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[Wikipedia] According to the peak-end rule, we judge our past experiences almost entirely on how they were at their peak (pleasant or unpleasant) and how they ended. Virtually all other information appears to be discarded, including net pleasantness or unpleasantness and how long the experience lasted. Evaluations are based primarily on the most extreme and the final moments of an episode with all other moments having essentially no influence on judgment. This heuristic was first suggested by Daniel Kahneman and others. He argues that because people seem to perceive not the sum of an experience but its average, it may be an instance of the representativeness heuristic.
[Odean] "Investors who insist on hunting for the next brilliant stock would be well advised to remember what California prospectors discovered ages ago: All that glitters is not gold".
All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, Barber and Odean, 2003
"We test the hypothesis that individual investors are more likely to be net buyers of attention-grabbing stocks than are institutional investors. We speculate that attention-based buying is a result of the difficulty that individual investors have searching the thousands of stocks they can potentially buy. We look at three indications of how likely stocks are to catch investors' attention: daily abnormal trading volume, daily returns, and daily news. Consistent with our predictions, we find that individual investors display attention based buying behavior. They are net buyers on high volume days, net buyers following both extremely negative and extremely positive one-day returns, and net buyers when stocks are in the news. Professional investors are less prone to indulge in attention-based purchases. With more time and resources, professionals are able to continuously monitor a wider range of stocks. Consistent with the predictions of our model, we find that stocks bought by individual investors on high-attention days tend to subsequently under perform stocks sold by those investors".
The Cross-Section of Analyst Recommendations, Sorescu and Subrahmanyam, 2004
We analyze the relation between analyst attributes (years of experience, reputation of the analysts' brokerage houses) and the short- and long-term price reactions to recommendations made by the analysts. We find that in the long-term, the recommendation changes of highly experienced analysts outperform those of low-experience ones. In addition, investors appear to overreact to dramatic upgrades of low-ability analysts, and underreact to small upgrades by high-ability analysts. These results are consistent with the Griffin and Tversky (1992) argument that agents place too much emphasis on the strength of the signal (the dramatic nature of the event) and insufficient emphasis on the weight (the ability of the analyst making the recommendation). Agents are prone to attaching undue importance to the enthusiasm in a recommendation letter, and not enough importance to the credibility of the recommendation writer. Since the investor bias is stronger for more extreme (high-strength) signals, the market overreacts to such signals. At the same time, the market underreacts to the weight (quality) of the signal. The net result is that prices experience reversals (overreaction) following high-strength, low-weight signals and drift (underreaction) following high-weight, low-strength signals.
Internet Stock Message Boards and Stock Returns, Antweiler and Frank, 2002
"This paper examines whether stocks with high posting levels also have unusual subsequent returns and/or risk. They do. We find that portfolios with particularly high message posting have abnormally poor returns. The poor returns in the high message posting portfolios are accompanied by high volatility. Consideration is given to market manipulation, differences of opinion, and anxiety reduction as possible explanations for the observed patterns".
Is the Market Mad? Evidence from Mad Money, Engelberg, Sasseville and Williams, 2006
"We documented statistically signifcant abnormal returns for the stocks recommended by Jim Cramer on the popular television show, Mad Money. The average cumulative abnormal overnight return for the smallest quartile of recommended stocks is 5.19%, and these returns completely disappear within 12 trading days. After documenting this market inefficiency, we analyzed the trading activity following Cramer's recommendations. Our findings that trading volume and buy-sell imbalance are unusually high on the day following Cramer's recommendation suggests that uninformed traders buy the stocks recommended by Cramer on the previous night. The uninformed traders do this despite the fact that these stocks became overpriced overnight and earn negative cumulative abnormal returns over the next two weeks. Our Finding that short sales volume is unusually high on the day following Cramer's recommendations suggest that some arbitraguers are aware of Cramer's effect on security prices. Taken together, our results suggest that the aggregate losers in our event study are the Mad Money viewers who decide to buy the recommended securities when the markets open the following day, and that the winners are the market makers and arbitraguers who sell the overpriced recommended stocks on day 1, as well as the traders who sell the recommended stocks on days 2 through 12. Individual investors who watch Mad Money would be wise to wait before purchasing the small stocks Cramer recommends, as these stocks tend to fall to their original levels following the overnight price spike caused by his recommendation".
Extrapolation Bias: Insider-Trading Improvement Signal, Fuller & Thaler Research Library
Extrapolation Bias exploits overreaction to past, negative information. This report deals with the behavioral biases that cause investors to overreact and describes one aspect of our Extrapolation Bias strategy which exploits overreaction. Naive extrapolation is largely the result of behavioral biases associated with two heuristics -- the representativeness heuristic and the saliency heuristic. Representativeness involves the tendency of humans to generalize about a population of future outcomes after observing a small sample -- for example, after observing only one or two outcomes, humans will frequently conclude that these two outcomes are representative of future outcomes. Saliency involves the tendency of humans to assign too high a probability to low frequency events after observing a recent, vivid example of the event. For example, immediately after a plane crash has been reported in the news, people will greatly overestimate the probability of future plane crashes. The first step in our Extrapolation Bias strategy is too identify conditions under which investors are most likely to naively extrapolate recent, negative information into the future. The second step is to determine whether the negative information investors are extrapolating into the future is, most likely, temporary.