Chasing performance persistence of hedge funds

The thesis examines the performance persistence of hedge funds using complement methodologies (namely cross-sectional regressions, quantile portfolio analysis and Spearman rank correlation test). In addition, six performance ranking metrics and six different combinations of selection and holding periods are compared. The data is gathered from HFI and Tremont databases covering over 14,000 hedge funds and time horizon is set from January 1996 to December 2007. The results suggest that there definitely exists performance persistence among hedge funds and the strength and existence of persistence vary among fund styles. The persistence depends on the metrics and combination of selection and prediction period applied. According to the results, the combination of 36-month selection and holding period outperforms other five period combinations in capturing performance persistence within the sample. Furthermore, model-free performance metrics capture persistence more sensitively than model-specific metrics. The study is the first one ever to use MVR as a performance ranking metric, and surprisingly MVR is more sensitive to detect persistence than other performance metrics employed.

Hedge-rahastojen menestysmittaus

Tutkimuksen tavoitteena on selvittää miten hedge-rahastot eroavat ns. normaaleista osakerahastoista ja kuinka suomalaiset hedge-rahastot ovat pärjänneet tutkimusjaksolla 2003-2005. Tutkimuksen empiirisen osan aineisto on kerätty julkisesti saatavilla olevasta informaatiosta. Aineistoon on kerätty seitsemän suomalaista ja yksi ruotsalainen hedge-rahasto. Empiirinen osa mittaa rahastojen menestymistä siihen sopiviksi valituilla menestysmittareilla. Tulokset osoittavat, että suomalaiset hedge-rahastot ovat pärjänneet käytetyillä mittareilla tutkimusperiodilla verrattain huonosti. Tutkittavista rahastoista tutkimuksessa parhaiten menestyi ruotsalainen Erik Penser Hedge Fond.

Hedge-rahastojen menestyksen pysyvyyden empiirinen tarkastelu sijoittajan näkökulmasta

This study examines performance persistence of hedge funds from investor's point of view and look at the methods by which an investor could choose the successful hedge funds to the portfolio. This study was used the data from HFI & Tremont databases on period 1998-2007. In this study used the 36-month combination (24-month selection and 12-month prediction periods). As the research methods used the Sharpe index, raw returns, MVR (mean variance ratio), GSC-clustering, the SDI index and the new combination of metrics. The evaluation criterions of the results used the volatility, excess returns and the Sharpe index. This study compared different results from the 7 time series with each other, and commenting the problems on a portfolio loss of funds.

Hedge-rahastojen menestyksen pysyvyys ja suhteellinen suoriutuminen sijoittajan näkökulmasta

The purpose of the thesis is to examine the long-term performance persistence and relative performance of hedge funds during bear and bull market periods. Performance metrics applied for fund rankings are raw return, Sharpe ratio, mean variance ratio and strategy distinctiveness index calculated of the original and clustered data correspondingly. Four different length combinations for selection and holding periods are employed. The persistence is examined using decile and quartile portfolio formatting approach and on the basis of Sharpe ratio and SKASR as performance metrics. The relative performance persistence is examined by comparing hedge portfolio returns during varying stock market conditions. The data is gathered from a private database covering 10,789 hedge funds and time horizon is set from January 1990 to December 2012. The results of this thesis suggest that long-term performance persistence of the hedge funds exists. The degree of persistence also depends on the performance metrics employed and length combination of selection and holding periods. The best results of performance persistence were obtained in the decile portfolio analysis on the basis of Sharpe ratio rankings for combination of 12-month selection period and the holding period of equal length. The results also suggest that the best performance persistence occurs in the Event Driven and Multi strategies. Dummy regression analysis shows that a relationship between hedge funds and stock market returns exists. Based on the results, Dedicated Short Bias, Global Macro, Managed Futures and Other strategies perform well during bear market periods. The results also indicate that the Market Neutral strategy is not absolutely market neutral and the Event Driven strategy has the best performance among all hedge strategies.

ETF hedge - rahastojen suoriutuminen sijoittajan näkökulmasta aikavälillä 2009 – 2013

Tutkielmassa analysoidaan yhtä ETF rahastojen viimeisimpiä aluevaltauksia: ETF hedge-rahastoja, joista ensimmäiset saivat alkunsa vuonna 2009 finanssikriisin aiheuttamien sääntömuutosten johdosta. ETF hedge-rahastot imitoivat perinteisten hedge-rahastojen positioita tarkoituksenaan saavuttaa hedge-rahastojen perinteisesti suuret voitot, mutta ilman niille ominaista suurta kustannusrakennetta. Tutkimuksessa selvitetään, miten ETF hedge-rahastot ovat suoriutuneet Yhdysvaltain markkinoihin nähden, sekä miten nämä rahastot ovat onnistuneet hedge-rahastojen position imitoinnissa.

Sijoitus- ja hedgerahastojen riski- ja menestysmittareiden vertaileva analyysi

Tutkimuksen tavoitteena on selvittää eroavatko erilaiset suorituskyvyn mittarit toisistaan ja antavatko ne toisista poikkeavia järjestyslukuja. Tutkimuksessa on käytetty seuraavia suorituskyvyn mittareita: Sharpen indeksi, mukautettu Sharpen indeksi, Safety first, Calmarin indeksi, Value at Risk, UPR, RMAD, RTSD, RTASD, Gini-pohjaiset, Z-pohjaiset sekä muutama modifioitu Sharpen indeksi. Tutkielman aineisto on saatu julkisista tietokannoista sekä osittain suljetuista tietokannoista. Aineisto koostuu sekä Amerikkalaisista hedge-rahastoista, että Suomalaisista osakerahastoista. Tuloksista on nähtävissä, että suorituskyvyn mittarit eivät paljonkaan poikkea toisistaan, kun selvitetään eroavatko tunnuslukujen antamat rahastojen parammuusjärjestykset toisistaan.

Performance persistence of equity funds in the Russian stock market

This thesis investigates performance persistence among the equity funds investing in Russia during 2003-2007. Fund performance is measured using several methods including the Jensen alpha, the Fama-French 3- factor alpha, the Sharpe ratio and two of its variations. Moreover, we apply the Bayesian shrinkage estimation in performance measurement and evaluate its usefulness compared with the OLS 3-factor alphas. The pattern of performance persistence is analyzed using the Spearman rank correlation test, cross-sectional regression analysis and stacked return time series. Empirical results indicate that the Bayesian shrinkage estimates may provide better and more accurate estimates of fund performance compared with the OLS 3-factor alphas. Secondly, based on the results it seems that the degree of performance persistence is strongly related to length of the observation period. For the full sample period the results show strong signs of performance reversal whereas for the subperiod analysis the results indicate performance persistence during the most recent years.

Testing Futures Market Efficiency and Optimal Hedge Ratio Estimation: Evidence from Futures and Options on RTS

This study investigates futures market efficiency and optimal hedge ratio estimation. First, cointegration between spot and futures prices is studied using Johansen method, with two different model specifications. If prices are found cointegrated, restrictions on cointegrating vector and adjustment coefficients are imposed, to account for unbiasedness, weak exogeneity and prediction hypothesis. Second, optimal hedge ratios are estimated using static OLS, and time-varying DVEC and CCC models. In-sample and out-of-sample results for one, two and five period ahead are reported. The futures used in thesis are RTS index, EUR/RUB exchange rate and Brent oil, traded in Futures and options on RTS.(FORTS) For in-sample period, data points were acquired from start of trading of each futures contract, RTS index from August 2005, EUR/RUB exchange rate March 2009 and Brent oil October 2008, lasting till end of May 2011. Out-of-sample period covers start of June 2011, till end of December 2011. Our results indicate that all three asset pairs, spot and futures, are cointegrated. We found RTS index futures to be unbiased predictor of spot price, mixed evidence for exchange rate, and for Brent oil futures unbiasedness was not supported. Weak exogeneity results for all pairs indicated spot price to lead in price discovery process. Prediction hypothesis, unbiasedness and weak exogeneity of futures, was rejected for all asset pairs. Variance reduction results varied between assets, in-sample in range of 40-85 percent and out-of sample in range of 40-96 percent. Differences between models were found small, except for Brent oil in which OLS clearly dominated. Out-of-sample results indicated exceptionally high variance reduction for RTS index, approximately 95 percent.

Monte Carlo Hypothesis Testing with The Sharpe Ratio

The purpose of this master thesis was to perform simulations that involve use of random number while testing hypotheses especially on two samples populations being compared weather by their means, variances or Sharpe ratios. Specifically, we simulated some well known distributions by Matlab and check out the accuracy of an hypothesis testing. Furthermore, we went deeper and check what could happen once the bootstrapping method as described by Effrons is applied on the simulated data. In addition to that, one well known RobustSharpe hypothesis testing stated in the paper of Ledoit and Wolf was applied to measure the statistical significance performance between two investment founds basing on testing weather there is a statistically significant difference between their Sharpe Ratios or not. We collected many literatures about our topic and perform by Matlab many simulated random numbers as possible to put out our purpose; As results we come out with a good understanding that testing are not always accurate; for instance while testing weather two normal distributed random vectors come from the same normal distribution. The Jacque-Berra test for normality showed that for the normal random vector r1 and r2, only 94,7% and 95,7% respectively are coming from normal distribution in contrast 5,3% and 4,3% failed to shown the truth already known; but when we introduce the bootstrapping methods by Effrons while estimating pvalues where the hypothesis decision is based, the accuracy of the test was 100% successful. From the above results the reports showed that bootstrapping methods while testing or estimating some statistics should always considered because at most cases the outcome are accurate and errors are minimized in the computation. Also the RobustSharpe test which is known to use one of the bootstrapping methods, studentised one, were applied first on different simulated data including distribution of many kind and different shape secondly, on real data, Hedge and Mutual funds. The test performed quite well to agree with the existence of statistical significance difference between their Sharpe ratios as described in the paper of Ledoit andWolf.