Abstract
This paper adapts two recent developments from the bibliometric literature to the problem of assessing the return performance of a financial asset. The result is a quantity-of-quality metric, which is both nonparametric and moment-free. As such, it offers a nonstandard perspective on the informational patterns in asset returns, and accordingly can complement traditional moment-based asset evaluation methods. The proposed approach is simple to apply, and while moment-free, captures intuitively important aspects of asset performance such as location, upside potential, downside risk, and volatility. It can also be expressed as a reward-to-risk ratio, which serves as a counterpart to the Sharpe ratio. Empirical and simulation results suggest that, relative to the Sharpe ratio, the proposed approach prefers assets with moderately higher means and standard deviations, and more favorable skewness.
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Notes
Dozens of refinements and generalizations of the h-index now exist (e.g., Bornmann et al. 2011), among them the g-index and the e-index (Zhang 2009). Numerous empirical studies of the h-index have also appeared (e.g., Cronin and Meho 2006; Haley 2013), as well as studies regarding its variability across citation databases (Henzinger et al. 2009; Haley 2014b).
This “history” is often truncated to a specific set of recent years; the exact interval varies by application.
The h-index is also commonly computed for individual scholars. In the scholar-level case, the ordered pairs are likewise (article rank, article citation count), but the universe of articles would be those published by a specific scholar across a variety of journals.
In cases where the sample size n is 100–200, the 45-degree line works well. In cases where the sample size is much larger or much smaller, the user might consider rescaling the slope by 100/n. This is a simple generalization that is akin to expressing the rank as percentile rank.
It is, of course, to be expect that the SR will not value skewness as it is only includes mean and standard deviation.
Perturbations of these moments were also explored, which yielded qualitatively similar results.
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Haley, M.R. A nonparametric quantity-of-quality approach to assessing financial asset return performance. Ann Finance 14, 343–351 (2018). https://doi.org/10.1007/s10436-018-0319-2
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DOI: https://doi.org/10.1007/s10436-018-0319-2