External Research – Performance Measurement

We provide evidence consistent with scale diseconomies for hedge funds being related to the aggregate assets pursuing particular investment strategies. This study extends Forsberg, Gallagher, and Warren who identified skilled managers with a persistent performance by forming peer cohorts of hedge funds using return correlations. Our analysis shows fund performance had a significant negative relation with cohort size, while the relation with fund size is inconsistent across specifications but evident where funds faced limited competition. We also document a weaker relation between performance and inflows where funds faced less competition, suggesting that cohort structure might influence the propensity to accept assets. https://www.cfainstitute.org/research/financial-analysts-journal/2022/1996200

Joenvaara, Juha and Karppinen, Jari and Teo, Melvyn and Tiu, Cristian Ioan, The Vocabulary of Hedge Funds (August 17, 2019). Available at SSRN: https://ssrn.com/abstract=3438758 or http://dx.doi.org/10.2139/ssrn.3438758.

Evidence from linguistics relates lexical diversity, i.e., the propensity of the writer to employ rich vocabulary, to cognitive ability. We find that lexically diverse hedge funds outperform lexically homogeneous hedge funds after adjusting for risk. Lexically diverse funds outperform by exploiting textual changes in corporate disclosures and harnessing qualitative information about firm manager’s decisions. Data on sibling fund lexical diversity together with an instrumental variable approach and regressions address endogeneity concerns. Investors react correctly but not fully to the information on fund manager skill embedded in lexical diversity. Our results support the notion that linguistic skills are helpful for investment performance.

An different way of selecting fund managers, based on linguistic skills rather than performance data.

The fallibility of Sharpe Ratio, and all of its cousins, becomes evident upon recognizing that it relies solely on the mean and standard deviation, which, as summary statistics,cannot distinguish between processes that are symmetric and asymmetric. In order for the Sharpe ratio, and other related statistical tools, to accurately measure a risk reward profile, the underlying time series has to be symmetric.Most users of Sharpe and related tools assume this is the case. However,the standard across the vast majority of investment strategies is quite the opposite of this assumption; returns are most generally asymmetrically distributed, and particularly, negatively skewed. Moreover, erroneously assuming that an asymmetric distribution is symmetric when it is left skewed,will cause any model to dramatically understate risk.This is especially true when underlying distributions exhibit high levels of negative skewness, which lo and behold,is most often the case.Download the paper here: https://www.logicafunds.com/blog-research

An interesting paper on the difficulties comparing different strategies using a common metric, i.e. the Sharpe Ratio. The problem here is that without knowing the higher moments of a distribution, it is difficult to use the “Skill Metric” that the authors have developed.

Several other papers have come up with variations of the Sharpe ratio and how to address the shortcomings of the measurement. In the end, the Sharpe Ratio is only a model and the model is only good as long as the assumptions hold. This paper broadens out our understanding of how to apply the tool in situations when the distribution of returns is not close to normally distributed.

Common risk metrics reported in academia include volatility, skewness, and factor exposures. The maximum drawdown statistic is rarely calculated, perhaps because it is path dependent and estimated with greater uncertainty. In practice, however, asset managers and fiduciaries routinely use the drawdown statistic for fund allocation and redemption decisions. To help such decisions, we begin by quantifying the probability of hitting a certain drawdown level, given various return distribution properties. Next, we show that drawdown-based rules can be particularly useful for improving investment performance over time by detecting managers that lose their ability to outperform. This can happen as a result of structural market changes, increased competition for the type of strategy employed, staff turnover or a fund accumulating too many assets. Finally, we show that drawdown-based rules can be used as a risk reduction technique, but this impacts both expected returns and risk.Download the paper here: https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3583864 (Otto van Hemert, Mark Ganz, Campbell R. Harvey, Sandy Rattray, Eva Sanchez Martin, and Darrel Yawitch)

An in-depth study of the drawdown measure, how to turn it into useful rules, but that returns and risks are impacted. The implicit audience of the paper is a fund-of-fund manager that bases his/her decision upon predefined loss levels.

They also manage to establish that your largest drawdown is always ahead of you and that rules based on maximum drawdown should be time-sensitive rather than static. We may replicate their finding using our database.