Elsevier

Journal of Empirical Finance

Volume 6, Issue 3, September 1999, Pages 309-331
Journal of Empirical Finance

A primer on hedge funds

https://doi.org/10.1016/S0927-5398(99)00006-7Get rights and content

Abstract

In this paper, we provide a rationale for how hedge funds are organized and some insight on how hedge fund performance differs from traditional mutual funds. Statistical differences among hedge fund styles are used to supplement qualitative differences in the way hedge fund strategies are described. Risk factors associated with different trading styles are discussed. We give examples where standard linear statistical techniques are unlikely to capture the risk of hedge fund investments where the returns are primarily driven by non-linear dynamic strategies.

Introduction

Institutional investors and wealthy individuals have long been interested in hedge funds as alternative investments to traditional portfolios of assets. For over half a century of its existence, the hedge fund industry has stayed opaque to the general investing public. Increasingly, spectacular hedge fund activities in the last decade, such as the attack on the British Pound led by George Soros and the recent collapse of Long-Term Capital which prompted the intervention from federal regulators, have heightened the public's interest in the hedge fund industry. The literature on the industry has grown substantially. The depth of the literature is still limited to showing readers “how” hedge funds are organized juxtaposed with stylized facts that are often hard to piece together into a coherent framework. In depth discussions can be found but are typically limited to the philosophy of a single investment style such as the work by George Soros.

In this paper, we attempt to provide a rationale on how hedge funds are organized, and provide some insight as to why one should expect hedge fund performance to differ from traditional mutual funds. Statistical differences among hedge fund styles are used to supplement qualitative differences in the way hedge fund strategies are described. Although one is constrained by space limitations, we hope to convey to the reader that hedge funds differ from each other not just because they describe themselves differently but their strategies and therefore their return characteristics do indeed differ.

Section snippets

A brief history of hedge funds

Hedge funds are generally regarded as private investment vehicles for wealthy individuals or institutional investors. They are typically organized as limited partnerships, in which the investors are limited partners and the managers are general partners. As general partners, the fund managers usually invest in a significant portion of their personal wealth into the partnership to ensure the alignment of economic interests among the partners. Investors to the partnership are charged a

The legal environment of hedge funds

We believe the difference in return characteristics between hedge funds and mutual funds is primarily due to differences in trading strategies. One fundamental difference is that hedge funds deploy dynamic trading strategies whereas most mutual funds employ a static buy-and-hold strategy. Another fundamental difference is the use of leverage. Hedge funds typically leverage their bets by margining their positions and through the use of short sales. In contrast, the use of leverage is often

The need for privacy and a regulatory-free environment

Having briefly reviewed the environment within which US-based hedge fund managers operate, we are now in a position to conjecture on the economics of hedge fund organizations.

Consider the problem confronting a money manager who believes that he has a set of skills that could earn above average risk adjusted returns. We are not advocating the existence of such strategies, but merely the hypothesis that the manager believes this to be so. Let us assume that the manager has a limited amount of

A qualitative summary of hedge fund investment styles

Although the institutional environment can affect the way in which hedge funds are organized, it is their return characteristics that hold the key to distinguishing between hedge fund strategies. To help investors assess the potential diversification benefits, consultants classify hedge funds and CTA funds based on their self-described investment styles.

Table 4 lists the seven main categories used by MAR and MAR/Hedge,

Quantitative characterization of hedge fund investment styles

As an alternative to the qualitative style descriptions, Fung and Hsieh (1997a)provided a quantitative classification scheme based on returns alone. The idea is simple. If two managers use the same trading strategy on the same markets, their returns are correlated to each other, even if they are not correlated to the returns of asset markets. Using principal component analysis to group funds based on their correlation with each other, Fung and Hsieh (1997a)found that the first five principal

The return characteristics of trend following CTAs

We believe it is the dynamic nature of hedge fund trading strategies that makes their returns appear to be uncorrelated to the major asset markets despite the fact that these are the same markets that hedge funds frequently transact in. However, there are some persistent patterns which differ across styles, and these differences can be illustrated with a graphical technique. We begin with the “trend following” style.

“Trend following” is a term used by the majority of CTAs to describe their

The return characteristics of Global/Macro funds

Most of the large hedge funds (in excess of US$1 billion in capital) are Global/Macro funds. Included in this group are the better known funds such as those managed by George Soro's Quantum Group, Julian Robertson's Jaguar Fund, Louis Bacon's Moore Global Fund, Leon Cooperman's Omega Overseas Fund, and Mark Kingdon's Kingdon Fund. Global/Macro funds had been the focus of attention in the Asian currency crisis of 1997. A legacy of George Soros' famous attack that contributed to the devaluation

The return characteristics of fixed income arbitrage

Fig. 5 applies this technique to a fixed income arbitrage fund. Its return seems to be insensitive to US equities. It tends to make 1% a month, or 12% a year, with very little volatility. How can a fund produce equity-like returns with bond-like volatility? In the July–August 1998 issue of Plan Sponsor magazine, we offered three possible explanations. (a) Fixed income arbitrage funds are capturing true mispricings; (b) they are acting as market makers providing liquidity; (c) they sell economic

The return characteristics of other styles

Fig. 6 graphs the return characteristics of short-sellers against the S&P market environment. Not surprising, short-sellers perform well in down markets and poorly in up markets.

Fig. 7 graphs the return characteristics of distressed securities funds against the high yield bond market environment. These hedge funds perform better in up markets, and less well in down markets. But the hedge funds outperform high yield bonds in all markets.

The case of Long-Term Capital Management (LTCM)

Where does LTCM fit in the hedge fund world? In terms of self-described style, LTCM fits the profile of fixed income arbitrage funds, although in the aftermath of their debacle, press reports also mention merger arbitrage and the sale of straddles on stock indices. In the quantitative dimension, LTCM has low correlation to the standard asset markets. The regression of LTCM's monthly returns from 1994 to 1997 on the eight asset classes used in Fung and Hsieh (1997a)has an R2 of only 7%. LTCM's

Conclusions

Research has shown that hedge funds and CTA funds have returns very different from those of mutual funds. This is likely due to the difference in investment style and trading strategies, possibly due to the different regulatory environment.

This paper summarizes the regulatory environment for US hedge funds and provides brief descriptions of their major trading strategies. It also provides the return characteristics of several different styles. These style classifications can be useful in

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