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[Shiller] "In addition to this long-run tendency toward reversal of trends, there ia a shorter-run weak tendency toward momentum, for stock prices to continue moving in the same direction".
[Shleifer] "Subsequent to De Bondt and Thaler's findings, researchers have identified more ways to successfully predict security returns, particularly those of stocks, based on past returns. Among these findings, perhaps the most important is that of momentum, which shows that movements in individual stock prices over the period of six to twelve months tend to predict future movements in the same direction. That is, unlike the long-term trends identified by De Bondt and Thaler, which tend to reverse themselves, relatively short-term trends continue.
The 52-week High and Momentum Investing, George and Hwang, 2004
There is substantial evidence that stock prices do not follow random walks and that returns are predictable. Barberis, Shleifer and Vishny (1998), Daniel, Hirshleifer, and Subrahmanyam (1998), and Hong and Stein (1999) present theoretical models that attempt to explain the coexistence of intermediate horizon momentum and long horizon reversals in individual stock returns as the result of systematic violations of rational behavior by traders. In Barberis, Shleifer, and Vishny and in Hong and Stein, momentum occurs because traders are slow to revise their priors when new information arrives. Long-term reversals occur because when traders finally do adjust, they overreact. In Daniel, Hirshleifer, and Subrahmanyam, momentum occurs because traders overreact to prior information when new information confirms it. Long-term reversals occur as the overreaction is corrected in the long run. In all three models, short-term momentum and long-term reversals are sequential components of the process by which the market absorbs news. In this paper, we find that a readily available piece of information - the 52-week high price - largely explains the profits from momentum investing. We examine the 52-week high because the models predict, in particular, that traders are slow to react, or overreact, to good news. A stock whose price is at or near its 52-week high is a stock for which good news has recently arrived. This may be the time when biases in how traders react to news, and hence profits to momentum investing, are at their peaks. We find that nearness to the 52-week high is a better predictor of future returns than are past returns, and that nearness to the 52-week high has predictive power whether or not stocks have experienced extreme past returns. This suggests that price levels are more important determinants of momentum effects than are past price changes. These findings present a serious challenge to the view that markets are semi-strong-form efficient. An explanation of behavior that is consistent with our results is that traders use the 52-week high as a reference point against which they evaluate the potential impact of news. This description is consistent with results in experimental economics research on the "adjustment and anchoring bias" surveyed in Kahneman, Slovic, and Tversky. Our results suggest that traders might use the 52-week high as an "anchor". We also examine whether long-term reversals occur when past performance is measured based on nearness to the 52-week high. They do not. This finding, coupled with those described above, suggests that short-term momentum and long-term reversals are not likely to be components of the same phenomenon. Our findings suggest that models in which agents' valuations depend on nearness of the share price to an anchor will be successful in explaining price dynamics.
Momentum Strategies, Chan, Jegadeesh and Lakonishok, 1996
"We relate the predictability of future returns from past returns to the market's underreaction to information, focusing on past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. There is little evidence of subsequent reversals in the returns of stocks with high price and earnings momentum. Market risk, size and book-to- market effects do not explain the drifts. Security analysts' earnings forecasts also respond sluggishly to past news, especially in the case of stocks with the worst past performance. The results suggest a market that responds only gradually to new information".
Momentum and overreaction in experimental asset markets, Vaginal, Porter and Smith, 2000
"Price volatility and investor overreactions are commonplace in experimental asset markets. Understanding the price dynamics in these markets is crucial for designing successful new trading institutions. We report on a series of experiments to test the predictions of a new momentum model using a dynamical systems approach".
Dispersion in Analyst Forecasts and the Profitability of Earnings Momentum Strategies, Disc he , 2001
"It is a well documented phenomenon that stock prices underreact to news about future earnings and drift in the direction suggested by revisions in analysts' earnings forecasts. This paper shows that the dispersion in analysts' consensus forecasts contains incremental information to predict future stock returns. Higher abnormal returns can be achieved by applying an earnings momentum strategy to stocks with a low dispersion. This finding supports one of the recent behavioral models in which investors focus too little on the weight of new evidence and conservatively update their beliefs in the right direction, but by too little in magnitude with respect to more objective information".
Daily Momentum and Contrarian Behavior, Goetzmann and Massa, 2000
Stock selection strategies in emerging markets, Van der Harta, Slagterb and van Dijk, 2002
Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies, Hong, Lim and Stein, 1999
"First, once one moves past the very smallest stocks, the profitability of momentum strategies declines sharply with firm size. Second, holding size fixed, momentum strategies work better among stocks with low analyst coverage. Finally, the effect of analyst coverage is greater for stocks that are past losers than for past winners".