Measuring Hedge Fund Performance

This paper presents a model to link daily hedge fund performance with the returns on indices selected to provide a comprehensive spectrum of possible market exposures. The model gives an estimate of the daily returns of hedge funds based on the daily values of a list of market indices. The daily return of each hedge fund is estimated as a linear combination of daily market index returns. The coefficients of this linear combination are obtained through linear regression of monthly index returns against monthly hedge fund returns

The Statistical Properties of Hedge Fund Index Returns

he monthly return distributions of many hedge fund indices exhibit highly unusual skewness and kurtosis properties as well as first-order serial correlation. This has important consequences for investors. We demonstrate that although hedge fund indices are highly attractive in mean-variance terms, this is much less the case when skewness, kurtosis and autocorrelation are taken into account. Sharpe Ratios will substantially overestimate the true risk-return performance of (portfolios containing) hedge funds. Similarly, mean-variance portfolio analysis will over-allocate to hedge funds and overestimate the attainable benefits from including hedge funds in an investment portfolio. We also find substantial differences between indices that aim to cover the same type of strategy. Investors’ perceptions of hedge fund performance and value added will therefore strongly depend on the indices used.Hedge fund, hedge fund index, skewness, kurtosis, autocorrelation, sharpe ratio, mean-variance analysis

Three Essays on Hedge Funds

This dissertation consists of three chapters. The first two chapters focus on the Chinese hedge fund industry, and the third chapter focuses on American and offshore hedge funds. In the first chapter, I study the Chinese hedge fund industry during its earliest development from 2003 to 2013. I find that it outperforms the Chinese stock market over this period by about 200% in cumulative returns. I also find that different investment strategies lead to significant differences in a fund’s performance, risk taking behavior, and return generating process, although no investment strategy demonstrates persistence in performance during this period. Moreover, I point out that for any research on survival issues of Chinese hedge funds, it is necessary to distinguish between dissolved funds according to why a fund stops reporting to a database. Chinese hedge funds are different from other hedge funds in the world because of their self-chosen disclosing mechanism, special legal structure, and constant policy changes. So in the second chapter, I investigate whether these special features affect the performance of Chinese hedge funds. I find strong evidence that better fund performance is associated with more frequent fund disclosure, higher complexity of trust companies and fund management companies, and slower speed of fund families in launching new funds. I also provide evidence that the new policy in July 2011, which allows trust companies to trade stock index futures, brings fundamental changes to the hedge fund industry, especially funds that focus on hedging techniques. The third chapter studies hedge funds and their service providers. By building a comprehensive numeric score of hedge funds’ service providers, I study the relationship between hedge funds’ use of service providers and funds’ characteristics, performances, and investor flows. I find that using well-known service providers is associated with larger fund size, younger fund age, offshore domiciliation, better past performance, and smaller and less volatile cash flows from investors, and it can also predict better fund performance in the future. My results are robust across different fund sizes, investment strategies, and different levels of asset growth

Welcome to the Dark Side - Hedge Fund Attrition and Survivorship Bias over the period 1994-2001

Hedge funds exhibit a high rate of attrition that has increased substantially over time. Using data over the period 1994-2001, we show that lack of size, lack of performance and an increasingly aggressive attitude of old and new fund managers alike are the main factors behind this. Although attrition is high, survivorship bias in hedge fund data is quite modest, which reflects the relatively small difference in performance between surviving and defunct funds. Concentrating on survivors only will overestimate the average hedge fund return by around 2% per annum. For small, young, and leveraged funds, however, the bias can be as high as 4-6%. We also find significant survivorship bias in estimates of the standard deviation, skewness and kurtosis of individual hedge fund returns. When not corrected for, this will lead investors to seriously overestimate the benefits of hedge funds. We find fund of funds attrition to be much lower than for hedge funds. Combined with a small difference in performance between surviving and defunct funds of funds, this yields relatively low survivorship bias estimates for funds of funds.

Quantitative Selection of Long-Short Hedge Funds

We develop a quantitative model to select hedge funds in the long-short equity sector. The selection strategy is verified on a survivorship-bias-free hedge fund database, from January 1990 to September 2002. We focus on the hedge funds acting exclusively in the U.S. market. We identify Fama-French factors and GSCI as the risk factors. Based on the evidence that many hedge funds do not exhibit persistent performance, we believe that persistent alpha is not generated based on publicly available information and opportunistic changes of exposure with respect to the risk factors. Instead we expect moderate exposure funds to be those who establish investment decisions based on special information or proprietary research. A hedge fund selection strategy is introduced and checked with out-of-sample data. A simulation of hedge funds from 1927 to 2002 is conducted. The funds selected according to our strategy demonstrate superior performance persistently.Hedge Fund; Long Short Strategy; Fama-French; Commodity; Performance Persistence; Skewness; Selection

Monitoring hedge funds: a fi nancial stability perspective.

Investor inflows into hedge funds have been significant in recent years and they have continued unabated. As a result, the presence and role of these investment funds in global capital markets have become increasingly important, and to a much greater extent than the amount of capital they manage would suggest. This is because hedge funds can, and often do, leverage their investment positions. Indeed, their leveraged assets are sometimes comparable with the assets of large banks. The growing and active participation of hedge funds in a large number of financial markets implies that the functioning of these markets could be seriously affected if the hedge fund sector came under stress. The positive contribution of hedge funds to the efficiency and liquidity of global financial markets is widely recognised, but there are also concerns that in times of stress their activities may create risks to financial stability. The lack of transparency and limited publicly available information about their balance sheets and activities poses significant challenges for financial stability analysis. While it is possible to base such an analysis on a multitude of information sources on hedge fund activities – including dedicated financial media, commercial hedge fund databases, quarterly industry reports, hedge fund return indices, academic studies, some supervisory data and market surveillance – these sources are not sufficient for an adequate monitoring and robust evaluation of hedge fund activities from a financial stability perspective. Three groups of indicators could be important for financial stability analysis, namely those which shed light on banks’ exposures to hedge funds, provide yardsticks of the crowding of hedge fund trades, and facilitate the gauging of endogenous hedge fund vulnerabilities. The latter group would include the measures of funding liquidity risk, leverage and exposures to market risk factors. The construction of all these indicators would be greatly facilitated if basic information on hedge fund balance sheets were available. Since this is not the case, various indirect estimation methods have to be relied upon. A “desirable vs. available” analysis reveals the most important information gaps, but it does not aim at providing recommendations on how to enhance hedge fund transparency in practice. Instead, it proposes three elements which a transparency framework would ideally include: fi rst, more aggregate information to all market participants; second, a highly standardised reporting template that would make disclosures more effective; fi nally, adequate information for a joint analysis of the aggregate activities of banks, hedge funds and other highly leveraged institutions in order to have a comprehensive picture of risks to the smooth functioning of financial markets.

Measuring the Influence of Commodity Fund Trading on Soybean Price Discovery

The increase in commodity fund trading in the agricultural commodity futures markets has raised concern that this trading is degrading the price discovery performance of these markets. We used the Beveridge-Nelson Decomposition procedure to estimate the price discovery performance of the soybean futures and spot markets. We found that the price discovery performance of the soybean futures market has improved along with the increased commodity fund trading. Our results indicated that a portion of the price discovered in the soybean futures market is passed to the spot market.price discovery, commodity funds, cointegration, Beveridge-Nelson decomposition,

VIX AND VIX FUTURES: A TOOL OF RISK REDUCTION AND DOWNSIDE PROTECTION FOR HEDGE FUNDS

We analyze VIX, VIX futures and hedge funds. VIX is a measure of the implied volatility of S&P 500 index options. VIX futures performance is measured by the S&P 500 VIX Mid-Term Futures Index and the CBOE VIX Premium Strategy Index. Credit Suisse Hedge Fund Index and Hedge Fund Research Indices represent hedge funds performance. In our project, we expand Dash and Moran (2005) by expanding the end period of survey from December 2004 to May 2014 and including two hedge fund databases, Credit Suisse and Hedge Fund Research. In addition, we conduct analyses on both VIX index and VIX futures indices, which are not included in the Dash and Moran (2005). Not only we check the addition of VIX index or VIX futures indices to hedge fund portfolios for risk reduction or downside protection, but also our analysis pays more attention to the period of 2008 financial crisis. We find that broad-based hedge fund indices and most narrow hedge fund indices are negatively and asymmetrically correlated with VIX. Addition of VIX index as well as VIX futures indices protects hedge fund portfolios from major drawdowns and helps reduce risk

Bear Factor and Hedge Fund Performance

We show that a simple and intuitive variable, the return of a bear spread portfolio orthogonalized with respect to the market (H-Bear factor), can serve as a new benchmark for explaining the cross-section of hedge fund returns. Low H-Bear exposure funds (bear risk insurance sellers) outperform high H-Bear exposure funds (bear risk insurance buyers) by 0.58% per month on average, outperform even during market crashes, but underperform when bear market risk materializes. Overall, we identify a new risk dimension that affects hedge fund performance, and we show that this risk factor is distinct from the already popular realized tail risk

Hedge fund performance attribution under various market conditions

We investigate US hedge funds' performance. Our proposed model contains exogenous and endogenous break points, based on business cycles and on a regime switching process conditional on different states of the market. During difficult market conditions most hedge fund strategies do not provide significant alphas. At such times hedge funds reduce both the number of their exposures to different asset classes and their portfolio allocations, while some strategies even reverse their exposures. Directional strategies share more common exposures under all market conditions compared to non-directional strategies. Factors related to commodity asset classes are more common during these difficult conditions whereas factors related to equity asset classes are most common during good market conditions. Falling stock markets are harsher than recessions for hedge funds