Close Navigation
Learn more about IBKR accounts
Can Statistics Actually Determine if Managers Have No Skill?

Can Statistics Actually Determine if Managers Have No Skill?

Posted June 24, 2020
Elisabetta Basilico, Ph.D., CFA
Alpha Architect

The article “Can Statistics Actually Determine if Managers Have No Skill?” first appeared on Alpha Architect Blog. See an excerpt below.

  • Campbell Harvey and Yan Liu
  • Journal of Finance, 2020
  • A version of this paper can be found here
  • Want to read our summaries of academic finance papers? Check out our Academic Research Insight category

The authors of this paper seek to exhibit ways in which type I and type II errors can be measured. Additionally, these types of errors present varying levels of economic importance and cost not fully taken into account by current statistical analysis.

In the context of multiple testing, it is difficult to evaluate the Type II error rate for at least two reasons: large numbers of alternative hypotheses and the multidimensional nature of the data.

In this paper, the authors propose a different approach, to consider the importance of test power. In other words, how well can statistical tests actually identify skill if it genuinely exists. Turns out that the power of common “manager performance” tests is pretty poor. To include the often-cited paper by Fama and French, “Luck versus Skill in the Cross-Section of Mutual Fund Returns.” The TLDR is that the Fama French tests would never find much evidence for manager skill, even if the reality was that there were lots of managers with investing skills.

The authors propose the following:

  1. A simple metric to summarize the information contained in the parameters of interest and to evaluate Type I and Type II error rates. In essence, this metric reduces the dimensionality of the parameters of interest and allows us to evaluate error rates around what we consider a reasonable set of parameter values.
  2. An error rates evaluation tool using a bootstrap method, which allows investors to capture cross-sectional dependence nonparametrically. Because this method is quite flexible in terms of how it defines the severity of false positives and false negatives, it is possible to evaluate error rate definitions that are appropriate for a diverse set of finance applications.

What are the Academic Insights?

The paper proceeds with two practical applications of this framework: to select outperforming strategies (by studying two datasets, the Standard and Poor’s CAPIQ database on a broad set of 484 long-short alpha strategies, and the 18,113 anomalies studied in Yan and Zheng, 2017), and to analyze mutual fund performance (by focusing on the joint test approach used in Fama and French (2010), which treats the mutual fund population as a whole and tests whether the entire mutual fund population has zero alpha (the null hypothesis) versus at least one fund has a positive alpha).

The authors show that when the threshold t-statistic increases, the Type I error rate (the rate of false discoveries among all discoveries) declines while the Type II error rate (the rate of misses among all non discoveries) increases.

Why does it matter?

Current research on multiple testing focuses on controlling the Type I error rate, This study shows that it is also important to consider the Type II error rate. For the selection of investment strategies, a weighted average of the Type I error rate and the Type II error rate is likely more consistent with the investor’s objective function. With the advent of big data and higher computing power, it is important to try and correct data mining as much as possible. This is an attempt made by the authors of the paper.

It’s an intense paper but worth the read!

Visit Alpha Architect Blog to read the full article.

Disclosure: Alpha Architect

The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).

This site provides NO information on our value ETFs or our momentum ETFs. Please refer to this site.

Disclosure: Interactive Brokers

Information posted on IBKR Campus that is provided by third-parties does NOT constitute a recommendation that you should contract for the services of that third party. Third-party participants who contribute to IBKR Campus are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from Alpha Architect and is being posted with its permission. The views expressed in this material are solely those of the author and/or Alpha Architect and Interactive Brokers is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to buy or sell any security. It should not be construed as research or investment advice or a recommendation to buy, sell or hold any security or commodity. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

IBKR Campus Newsletters

This website uses cookies to collect usage information in order to offer a better browsing experience. By browsing this site or by clicking on the "ACCEPT COOKIES" button you accept our Cookie Policy.