The third explanation, a model proposed by Hong and Stein (1999), observes that information is not evenly available to all market participants. Both behaviors lead to initial momentum and subsequent mean reversion in prices. In the case of biased self-attribution-when success is attributed to superior skill, but failures to bad luck-investors tend to pay attention to confirmatory signals and ignore conflicting ones, which again inspires more aggressive trading. 9 Overconfidence encourages investors to overestimate the accuracy of their insights or private information, which causes them to trade more aggressively. 8 When the initial news is followed by confirming news, the stock price adjusts in the same direction (momentum), often to the point of over-extrapolation to where the stock price is poised for mean reversion.ĭaniel, Hirshleifer, and Subrahmanyam (1998) propose a second explanation, arguing that investors overestimate precision of their private information and underestimate precision of public information as a result of biased self-attribution and overconfidence. The first article, Barberis, Shleifer, and Vishny (1998), suggests that when earnings surprises reach the market, investors do not pay them enough attention, and the stock price initially underreacts to the news. The three underlying theories do not contradict each other and each is likely to be partially responsible for the momentum effect. 7 Three articles are frequently cited as offering the best explanation of the momentum effect. The most convincing explanations for momentum lie in the behavioral realm. 5 Our understanding has been improved through the work of many researchers, in multiple ways, ranging from correlations between past and subsequent returns to long–short factor portfolios. Whereas investors have pursued momentum investing for centuries, the “science” of understanding momentum is rather new, dating back only about a quarter-century. This is the fourth and final article in the Alice in Factorland series. Yes, momentum can probably be saved, even net of fees and trading costs. By evading these traps, we can narrow the gap between paper and live results. The three traps for momentum investing are 1) high turnover, in crowded trades, which leads to high trading costs 2) a careless sell discipline, because momentum’s profits accrue for months, not years, and then reverse course and 3) repeat winners (and losers), which have been soaring (or tumbling) for so very long they enjoy little or no momentum follow-through. But a careful review of the competitive landscape reveals that most claims of the merits of momentum investing are not supported by data, particularly not live mutual fund results, net of trading costs and fees. A handful (especially in the hedge fund community) may be able to point to respectable fund performance, net of trading costs and fees. To be sure, most advocates of momentum investing will disavow the standard model, and will claim they use proprietary momentum strategies with better simulated, and perhaps better live, performance. This means 18 years with no alpha, before subtracting trading costs and fees! 2 Worse, because the standard momentum factor gave up so much ground in the last momentum crash of 2008–2009, it remains underwater in the United States, not only compared to its 2007 peak, but even relative to its 1999 performance peak. 1 No US-benchmarked mutual fund with “momentum” in its name has cumulatively outperformed its benchmark since inception, net of fees and expenses. However, live results for mutual funds that take on a momentum factor loading are surprisingly weak. So, our title may seem unduly provocative. On paper, momentum is one of the most compelling factors: simulated portfolios based on momentum add remarkable value, in most time periods and in most asset classes, all over the world.
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