Intelligent Selection of Smart Beta Mutual Funds

Abstract

Unlike index funds, smart beta mutual funds (SBFs) attempt to beat the market by weighting securities based on price, value, momentum, and other fundamental characteristics through careful adherence to trading rules based upon these traits. Since their debut, SBFs have attracted over a trillion dollars. This research attempts to ascertain whether SBFs beat their category averages and whether limiting the costs of investing in SBFs can enhance their performance relative to category averages. Our findings show that SBFs do not necessarily outperform the market, as measured using category averages. Return and risk are both higher, resulting in risk-adjusted performance that is very similar to the average of their respective category. However, performance can be greatly improved by selecting SBFs that charge less for their services. Limiting the sample to those SBFs with the lowest 25% of expense ratios results in even better performance.

TOPICS: Mutual fund performance, factor-based models, performance measurement