1. This is the third of four articles in the “Alice in Factorland” series. In the first article, we showed that factor returns are routinely not captured by active managers. Particularly, mutual funds capture only about half of the value premium implied by the theoretical paper portfolios and, surprisingly, almost none of the momentum premium. In the second article, we showed that even though factor models are useful in understanding the performance drivers of smart beta strategies, attempting to replicate smart beta strategies with factors delivers worse returns, with far higher turnover and trading costs, and far lower capacity. For smart beta strategies to qualify as “smart,” practical considerations are important. In our next (fourth) article of the series, we will take a deeper dive into momentum. We have seen that active managers are not able to capture the momentum premium. Worse, “standard momentum” hasn’t paid off in US large stocks since 2001. Can momentum be saved as a factor? Yes, but the strategy’s popularity may already exceed its capacity.
2. Except when we explicitly refer to CAPM or Fama–French alpha, we use the word “alpha” to denote excess return over the capitalization-weighted benchmark or the return of a long–short portfolio.
3. Goyal and Wahal (2008), among others, document that disappointing one-, two-, and three-year prior performance is strongly related to the likelihood of a fund manager being fired by an institutional plan sponsor. Goyal and Wahal also show that institutional investors tend to hire fund managers that have recently outperformed their benchmarks. To any practitioner, these findings are no surprise.
4. We quote numbers comparing the cumulative wealth generated by Russell 1000 and Russell 1000 Value comparing the three-year period up to February 2000 and the three- and five-year periods starting from March 2000.
5. Kinnel (2005, 2014, 2015, 2016) and Hsu, Myers, and Whitby (2016) demonstrate that an investor’s time-weighted return is significantly lower than their dollar-weighted return. This performance gap shows that investors, on average, have a lower return due to their own timing decisions in allocating among funds. We conjecture that trend chasing is a likely culprit.
6. Academic literature on manager skill is highly nuanced and perhaps agrees only on the point that if skill exists, it is hard to identify. Early work by Sharpe (1966) and Jensen (1968) find no evidence for persistence in the average manager’s performance. Hendricks et al. (1993) find some evidence for persistence in manager performance, after controlling for the three Fama–French factor exposures. Carhart (1997) shows that performance persistence disappears when, in addition to the Fama–French three factors, the study controls for momentum. More recently, Kosowski et al. (2006) and Kosowski, Naik, and Teo (2007) find evidence of some persistence in skill when the study controls for multiple factors and adjusts for other aspects of manager performance, such as non-normality of return. Even with this small level of skill persistence, Berk and Green (2004) argue that, in equilibrium, active managers should consume most of the benefits of skill in terms of higher fees, and very little benefits would flow to investors. Harvey and Liu (2017) show that the lack of predictability of returns appears because of significant noise in the historical alphas. Pooling information across funds can make alpha forecasts more accurate.
7. We focus on institutional, no-load, and A-share classes because they are the most relevant to retail and institutional investors. These three classes differ in their fee structures and represent investment returns to different types of investors. Inclusion of all three share classes enriches the sample.
8. Given the small number of unique funds before the 1990s, we exclude from our sample all observations before 1990.
9. Fund expense information is provided in the data on an annual basis. Many new funds lack expense information until the subsequent year after they first appear in the data, which explains the sawtooth pattern of the percentage of funds without fee data.
10. Awareness of this truism has sown the seeds of somewhat of an obsession in the industry about fees. Well over a century ago, Bastiat wrote about the seen and the unseen in economics. Fees offer a vivid example. Investors who scrap and claw to save a few basis points in fees will cheerfully ignore 100 bps (or more!) in unseen trading costs or will cheerfully pay “two plus twenty” to gain access to a “brilliant” hedge fund manager (i.e., brilliant past returns). The Smart Beta Interactive tool on the Research Affiliates website illustrates the enormous differences in trading costs among strategies.
11. For details on the Fama–French five-factor model, see Fama and French (2015), which is an extended version of the very influential Fama–French three-factor model introduced by Fama and French (1993). For details on the momentum factor, see Jegadeesh and Titman (1993). For details on the low beta factor, see Frazzini and Pedersen’s (2014) BAB factor.
12. To control for overlapping observations and serial correlation between funds, both of which would artificially increase t-statistics, we use the Petersen (2009) method of clustering standard errors across time periods and across funds. Using a pooled regression as the method of studying performance predictability has the following limitations: 1) when the dependent variable is the simple return, the pooled results compare performance across different time samples and cannot be directly used to differentiate between managers; and 2) pooling observations across periods introduces a look-ahead bias because investors at the beginning of the sample would not know the full distribution of past returns over the entire future sample. Bearing these limitations in mind, the pooled regression provides a simple way to study performance persistence of mean reversions at different horizons for different funds. Later in this article we show that time-series predictability of fund returns by the past return is driven to a significant degree by the time-series predictability of the equity factor return to which a fund has exposure. This look-ahead bias is present in many academic studies in which the subject of analysis is the time-series predictability of returns (for example, Campbell , Campbell and Shiller [1988, 1989], Campbell and Viceira , Campbell and Yogo , and the survey in Cochrane , etc.), and our work is not an exception.
13. These same anecdotal rules apply equally to real estate and other asset classes.
14. Chow et al. (2017) demonstrate that trading, or market impact, costs are important, yet frequently ignored, by investors in their analysis of a smart beta strategy. The authors provide estimates of trading costs for a few recently popular strategies. Strikingly, the trading costs are almost always an order of magnitude higher than the licensing costs of these strategies, and often on an order of magnitude comparable to the historical alpha of these strategies.
15. We have seen highly sophisticated institutional investors make this mistake, incurring dozens of basis points in transition costs, to shift assets to a new strategy that will incur 50 bps or more in annual trading costs, in order to trim 10 bps in annual fees. It’s an easy error to make. Hidden costs aren’t posted by funds or managers, and they can be astonishingly large!
16. For individual stocks, some of these may be zero or negative, creating problems. For portfolios, that’s rarely true, especially with five-year-smoothed financial metrics.
17. The five-year relationship is weaker for the factors and strategies with higher turnover. This is unsurprising. The momentum or low-beta portfolio one or two years hence will be very different from today’s portfolio. The near-term (one-year or one-month) predictive relationship, while obviously weak, is less sensitive to this nuance.
18. The eight factors used in the exhibit are value (defined by price-to-book ratio), value (defined by a blend of the ratios of price to book, price to five-year average earnings, price to five-year average sales, and price to five-year average dividends), size, momentum, low beta, illiquidity, profitability, and investment.
19. We chose these three factors because they were broadly known for the entire sample period of our study, whereas the investment, profitability, and low beta factors just became recognized as established factors quite recently. Further, we had a preference for a shorter list of factors, because we use monthly data to estimate the fund factor loadings; too many factors would result in a very noisy measurement of fund factor sensitivity.
20. Suppose that a forecast signal s with probability p0 is equal to the future return, r (i.e., the signal is clairvoyant). Also suppose that with probability (1 – p0) it is independent of the future return, but has the same mean and standard deviation. Then, the correlation between s and r is equal to p0. This can be demonstrated using the law of total expectations, breaking the expectation into the clairvoyant and uninformative events, and using the fact that the signal and return are perfectly correlated with probability p0 and are uncorrelated with probability (1 – p0).
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