Prior work documents a strong positive relationship between mutual fund flows and a variety of past performance measures, including market-adjusted returns and alphas based on different factor models. This literature does not, however, address which of the performance measures is the best predictor of flows.

Findings

Barber, Huang and Odean (2016) estimate mutual fund alphas using six competing empirical models of managerial skill: market-adjusted returns, the capital asset pricing model, the Fama and French (1993) three-factor model, a four-factor model that adds momentum (Carhart 1997), a seven-factor model that adds the three industry factors of Pástor, and Stambaugh (2002a, 2002b), and a nine-factor model that adds profitability and investment factors (Fama and French 2015).

They then study how these alphas affect fund flows. In simple linear regressions of fund flows on the six performance measures, they find that the partial effect of CAPM alpha on fund flows is roughly double that of its nearest competitor (market-adjusted returns). In their further tests using pair-wise comparison, they find greater flows to mutual funds with higher ranks based on CAPM alpha than to funds with higher ranks based on competing models.

Agarwal, Green, and Ren (2018) also find that the CAPM alpha consistently wins a model horse race in predicting hedge fund flows. Berk and Van Binsbergen (2016) also reach a similar conclusion: fund flows are best explained by CAPM alphas than by competing models.

Alpha from CAPM is the most important return determinant of fund flow.

However, what about risk?

Barber et al (2016) further decompose the returns of a fund into eight components: seven factor-related returns (market (beta), size, value, momentum, and three industry factors) and the fund’s alpha, all estimated using a seven-factor model. They find that returns related to a fund’s beta do not generate the same flows as the fund's alpha or other factor-related returns.

This suggests investors do not reward fund managers for returns attributable to a fund's beta.

Analysis and Implications

Here, we illustrate the effect of alpha improvement on fund flow in the UK equity mutual fund industry. We select open-end equity funds that have a UK geographical focus from the fund universe which produces 857 funds.

The Table below reports the annualized return and CAPM alpha of the funds over the last 5-year period. For comparison, we also report the statistics for the SMART-X UK portfolio. We can see the median alphas of these funds are close to zero with 0.43% being earned annually. By contrast, the SMART-X UK portfolio outperforms even the top 1% of funds. We report the alpha improvement in column 3. For example, the Smart-X UK portfolio offers an annual alpha that is 14.93 percent above the median of the funds.

Barber et al. (2016) find that “A one percentage point increase in a fund’s CAPM alpha is associated with a 0.474 percentage point increase in monthly fund flow.” By studying the coefficients associated with CAPM alpha in the fund-flow determinant regressions in their Table 3 (P2621), we translate the improvement of alpha in column 3 into the percentage of fund-flow increase in column 4 by using this coefficient of 0.474. It shows that the SMART-X UK portfolio performance will offer a 7.08% increase of fund-flow for a median performance fund. And the increase of fund flow increased to 9.52% for the worse performance fund.

To illustrate this in value terms, we obtain the average size of the fund in each category. The average fund size reported in column 5 suggests that funds between the top 5% and 1% have the largest average size - confirming the magnet effect of performance to fund flow.

If we consider a fund in the bottom 10% of the distribution can enhance its performance with the SMART-X strategy to bring it up to a top 5% fund performance, its fund size will more than double from 412.44 to 1188.24 million during this course of improvement.

A Take Away Message

CAPM alpha is the most important preperformance measure that investors use to direct their investment. The top 5% performance funds are the big winners of fund flow.

References:

Agarwal V, Green, T. C., Ren, H. 2018. Alpha or beta in the eye of the beholder: What drives hedge fund flows? Journal of Financial Economics, 127: 417-434.

Barber, B. M., Huang, X., & Odean, T. 2016. Which factors matter to investors? Evidence from mutual fund flows. The Review of Financial Studies, 29(10), 2600-2642.

Berk, J. B., and J. H. Van Binsbergen. 2016. Assessing asset pricing models using revealed preference. Journal of Financial Economics 119: 1–23.

Carhart, M. M. 1997. On persistence in mutual fund performance. Journal of Finance, 52:57–82.

Fama, E., and K. R. French. 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33:3–56.

———. 2015. A five-factor asset pricing model. Journal of Financial Economics, 116:1–22.

Pástor, L., and R. F. Stambaugh. 2002a. Investing in equity mutual funds. Journal of Financial Economics, 63:351–80.

———. 2002b. Mutual fund performance and seemingly unrelated assets. Journal of Financial Economics, 63:315–49.

Can Anomaly Returns be Enhanced by more Dynamic Rebalancing?

Review and replication of “Anomalies Enhanced: Don’t be passive as information arrives” by Han, Huang and Zhou (2018)

Background

One major stream of ‘factor’ investment is based on anomaly investing. An anomaly is a characteristic that enables abnormal returns to be earned by using publicly available information – thus violating the efficient market hypothesis.

Many anomaly strategies are based on ﬁrm characteristics and are rebalanced yearly. This normally works well as most of the anomalies take time to deliver and one year is normally long enough to see the benefit of investing in mispriced stocks. However, annual rebalancing, normally at the end of June, ignores any information during the year that may be useful in improving performance or reducing the potential downside.

The Question

Can we use information during the year to enhance the performance of anomaly factor investment strategies? If yes, how? Han, Huang and Zhou (2018) take on this challenge.

Research design and findings

Han et al. (2018) study eight major anomalies including the book-to-market ratio, operating profit, gross profitability, asset growth, investment growth, net stock issue, the accrual anomaly, and the net operating assets anomaly. In searching for enhancement, they provide dynamic trading strategies to rebalance the anomaly portfolios monthly.

Specifically, they test three dynamic strategies: a volatility-managed portfolio approach, an alpha-managed approach by using CAPM alpha, and a trend-managed approach by using technical analysis. They find that the trend-managed strategies perform the best, earning on average 1.23% per month across the eight anomalies which doubles the original anomaly return.

Therefore, the answer to the first question is yes; and this suggests that many well-known anomalies are more proﬁtable than previously thought, yielding new challenges for their theoretical explanations.

For the second question, here is how they perform the trend-managed anomaly investment. Every month, they compute and compare a stock’s short- and long-term performance by its moving average prices (MAs) over the past 50 and 200 days, respectively. If the 50-day MA price is greater than the 200-day MA price, i,e., the short-term trend/expected return is above the long-term trend, they regard the stock as performing well and keep it in the long leg of an anomaly, and they sell it otherwise. They do the opposite for the short leg. This is basically a simple MACD strategy.

Where is the information coming from? They find that technical analysis helps to reduce information uncertainty. Using the trend-managed approach is especially effective for firms with high information uncertainty measured by idiosyncratic volatility, firm age and the number of analysts following.

Whether the extra returns from the enhanced anomalies disappear after accounting for transaction costs is an important question, because rebalancing monthly requires more trading than the original anomalies. They show that liquidity and transaction costs will not affect this benefit significantly.

The takeaway message for this article is that technical analysis is surprisingly related to fundamental analysis. As their title suggests, don’t ignore information when it arrives. That is why our Smart-X strategy is rebalanced monthly.

Replication

We provide a simple replication of one anomaly (the book to market ratio) to demonstrate the power of this approach and we test a long-only strategy. The original BM strategy invests in 50 stocks that have the highest book to market ratio at the point of sorting and hold for one year. We ‘enhanced’ this approach by using the MACD rule on a monthly basis which is similar to that proposed by Han et al. (2018) with a short-leg of 5 days and a long-leg of 30 days.

The total returns of the original and enhanced BM strategies are plotted in the following figure with the S&P 500 index.

We can see that while the BM value strategy earns an excellent active (comparing to S&P 500) return of 5.48% over the 20 year period, the enhanced BM strategies earn 2.73% more per year. The differences become much more impressive when they are accumulated; the total return of the enhanced BM strategy is 1.8 times of that of the original one. A similar improvement is observed in the Jensen Alphas. Other statistics suggest that the enhanced BM improves performance with more ‘active’ management and this is demonstrated by a lower beta and correlation with the market portfolio.

Reference

Han, Yufeng and Huang, Dayong and Zhou, Guofu, Anomalies Enhanced: Don’t be passive as information arrives (March 27, 2018). Available at SSRN: https://ssrn.com/abstract=2624650 or http://dx.doi.org/10.2139/ssrn.2624650

Further Reading

Which Factors Matter When Investors Select a Mutual Fund?

Can Anomaly Returns be Enhanced by more Dynamic Rebalancing?