Chapter 8 - The Hedge Fund Paradigm

Tail risk of hedge funds: an extreme value application

A Total Risk Measurement Framework for Hedge Funds and Funds of Funds

Various studies have analyzed the determinants of hedge fund performance. The majority of them, however, come to contradictory conclusions with respect to the direction of influence of different factors on fund performance. The key reason for the inconsistencies is the highly dynamic nature of hedge funds. This paper specifically focuses on the dynamics of the relations between hedge fund performance and various microeconomic factors. It quantifies shifts in the average fund alpha that result from changes in hedge fund style, age, size, and fee structure and investigates the time variation of these shifts. The empirical results highlight the dynamic nature of the hedge fund industry. Hedge funds seem to generate a positive and significant alpha on average; however, the alpha level varies considerably over time. It is hard to predict the exact absolute alpha level based on the hedge fund micro-factors, but it seems to be possible to rank hedge funds using the micro-information. The results suggest that large funds with high relative inflow, charging higher than median management fees, are likely to deliver a higher alpha than their peers most of the time.KeywordsHedge FundHigh AlphaAbsolute FlowFund FlowAverage AlphaThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

PurposeThe current financial crisis revealed weaknesses in the US financial system, including the difficulty of valuing complex assets. This paper seeks to examine regulatory and compliance issues for hedge funds valuing complex assets.Design/methodology/approachWithin the context of hedge fund valuation, the paper provides a general overview of: the regulatory background of hedge funds and the central role valuation plays in the operation and regulation of such funds; relevant cases brought by the SEC; and a discussion of valuation best practices.FindingsHedge funds are not “unregulated.” There is a body of law and accounting standards that applies to hedge fund valuation. Nevertheless, hedge fund valuation standards are evolving in this era of heightened regulatory scrutiny. The common concepts that have emerged from valuation best practices will likely provide the underpinning for any regulatory initiatives regarding hedge fund valuation.Research limitations/implicationsBy the time of publication, Congress may pass pending legislation governing hedge funds and there may be additional notable SEC cases on hedge fund valuation.Practical implicationsThe economic crisis has revitalized the SEC's interest in this area. Consequently, hedge funds should consider adoption of a compliance program that specifically targets valuation by stressing investor disclosure, independence of the valuation function, comprehensive written valuation polices and procedures, and internal controls.Originality/valueThe paper compiles and organizes in one place the regulatory and compliance standards governing asset valuation by hedge funds.

Can Liquidity Risk Explain the Size-Performance Relationship for Hedge Funds?

The data dependency of empirical financial research is of common concern to both academics and practitioners. This is especially true for hedge funds because no single, commonly accepted database exists and because many of the databases may hold different sets of reporting managers. Each database uses current reporting managers as the basis for the construction of hedge fund indices, and these index returns reflect the characteristics of the funds reporting to the relevant database. Unlike historical returns derived from current databases, however, historical returns from most major hedge fund indices do not contain backfill or survivor bias. At the same time, performance characteristics may differ between indices because each index is constructed based on a different set of rules (e.g., equal weighted, asset weighted, and so on). In this article, the authors conduct a series of empirical tests similar to those previously conducted in academic studies. The authors use only those hedge fund indices that reflect the average returns of the entire set of reporting managers; that is, the indices representing overall industry returns. Results indicate that return-based style analyses, often used as a basis for hedge fund analysis, are impacted both by the period of analysis as well as the hedge fund index used. Moreover, results indicate that the addition of variables beyond those designed to capture underlying equity, interest rate, and credit risk have little impact on the explanatory power of these hedge fund universe indices beyond a very low level of statistical significance.

This paper models hedge fund exposure to risk factors and examines time-varying performance of hedge funds. From existing models such as asset-based style (ABS)-factor model, standard asset class (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 explain most of the variance in performance of each hedge fund portfolio based on investment strategy. The results show instability of coefficients in the performance attribution regression. Incorporating a time-varying factor exposure feature would be the best way to measure hedge fund performance. Furthermore, the optimal models with fewer factors exhibit greater explanatory power than existing models. Using rolling regressions, our customized investment strategy model shows how hedge funds are sensitive to risk factors according to market conditions.

A primer on hedge funds

What are the diversification effects of introducing hedge funds into a portfolio of stocks and bonds? In terms of skewness and kurtosis, equity and hedge funds do not combine very well. Although hedge funds significantly improve a portfolio's mean-variance characteristics, their inclusion can also be expected to lead to significantly lower skewness as well as higher kurtosis. Hedge funds do not provide a free lunch, but rather entail a definite trade-off between profit and loss potential. To have any impact on the overall portfolio, allocations to hedge funds would have to far exceed the typical 1% to 5% that many institutions typically consider.

This study examines the impact of trading costs and related factors on the unobserved performance of hedge funds through a qualitative literature review approach. The review covers recent studies on explicit and implicit costs, liquidity risk, portfolio complexity, hedge fund size, and risk management strategies. The findings reveal that trading costs significantly affect the net returns of hedge funds, while liquidity risk and portfolio complexity hinder asset management efficiency. Large hedge funds benefit from economies of scale but face challenges in risk management and transparency. The study also highlights that data-driven risk management strategies can enhance long-term return stability. This research underscores the need for a holistic evaluation to understand hedge fund performance and discusses limitations associated with the qualitative approach.

Three Essays on Corporate Policies Olga Kuzmina Different fields of economics have historically tended to focus on firms’ strategies in isolation. In contrast, a lot of the recent work explores how various aspects of firm behavior interact with each other. This dissertation contributes to this growing literature by studying the interdependences of organizational and financial policies within firms in different contexts. The first essay studies the interactions between acquisition decisions of multinationals and innovation decisions in the subsidiaries they buy. My coauthors Maria Guadalupe and Catherine Thomas, and I use a rich panel dataset of Spanish manufacturing firms and a propensity score reweighting estimator to show that multinational firms acquire the most productive domestic firms, which, on acquisition, conduct more product and process innovation (simultaneously adopting new machines and organizational practices) and adopt foreign technologies, leading to higher productivity. The proposed model of endogenous selection and innovation in heterogeneous firms can explain both the observed selection patterns and the innovation decisions. The innovation upon acquisition is further shown in the data to be associated with the increased market scale provided by the parent firm, thereby highlighting the role of foreign ownership in increasing the benefits from innovation. This work has potentially important implications for the evolution of within-industry productivity distributions. Under the mechanism described in the paper, foreign entry may lead to divergence of productivity and contribute to the stylized fact of large and persistent productivity differences even within narrowly defined industries. I further use this rich dataset in my second essay to establish a causal relationship between the use of flexible contractual arrangements with labor and capital structure of the firm. Using the exogenous inter-temporal variation from government subsidies, I find that hiring more temporary workers leads firms to have more debt. Since temporary workers, unlike permanent ones, can be fired at a much lower cost during their contract duration, or their contracts may be not extended upon expiration, a firm can more easily meet its interest payments and avoid bankruptcy when faced with a negative shock. I interpret this result as evidence of flexible workforce decreasing operating leverage which, in turn, promotes financial leverage. This study therefore contributes to the literature exploring the interactions between firm employment decisions and corporate policies by providing evidence for a new channel – the one of flexible employment contracts. Given the overwhelming extent of labor reforms in continental Europe in recent years that are aimed at offering more job security to workers, it is important to understand how such policies would affect firms, and for that it is necessary to model the interdependences of firms’ strategies. Finally, my third essay looks at a different type of firms – hedge funds. Although, they do not produce goods in a strict sense of the word, they provide valuable services to investors by smartly investing into large selections of assets. Hedge funds are a very interesting type of financial firms to study due to their lower regulation and reporting standards that enable them to use some know-how trading strategies and potentially outperform other investors. A part of such outperformance can be explained by higher risks born by certain hedge funds, which outlines the broad question we explore in this paper with my coauthor Sergiy Gorovyy. We use a proprietary dataset obtained from a fund of funds to study the risk premia associated with hedge fund transparency, liquidity, complexity, and concentration over the period from April 2006 to March 2009. We are able to directly measure these qualitative characteristics by using the internal grades that the fund of funds attached to all the funds it invested in, and that represent the unique information that cannot be obtained from quantitative data alone. Consistent with factor models of risk premium, we find that during the normal times low-transparency, lowliquidity, low-complexity, and high-concentration funds delivered a return premium, with economic magnitudes of 5% to 10% per year, while during bad states of the economy, these funds experienced significantly lower returns. We also offer a novel explanation for why highly concentrated funds command a risk premium by revealing that it is mostly prevalent among the non-transparent funds where investors are unaware about the exact risks they are facing and hence cannot diversify them away. The large an significant return premium associated with more secretive, less transparent hedge funds has an important policy implication with respect to whether hedge funds should be required to disclose the information regarding their trades and positions, especially in the light of the recent regulatory changes, including the Dodd-Frank Wall Street Reform Act passed in July 2010, the consequences of which are yet to be evaluated.

Using data from the Lipper/TASS hedge fund database over the period 1994–2011, the authors examine the effect of liquidity risk on the relationship between size and performance for funds of hedge funds (FOFs). After confirming a significant positive size effect for FOFs, they explicitly introduce liquidity risk and find that this scale effect becomes more pronounced among FOFs with lower liquidity risk. To the extent that more illiquid FOFs exhibit higher liquidity risk, the results provide evidence in support of the liquidity hypothesis that size does not erode, but instead helps improve, performance, since FOFs do not directly manage portfolios of securities and thus are less affected by the liquidity costs associated with trading a large portfolio.

The rapid growth of the hedge fund industry presents significant business opportunity for the institutional investors particularly in the form of portfolio diversification. To facilitate this, there is a need to develop a new set of risk analytics for investments consisting of hedge funds, with the ultimate aim to create transparency in risk measurement without compromising the proprietary investment strategies of hedge funds. As well documented in the literature, use of dynamic options like strategies by most of the hedge funds make their returns highly non-normal with fat tails and high kurtosis, thus rendering Value at Risk (VaR) and other mean-variance analysis methods unsuitable for hedge fund risk quantification. This paper looks at some unique concerns for hedge fund risk management and will particularly concentrate on two approaches from physical world to model the non-linearities and dynamic correlations in hedge fund portfolio returns: Self Organizing Criticality (SOC) and Random Matrix Theory (RMT).Random Matrix Theory analyzes correlation matrix between different hedge fund styles and filters random noise from genuine correlations arising from interactions within the system. As seen in the results of portfolio risk analysis, it leads to a better portfolio risk forecastability and thus to optimum allocation of resources to different hedge fund styles. The results also prove the efficacy of self-organized criticality and implied portfolio correlation as a tool for risk management and style selection for portfolios of hedge funds, being particularly effective during non-linear market crashes.

The hedge fund universe is not homogeneous and should not be considered a coherent asset class. Rather, hedge fund management is an extension of traditional active management, with some critical differences. Thus, if investors seek to place portfolio constraints on hedge fund allocation, they should do so not to limit general exposure to an asset class but rather to achieve a certain level of exposure to manager and liquidity risk.

Hedge Fund Performance 1990-2000: Do the Money Machines Really Add Value?