scholarly journals On The Persistence Of Selectivity And Market Timing Skills In Hedge Funds

2013 ◽  
Vol 12 (12) ◽  
pp. 1575
Author(s):  
John Muteba Mwamba
Keyword(s):  
Hedge Funds ◽  
Market Timing ◽  
Hedge Fund ◽  
Efficient Market Hypothesis ◽  
Investment Strategy ◽  
Chi Square ◽  
Cross Sectional ◽  
Data Set ◽  
Fund Managers ◽  
Limited Arbitrage

This paper investigates the persistence of hedge fund managers skills during periods of boom and/or recession. We consider a data set of monthly investment strategy indices published by Hedge Fund Research group. The data set spans from January 1995 to June 2010. We divide this sample period into four overlapping sub-sample periods that contain different economic cycles. We define a skilled manager as a manager who can outperform the market consistently during two consecutive sub-sample periods. We first estimate outperformance, selectivity and market timing skills using both linear and quadratic Capital Asset Pricing Model-CAPM. Persistence in performance is carried out in three different fashions: contingence table, chi-square test and cross-sectional auto-regression technique. The results show that fund managers have the skills to outperform the market during periods of positive economic growth only. This market outperformance is due to both selectivity and market timing skills. These results contradict the Efficient Market Hypothesis-EMH due to limited arbitrage opportunity.

scholarly journals The Effect Of Probability And Uncertainty Models On Hedge Fund Performance Analysis

2014 ◽  
Vol 30 (5) ◽  
pp. 1339
Author(s):  
John Muteba Mwamba
Keyword(s):  
Hedge Funds ◽  
Rational Expectations ◽  
Market Timing ◽  
Hedge Fund ◽  
Efficient Market Hypothesis ◽  
Fund Performance ◽  
Data Set ◽  
Investment Style ◽  
Performance Skills ◽  
Hedge Fund Performance

<p>This paper implements two types of framework to investigate the outperformance, selectivity, and market timing skills in hedge funds: uncertainty and probability. Using the uncertainty framework, the paper develops an uncertain fuzzy credibility regression model in the form of a linear and quadratic CAPM in order to estimate these performance skills. Using the probability framework the paper implements frequentist and Bayesian CAPMs (linear and quadratic) to estimate the same performance skills. We consider a data set of monthly investment style indices published by Hedge Fund Research group. The data set extends from January 1995 to June 2010. We divide this sample period into four overlapping sub-sample periods that contain different market trends. Using the probability framework, our results show that bounded rationality triggers inefficiencies in the market that fund managers can utilise to outperform the market. This market outperformance is due to selectivity and market timing skill during periods of economic recovery only. We admit that these results contradict the rational expectations model. However, with the uncertainty framework this effect disappears on behalf of the rational expectations model and the efficient market hypothesis. This disappearance may be a result of the increased amount of high frequency trading witnessed recently that has made market inefficiencies, which are the main source of hedge fund performance, rarer.</p>

scholarly journals Return Prediction Based on Discriminating market-styles with Reinforcement Learning

2021 ◽  
Vol 15 ◽  
pp. 782-791
Author(s):  
Zhiguo Bao ◽  
Shuyu Wang
Keyword(s):  
Reinforcement Learning ◽  
Prediction Model ◽  
Nonlinear Model ◽  
Hedge Funds ◽  
Hedge Fund ◽  
Investment Strategy ◽  
Data Sets ◽  
Full Data ◽  
Data Set ◽  
Fitting Ability

For hedge funds, return prediction has always been a fundamental and important problem. Usually, a good return prediction model directly determines the performance of a quantitative investment strategy. However, the performance of the model will be influenced by the market-style. Even the models trained through the same data set, their performance is different in different market-styles. Traditional methods hope to train a universal linear or nonlinear model on the data set to cope with different market-styles. However, the linear model has limited fitting ability and is insufficient to deal with hundreds of features in the hedge fund features pool. The nonlinear model has a risk to be over-fitting. Simultaneously, changes in market-style will make certain features valid or invalid, and a traditional linear or nonlinear model is not sufficient to deal with this situation. This thesis proposes a method based on Reinforcement Learning that automatically discriminates market-styles and automatically selects the model that best fits the current market-style from sub-models pre-trained with different categories of features to predict the return of stocks. Compared with the traditional method that training return prediction model directly through the full data sets, the experiment shows that the proposed method has a better performance, which has a higher Sharpe ratio and annualized return.

Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry

2016 ◽  
Vol 51 (6) ◽  
pp. 1991-2013 ◽  
Author(s):  
David M. Smith ◽  
Na Wang ◽  
Ying Wang ◽  
Edward J. Zychowicz
Keyword(s):  
Hedge Funds ◽  
Lower Risk ◽  
Market Timing ◽  
Hedge Fund ◽  
Technical Analysis ◽  
Fund Managers ◽  
Short Sale ◽  
Timing Ability ◽  
Fund Industry

This article presents a unique test of the effectiveness of technical analysis in different sentiment environments by focusing on its usage by perhaps the most sophisticated and astute investors, namely, hedge fund managers. We document that during high-sentiment periods, hedge funds using technical analysis exhibit higher performance, lower risk, and superior market-timing ability than nonusers. The advantages of using technical analysis disappear or even reverse in low-sentiment periods. Our findings are consistent with the view that technical analysis is relatively more useful in high-sentiment periods with larger mispricing, which cannot be fully exploited by arbitrage activities because of short-sale impediments.

scholarly journals HEDGE FUNDS: RISK AND PERFORMANCE

2018 ◽  
Vol 06 (01) ◽  
pp. 1850003
Author(s):  
SANGHEON SHIN ◽  
JAN SMOLARSKI ◽  
GÖKÇE SOYDEMIR
Keyword(s):  
Risk Factors ◽  
Hedge Funds ◽  
Factor Model ◽  
Explanatory Power ◽  
Hedge Fund ◽  
Investment Strategy ◽  
Time Varying ◽  
Asset Class ◽  
And Performance ◽  
Fund Portfolio

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.

Determinants of Hedge Fund Internal Controls and Fees

2010 ◽  
Vol 85 (6) ◽  
pp. 1887-1919 ◽  
Author(s):  
Gavin Cassar ◽  
Joseph Gerakos
Keyword(s):  
Hedge Funds ◽  
Agency Costs ◽  
Hedge Fund ◽  
Short Selling ◽  
Internal Controls ◽  
Fund Managers ◽  
Asset Value ◽  
Net Asset Value ◽  
Financial Misstatements ◽  
Financial Misstatement

ABSTRACT: We investigate the determinants of hedge fund internal controls and their association with the fees that funds charge investors. Hedge funds are subject to minimal regulation. Hence, hedge fund managers voluntarily implement internal controls, and managers and investors freely contract on fees. We find that internal controls are stronger in funds with higher potential agency costs. Further, internal controls are stronger in funds domiciled in jurisdictions that provide investors with limited legal redress for fraud and financial misstatements. Short selling funds, however, are more likely to protect information about their investment positions by implementing weaker internal controls. With respect to fees, we find that the percentage of positive profits that the manager receives increases in the strength of the fund’s internal controls. Finally, removing the manager from setting and reporting the fund’s official net asset value, along with reputational incentives and monitoring by leverage providers, are all associated with lower likelihoods of future regulatory investigations of fraud and/or financial misstatement.

Portraits of five hedge fund fraud cases

2008 ◽  
Vol 15 (2) ◽  
pp. 179-213 ◽  
Author(s):  
Majed R. Muhtaseb ◽  
Chun Chun “Sylvia” Yang
Keyword(s):  
Asset Management ◽  
Hedge Funds ◽  
Hedge Fund ◽  
Investment Fund ◽  
Good Opportunity ◽  
Content Type ◽  
Fund Managers ◽  
Management Group ◽  
Fund Industry ◽  
Asset Managers

PurposeThe purpose of this paper is two fold: educate investors about hedge fund managers' activities prior to the fraud recognition by the authorities and to help investors and other stakeholders in the hedge fund industry identify red flags before fraud is actually committed.Design/methodology/approachThe paper investigates fraud committed by the Bayou Funds, Beacon Hill Asset Management, Lancer Management Group (LMG), Lipper & Company and Maricopa investment fund. The fraud activities took place during 2000 and 2005.FindingsThe five cases alone cost the hedge fund investors more than $1.5 billion. Investors may have had a good opportunity for avoiding the irrecoverable costs of the fraud had they carefully vetted the backgrounds of the hedge fund managers and/or continuously monitored the funds activities, especially during turbulent market environments.Originality/valueThis is the first research paper to identify and extensively investigate fraud committed by hedge funds. In spite of the size of the hedge fund industry and relatively substantial level and inevitably recurring fraud, academic journals are to yet address this issue. The paper is of great value to hedge funds and their individual and institutional investors, asset managers, financial advisers and regulators.

Style and Skill: Hedge Funds, Mutual Funds, and Momentum

2020 ◽  
Vol 66 (12) ◽  
pp. 5505-5531 ◽  
Author(s):  
Mark Grinblatt ◽  
Gergana Jostova ◽  
Lubomir Petrasek ◽  
Alexander Philipov
Keyword(s):  
Mutual Funds ◽  
Hedge Funds ◽  
Mutual Fund ◽  
Hedge Fund ◽  
Fund Performance ◽  
Short Sales ◽  
Fund Managers ◽  
Liquidity Provision ◽  
Stock Picking ◽  
Hedge Fund Performance

Classifying mandatory 13F stockholding filings by manager type reveals that hedge fund strategies are mostly contrarian, and mutual fund strategies are largely trend following. The only institutional performers—the two thirds of hedge fund managers that are contrarian—earn alpha of 2.4% per year. Contrarian hedge fund managers tend to trade profitably with all other manager types, especially when purchasing stocks from momentum-oriented hedge and mutual fund managers. Superior contrarian hedge fund performance exhibits persistence and stems from stock-picking ability rather than liquidity provision. Aggregate short sales further support these conclusions about the style and skill of various fund manager types. This paper was accepted by Tyler Shumway, finance.

Are hedge fund returns affected by media coverage of macroeconomic news?

2019 ◽  
Vol 36 (3) ◽  
pp. 427-439
Author(s):  
Sandip Dutta ◽  
James Thorson
Keyword(s):  
Hedge Funds ◽  
Media Coverage ◽  
Hedge Fund ◽  
Real Sector ◽  
Macroeconomic News ◽  
Content Type ◽  
Fund Managers ◽  
News Announcements ◽  
Hedge Fund Returns ◽  
The Impact

Purpose Extant literature suggests that the difficulty associated with the interpretation of macroeconomic news announcements by the market in general in different economic environments, might be the reason why most studies do not find any significant relationship between real-sector macroeconomic variables and financial asset returns. This paper aims to use a different approach to measure macroeconomic news. The objective is to examine if a different measure of a macroeconomic news variable, constructed from media coverage of the same, significantly affects hedge fund returns. Design/methodology/approach The authors use a news index for unemployment, which is a real-sector variable, constructed from newspaper coverage of unemployment announcements and examine its impact on hedge fund returns. Findings Contrary to the other studies that examine the impact of macroeconomic news on hedge fund returns, the authors find that media coverage of unemployment news announcements significantly affects hedge fund returns. Practical implications Overall, this paper demonstrates that the manner in which the market interprets macroeconomic news announcements in different economic environments is probably a more relevant factor for hedge funds and is more likely to impact hedge fund returns. In conjunction with variables – constructed from media coverage of unemployment news announcements – that factor in the manner of interpretation, it is found that surprises also matter for hedge fund returns. This is an important consideration for hedge fund managers as well. Originality/value To the best of the authors’ knowledge, this is the first study that examines the impact of media coverage of macroeconomic news announcements on hedge fund returns and finds significantly different results with real-sector macro variables.

Hedge Funds: The Good, the Bad, and the Lucky

2017 ◽  
Vol 52 (3) ◽  
pp. 1081-1109 ◽  
Author(s):  
Yong Chen ◽  
Michael Cliff ◽  
Haibei Zhao
Keyword(s):  
Hedge Funds ◽  
Expectation Maximization ◽  
Investment Strategy ◽  
Performance Measure ◽  
Cross Sectional ◽  
Normal Distributions ◽  
Out Of Sample ◽  
Mixture Of Normal Distributions ◽  
Skill Groups ◽  
The Mean

We develop an estimation approach based on a modified expectation-maximization (EM) algorithm and a mixture of normal distributions associated with skill groups to assess performance in hedge funds. By allowing luck to affect both skilled and unskilled funds, we estimate the number of skill groups, the fraction of funds from each group, and the mean and variability of skill within each group. For each individual fund, we propose a performance measure combining the fund’s estimated alpha with the cross-sectional distribution of fund skill. In out-of-sample tests, an investment strategy using our performance measure outperforms those using estimated alpha and t-statistic.

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