Can a volatility-scaled trading strategy in actively managed US mutual funds improve investment performance?

Prior studies analyzed volatility-scaled trading for equities and found superior performance. The authors’ analysis of volatility-scaled strategies for equity mutual funds built on this previous research.

The authors looked at how volatility-scaled strategies for buying and selling actively managed US equity funds compared with a buy-and-hold strategy for the same funds.

1. The first strategy involved increasing the investment weight in inverse proportion to past volatility. With this strategy, low volatility meant that investors needed to use leverage (borrowed money) to increase their position in their chosen fund. The authors capped the maximum leverage at 2 to 1.
2. The second strategy involved scaling investment exposure to a fund in inverse proportion to downside volatility.
3. The authors also examined a strategy of targeting a specific level of volatility (20%) and increasing or decreasing the fund investment to achieve the desired level of volatility. They made this adjustment for total volatility and downside volatility. To analyze fund flows for evidence that investors were implementing such volatility-based strategies, the authors examined the relationship between historical fund volatility and fund flows.

To conduct their analysis, the authors used daily and monthly performance data for a sample of actively managed US equity mutual funds over the period from September 1998 through December 2019. The sample excluded funds that charged a sales load or a redemption fee. Funds were included only if they had at least $15 million in assets but were not excluded if they subsequently fell below that level. All funds needed to have at least 36 months of returns to be included in the sample. The final sample of 1,817 funds had median total net assets under management of $234 million and a mean of $838 million.

Compared with a buy-and-hold approach, all three strategies produced statistically significant superior performance. Past volatility was shown to negatively predict future returns among approximately three-fourths (76%) of the sample funds. The authors conclude, “Therefore, volatility scaling works because decreasing investment in a fund when its past volatility was high not only avoids high future volatility but also avoids low future returns.”

When volatility scaling was used, the 1-factor alpha per year after fees of the median (mean) fund jumped to 1.75% (1.19%) from –0.79% (–0.74%) without volatility scaling. The median (mean) Sharpe ratio also improved to 0.49 (0.43) from 0.4 (0.36).

The approach that used downside volatility scaling performed even better. It produced a median (mean) 1-factor alpha of 2.32% (1.59%). The median (mean) Sharpe ratio improved to 0.5 (0.44).

Similar results were also found for 3- and 4-factor alphas for both the total volatility scaling approach and the downside volatility scaling approach, but the differences were lower in magnitude than for 1-factor alpha. The differences in results between all three of the scaled approaches versus the unscaled approach were significant at the 1% level. The authors also found that a clear negative relationship between past volatility and fund flows was significant at the 1% level. “Our findings suggest that investors seem to have knowledge of the value of volatility timing,” the authors state.

The returns on actively managed equity mutual funds can be improved by scaling positions inversely with volatility or downside volatility.