Hedge Fund Investing

The results of recent replication studies suggest that false positive findings are a big problem in empirical finance. We contribute to this debate by reviewing a sample of articles dealing with the short-term directional forecasting of the prices of stocks, commodities, and currencies. Screening all relevant articles published in 2016 by one of the 96 journals covered by the Social Sciences Citation Index in the category "Business, Finance," we select only those studies that use easily accessible data of daily or higher frequency. We examine each study in detail, from the selection of the dataset to the interpretation of the results. We also include empirical analyses to illustrate the shortcomings of certain approaches. There are three main findings from our review. First, the number of selected papers is very low, which is surprising even when the strict selection criteria are taken into account. Second, there are hardly any relevant studies that use high-frequency data-despite the hype about financial big data and machine learning. Third, the economic significance of the findings-for example, their usefulness for trading purposes-is questionable. In general, apparently good forecasting performance does not translate into profitability once realistic transaction costs and the effect of data snooping are taken into account. Other typical problems include unsuitable benchmarks, short evaluation periods, and nonoperational trading strategies.

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Evaluation of current research on stock return predictability

Reschenhofer

Mangat

Zwatz

et al. 2019

Journal of Forecasting

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An Analysis of the Risk‐Return Characteristics of Serially Correlated Managed Futures

Elaut

Erdős²,

Sjödin

2016

Journal of Futures Markets

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We investigate the implications of low but persistent serial correlation in Managed Futures' returns for portfolio management. Using a measure based on the unweighted sum of autocorrelations, we find that more positively autocorrelated Managed Futures exhibit distinctly different risk‐return profiles and outperform, on a risk‐adjusted basis, Managed Futures that exhibit lower degrees of serial correlation. The observed premium is unlikely to be explained by a concentration in certain strategies, fund size and age, attrition or delisting bias, and does not seem to hamper Managed Futures' portfolio benefits as a tail‐risk hedge. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 36:992–1013, 2016

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Do mutual funds have consistency in their performance?

et al. 2019

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Using a comprehensive data set of 714 Chinese mutual funds from 2004 to 2015, the study investigates these funds' performance persistence by using the Capital Asset Pricing model, the Fama-French three-factor model and the Carhart Four-factor model. For persistence analysis, we categorize mutual funds into eight octiles based on their one year lagged performance and then observe their performance for the subsequent 12 months. We also apply Cross-Product Ratio technique to assess the performance persistence in these Chinese funds. The study finds no significant evidence of persistence in the performance of the mutual funds. Winner (loser) funds do not continue to be winner (loser) funds in the subsequent time period. These findings suggest that future performance of funds cannot be predicted based on their past performance.

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Hedge Fund Investing

Cited by 33 publications

References 6 publications

Evaluation of current research on stock return predictability

Evaluation of current research on stock return predictability

An Analysis of the Risk‐Return Characteristics of Serially Correlated Managed Futures

Do mutual funds have consistency in their performance?

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