes in the return process. Back-testing results show that the choice of the model used to forecast volatility has an importance. Indeed, the VaR based on standard deviation is not relevant to measure hedge funds risks as it fails the appropriate tests. On the opposite side, GARCH-, TGARCH- and EGARCH-type VaRs are accurate as they pass most of the time successfully the back-testing tests. But, the quantile used has a more significant impact on the relevance of the VaR models considered. GARCH-type VaR computed with the student and especially cornish fisher quantiles lead to better results, which is consistent with Monteiro (2004) and Pochon and Teïletche (2006).

Originality/Value of chapter – A large set of GARCH-type models are considered to estimate hedge funds volatility leading to numerous evaluation of VaRs. These estimations are very helpful. Indeed, public savings under institutional investors management then delegate to hedge funds are concerned. Therefore, an adequate risk management is required. Another contribution of this chapter is the use of daily data to measure all hedge fund strategies risks.

The purpose of this paper is to examine in which ways hedge funds contribute to financialization.

Two already identified conduits through which financialization operates are applied to hedge funds.

The paper finds that hedge funds drive the phenomenon of financialization in two major ways, i.e. the financialization of corporations, and the financialization of markets. Hence, hedge funds can be conceived as agents of change for financialization.

There are indications that hedge funds possess disciplinary power. Future research should address this pivotal point, even though such power will be difficult to prove empirically.

Hedge funds have been found to potentially increase market volatility. In times of crisis, stricter regulation of these investors that take excessive risks seems prudent.

Through linking “hedge funds” with “financialization” this paper closes a research gap. In addition, the so far rather structural debate about financialization benefits from the actor-centered approach of this paper.

This research aims to examine hedge funds’ performance, risk and flow before and after the implementation of the Stop Trading on Congressional Knowledge (STOCK) Act.

This paper includes the use of different factor models to highlight the performance and risk of hedge funds before and after the implementation of the STOCK Act. Hedge fund holdings are retrieved from Thomson Reuters Lipper Hedge Fund Database (TASS).

This study finds significant differences before and after the implementation of the STOCK Act. The results for the entire sample period indicate that hedge funds suffered lower-alpha, standard deviation and idiosyncratic risk after the implementation of the STOCK Act.

The paper’s originality and value lie in addressing the relationship gap between the STOCK Act and hedge fund performance.

The purpose of this paper is to gain a better understanding of the market timing skills displayed by hedge fund managers during the 2007‐08 financial crisis.

The performance of a market timer can be measured through the 1966 Treynor and Mazuy model, provided the regression alpha is properly adjusted by using the cost of an option‐based replicating portfolio, as shown by Hübner. The paper adapts this approach to the case of multi‐factor models with positive, negative or neutral betas. This new approach is applied on a sample of hedge funds whose managers are likely to exhibit market timing skills. This concentrates on funds that post weekly returns, and analyzes three hedge funds strategies in particular: long‐short equity, managed futures, and funds of hedge funds. The paper analyzes a particular period during which the managers of these funds are likely to magnify their presumed skills, namely around the financial and banking crisis of 2008.

Some funds adopt a positive convexity as a response to the US market index, while others have a concave sensitivity to the returns of an emerging market index. Thus, the paper identifies “positive”, “mixed” and “negative” market timers. A number of signs indicate that only positive market timers manage to acquire options below their cost, and deliver economic significant performance, even in the midst of the financial crisis. Negative market timers, by contrast, behave as if they were forced to sell options without getting the associated premium. This behaviour is interpreted as a possible result of re sales, leading them to liquidate positions under the pressure of redemption orders, and inducing negative performance adjusted for market timing.

The paper suggests that the convexity in returns that is generally associated with market timing can be attributed to three sources: timing skills, exposure to nonlinear risk factors, or liquidity pressures. It manages to identify the impact of the latter two effects in the context of hedge funds.

Hedge fund replication gained considerable attention during the period surrounding 2007 when it was anticipated to become for hedge fund investors what index funds are for equity investors. The hedge fund replication concept only lacked a track record. This paper aims to present an updated evaluation.

Performance is evaluated on both a raw‐return basis and a risk‐adjusted basis using Fung and Hsieh's 8‐factor model. Particular emphasis is given to analyzing the performance of these products during the financial crisis and to highlighting the specific characteristics that distinguished them from their hedge fund cousins during this period.

The results show that the hedge fund replication space is definitely proving its existence as a credible hedge fund investment alternative. Talented hedge fund managers will always be in high demand, but they may have to just prove their high compensation a bit harder.

Although this study is based on a short sample period, the results indicate that hedge fund replication products delivered returns that were at par with the returns of hedge funds. The replication products performed comparatively well during the crisis probably as a result of having less exposure to illiquid assets.

To the best of the authors' knowledge, this article uses the most extensive data set of 22 hedge fund replication products to analyze their performance.

Good market timing skills can be an important factor contributing to hedge funds’ outperformance. In this chapter, we use a unique semiparametric panel data model capable of providing consistent short period estimates of the return correlations with three market factors for a sample of Long/Short equity hedge funds. We find evidence of significant market timing ability by fund managers around market crisis periods. Studying the behavior of individual fund managers, we show that at the 10% significance level, 17.12% of funds exhibit good linear timing skills and 21.32% of funds possess some level of good nonlinear market timing skills. Further, we find that market timing strategies of hedge funds are different in good and bad markets, and that a significant number of managers behave more conservatively when the market return is expected to be far above average and more aggressively when the market return is expected to be far below average. We find that good market timers are also likely to possess good stock selection skills.

The development of alternative investment has not yet been accompanied by genuine consideration of the specific characteristics of the risks and returns of hedge funds with regard to the provision of information to investors. To fill the gap, in 2004 EDHEC launched an international consultation process, seeking to implement a new framework for funds of hedge funds reporting.

The consultation process was based on a series of recommendations proposed by EDHEC with regard to the academic state‐of‐the‐art on risk measurement in the alternative universe. The findings of the survey, which brought together the opinions of 98 institutional investors and fund managers, allow a consensus to be established on the information required for the implementation of a relevant reporting method in the field of alternative investment.

Interestingly, despite somewhat conflicting goals, investors and fund managers, except for slight discrepancies, globally agree on the definition of relevant information, and as a result on the content of the reports of tomorrow.

A very large majority of hedge fund managers are satisfied with a reporting method based on a mean‐variance structure, which is totally inappropriate for the risk and return profiles of alternative investment. To address this issue, the paper presents a series of indicators relying on a basic return‐based analysis that allow a true and fair picture of the risk and return characteristics of alternative investment to be drawn. This paper therefore offers a pragmatic but robust answer, for both investors and fund managers, to the fund of hedge funds reporting quandary.