Momentum is the best-known anomaly in equities. It says that past winners (losers) will continue to have strong (weak) returns in the future. But does that anomaly also work in mutual funds? Academic research confirms it and shows it could be a good way to pick the future best performing mutual funds.
Several measures of momentum could be used – 1 year high in fund net asset value, simple momentum or fund sensitivity to momentum factor (momentum load). All three components contain significant, independent information about future fund performance (and have approximately zero correlation); therefore, all of them could be probably used altogether to obtain even greater performance. However, no such combined strategy has been investigated in source paper; therefore, we present the strongest predictor among three – simple price momentum.
Academic research suggests that it seems unlikely that investors are identifying skilled managers, but rather, they are crudely chasing performance. Equity mutual funds usually differ by the style employed by their managers. Therefore mutual fund investors are in reality picking different management styles and academic research suggests there is strong momentum effect between equity styles (see „Style Rotation Effect“) and momentum effect in market anomalies (see „Momentum effect in anomalies/trading systems“) therefore it appears that this performance chasing has strong fundamental reasons for functionality.
Confidence in anomaly's validity
Backtest period from source paper
Notes to Confidence in Anomaly's Validity
Period of Rebalancing
Notes to Indicative Performance
per annum, annualized (geometrically) 1 month performance 1,46% of top 6-month momentum decile of No-Load funds from table V
Notes to Period of Rebalancing
Number of Traded Instruments
Notes to Estimated Volatility
calculated from t-statistic from table V
Notes to Number of Traded Instruments
Notes to Maximum drawdown
Notes to Complexity Evaluation
Simple trading strategy
The investment universe consists of equity funds from the CRSP Mutual Fund database. This universe is then shrunk to no-load funds (to remove entrance fees). Investors then sort mutual funds based on their past 6-month return and divide them into deciles. The top decile of mutual funds is then picked into an investment portfolio (equally weighted), and funds are held for three months. Other measures of momentum could also be used in sorting (fund’s closeness to 1 year high in NAV and momentum factor loading), and it is highly probable that the combined predictor would have even better results than only the simple 6-month momentum.
Hedge for stocks during bear markets
No - A fund momentum strategy is implemented in a long-only variant, which means that it is not suitable to hedge equity market risk.
Sapp: The 52-Week High, Momentum, and Predicting Mutual Fund Returns
The 52-week high share price has been shown by George and Hwang (2004) to carry significant predictive ability for individual stock returns, dominating other common momentum-based trading strategies. This study examines the performance of trading strategies for mutual funds based on (1) an analogous 1-year high measure for the net asset value of fund shares, (2) prior extreme returns and (3) fund sensitivity to stock return momentum. All three measures have significant, independent, predictive ability for fund returns. Further, each produces a distinctive pattern in momentum profits, whether measured in raw or risk-adjusted returns, with profits from momentum loading being the least transitory. Nearness to the 1-year high and recent extreme returns are significant predictors of fund monthly cash flows, whereas fund momentum loading is not.
Sapp, Tiwari: Stock Return Momentum and Investor Fund Choice
Recent research by Gruber (1996) and Zheng (1999) finds that investors are able to predict mutual fund performance and invest accordingly. This phenomenon has been dubbed the “smart money” effect. We show that the smart money effect is explained by stock return momentum at the one year horizon. This finding then begs the question of what exactly investors seem to be chasing —momentum styles or recent large returns? Further evidence suggests investors do not select funds based on a momentum investing style, but rather simply chase funds that were recent winners. Thus, our finding that a common factor in stock returns explains the smart money effect offers no affirmation of investor fund selection ability. We also investigate the profitability of a pure momentum style strategy that invests solely in no-load equity mutual funds. We show that a quarterly fund selection strategy of investing in the top decile portfolio of no-load funds ranked by their historical momentum exposure yields an annualized 3-factor alpha of 3.72% over the period 1973-2000.
Friesen, Nguyen: The Economic Impact of Mutual Fund Investor Behaviors
This study analyzes how the determinants of mutual fund investor cash flows have changed over time, and the associated impact on investor returns. Using data from 1992-2016 we find that investor return-chasing behavior essentially disappeared starting in 2011. Investor flows have become more sensitive to expenses, past risk and alpha. Investors are paying more attention to fund characteristics that matter (e.g. risk, alpha and expenses), and less attention to characteristics that don’t (e.g. past returns). Nevertheless, the average investor dollar-weighted return is about 1.2% below the average buy-and-hold return in their underlying mutual fund nearly every year in our sample, suggesting consistently poor timing ability over the entire period. We decompose the economic impact of investor behaviors on investor returns and find that investors’ focus on alpha is actually more detrimental than their previous focus on past returns. Investors do benefit from choosing high-alpha funds (smart money), but poorly time their cash flows by investing in those funds after periods with the highest realized alphas (dumb money). The dumb money effect dominates the smart money effect for the simple reason that at the fund level, past alphas are strongly and negatively correlated with future alphas. Although past alphas are positively correlated to future alphas in the pooled cross-section of mutual fund data, this result does not hold at the individual fund level, which is the level where most mutual fund customers invest. Overall, our results suggest that mutual fund investors know that alpha is important, but have not yet learned how to effectively integrate this knowledge into their investment decisions.