Hi guys, welcome back. We've been talking about puzzles related to the behavior of the aggregate stock market, right? Patterns that are inconsistent with efficient markets, and how they relate to behavioral biases that we talked about in earlier lectures. So, in this lecture, let's now turn to the behavior of individual stocks. Well, it is, in fact, one of the most frustrating, from a ration point of view. Researchers have found several facts about the cross sectional of average returns. Right, where one group of stocks earn higher average returns on another. Right, and as I mentioned earlier in earlier lectures, these facts have come to be known as anomalies. Because they can't be easily explained, or accounted for by our rational models of risk and return. So in this lecture, you're going to learn about three types of patterns that we observe in individual stock returns that may be closely linked to the typical behavioral mistakes that a message make, right? Specifically, you're going to learn about long-term reversals, the value effect and momentum, okay? So, let's start with De Bondt-Thaler Winner Loser Effect, also known as the long-term reversal effect. Suppose you have historical monthly returns data, and all stocks listed on the New York Stock Exchange. Going back, right, and that's what researchers typically use. So let's think about the following exercise. Suppose you take all the stocks, list it on the New York Stock Exchange, and you rank them based on their past three year cumulative performance. Right, so at every point in time you look at the past three year returns and then rank them. Right, and now you form two portfolios. Right, one that consists of the very best. Right, a winner's portfolio. Right, and another that consists of the very worst all the way at the bottom, right? A loser portfolio, all right? And now you measure the average return to each of those portfolios over the subsequent three years. What would you find? Well, what you're likely to find is that the average return on the loser portfolio is higher than the average return of the winner portfolio by almost 8% a year, pretty amazing? Right, so that's what De Bondt and Thaler did, right? In fact, sorry, the graph here shows you the findings of a similar exercise that De Bondt and Thaler conducted. All right, and in this graph, you see the cumulative five year abnormal returns for these two portfolios. Right, one consisting of the past losers. Right, and one consisting of the past winners. All right, it's the cumulative. Risk adjusted return. Right, well, what they find is, right, the losers over the subsequent five years outperform the market by almost about 30%, right? While the winners do worse than the market by about 10%. So, what's going on here? Well, traditional finance theories, right, would suggest that this must be compensation for risk, right? That is the losers have higher returns, because they are riskier then the average stock and conversely for the winners. But the difference remains even on a risk adjusted basis, right? So, one possible explanation is that this pattern is due to invested behavior. Specifically, the fact that investors who might be relying on the representativeness Heuristic, which we talked about in previous lectures. They become overly pessimistic over past losers, and overly optimistic about past winners, right? In other words, they overreact. So, this example of heuristic driven biased over reaction, therefore, causes prices to deviate from their fundamental values, right. So, past losers become undervalued, because he messes overreact, and past winners become overvalued, right? But this mispricing isn't permanent, and eventually it corrects, leading to losers outperforming the market, and winner underperforming the market. So, this effect comes about because investors cling to the idea that good stocks are the stocks of good companies and vice versa, all right? So, in investor's minds, good companies are representative of successful companies, and therefore, they're associated with stronger earnings, and higher returns going forward, right? And on the other hand bad companies are representative of unsuccess of companies, right? With poor earnings and low returns going forward, right? So, here's an example recounted by Professor at Santa Clara University, right? So, let's go back in time to June 1997, and examine two companies, Dell and Unisys. So, Dell at the time, right, had a strategy that is really simple, right? Sell custom made computers directly to consumers. And at the time in June 1997, right? They were doing really well, their sales had gone up by 47% over the previous year. Their earnings per share had doubled. The stock price had increased by more than 150% over the past three years. Now in investors' minds, right? There is no question that Dell was representative of a successful company, okay? Well, what about Unisys? Well, if you don't know or remember, Unisys is the product of a takeover in the 1980s by Burroughs of Sperry Univac. Right, now, before the merger, both companies were struggling. And after the merger when they came together, they became one large struggling company instead, right? And at the time, in June 1997, right, they be losing money, their market share was falling, their stock price was significantly down. They are going through downsizing right? So clearly, in investors minds, Unisys was more representative of an unsuccessful company, all right? So, how should investors form expectations about future returns? Well, if you believe in a rational model of risk and return, you can think about CAPM, right? Which we looked at in the previous course. And if you looked at their betas at the time, right? The beta of Dell was about 1.6. And beta of Unisys was about 1.9, which translated into an expected return of 19.1% for Dell, 21.7% for Unisys. Right, so, that's what was predicted by the CAPM, right, according to the risk and return model. So, Professor Shefrin conducted a survey, right, to elicit what the investors expected about the future returns of these companies. The respondents were MBA students, who were familiar with finance concepts, and who lived and worked in Silicon Valley. Right, now, this is interesting. The respondents expected an expected return of 20.9% over the next year for Dell. All right, which is not too far out from what is being predicted by the CAPM. In contrast, however, right, the response is predicted and expected return of only 3.6% for Unisys over the same period. All right, so, they either expected Unisys to be a very safe stock, right, and therefore, expected a low return. Or they expected a low return because they believed Unisys to be a representative of a bad company. Right, and of course, stocks of bad companies are bad stocks. Now, were these expectations based on perceptions of risk or representativeness? Which do you think is more plausible? I personally might go with a representativeness, right? So, what did happen? All over the following year, right, over the period from July 1997 to June 1998, Dell continued to do well, right, at an amazing return of 216%. What about Unisys? Well, as for Unisys, over the same period, it earned 270, over 270%. 270.5%, right, beating Dell by more than 50%. Okay, so in this lecture, we'll look at the winner-loser effect, the long-term reversal example, which seems to be driven by investors overreaction, caused by them relying on a heuristic driven bias, such as the representativenes bias.
