A new performance measure—active return consistency—can assess the strength and consistency of a fund’s performance against a chosen benchmark. The author finds that for shorter periods, return ranks tend to be volatile and biased toward the median performance, especially for time periods of 12 months and less.
In his investigation of performance measures, the author carries forward work done by Philips, Yashchin, and Stein (working paper 2003) by using active one-month ranks instead of information ratios, which improves the accuracy of the results. He derives a new performance measure of active return consistency that focuses on the strength and consistency of a fund’s performance against a chosen benchmark. Additionally, he proposes that longer-term datasets be used for reliability of results because shorter periods, especially 12 months and less, lead to biased results. The resultant ranking of performance determines outperforming funds.
How Is This Research Useful to Practitioners?
Investment managers are always looking for measurement benchmarks that can better identify outperforming funds for investment. The author proposes a new measure (i.e., active return consistency using a ranking approach) that promises to identify outperformers in a given dataset. Measuring outperformance against median peer performance is a concept that has been used for benchmarking in the past. The author outlines his ranking approach and emphasizes a minimum dataset to ensure validity of results. He also dispels the myth that a fund’s short-term rank being higher than its long-term rank is a sign of improvement on the pretext of data bias in smaller datasets. Investment managers can also use this tool as an alternative and more informative marketing tool for their own funds.
How Did the Author Conduct This Research?
The author’s data are from the Morningstar Direct database between 1 March 2004 and 28 February 2014. He derives monthly returns gross of fees for the oldest share class of all actively managed funds for US intermediate-term bond and US large-cap blend equity mutual funds. He then determines the cross-sectional median return for each month to serve as a benchmark. Any funds with fewer than 120 months of performance are excluded. The final sample comprises two sets of 105 large-cap blend equity funds and 99 intermediate-term bond funds.
The author proposes a new information ratio to measure the performance of active returns. Accordingly, the 10 highest and 10 lowest funds are selected for active return consistency.
By linking information ratios to monthly active ranks, the author carries the research of Philips, Yashchin, and Stein forward. They proposed monitoring the CUSUM (cumulative sum control chart) of monthly risk-adjusted returns—that is, information ratios. CUSUM is a sequential analysis technique used for monitoring change detection.
This research is useful in the sense that exploring new ways to assess performance is critical in order for fund managers to outperform their peers.