Implications of Short Selling and Divergence of Opinion for Investment Strategy 155 Scherbina later expanded on the idea that analysts with poor fore- casts for earnings simply stop coverage rather than put out bad news. 78 She presents empirical evidence that this happens. The average earnings surprise (reported quarterly earnings minus average of the analysts’ esti- mates) is negative and correlated with the dispersion of opinion. She estimates a measure of bias in earnings for the case where the number of analysts following a stock declines by assuming they would have esti- mated earnings one cent lower than the lowest estimate. This estimate of bias turns out to highly significant in predicting the earnings surprise. As in previous studies, the earnings surprise is related to the past quar- ter revision in earnings forecasts (i.e., analysts do not adjust their esti- mates as much as they should, probably to minimize sharp changes). High market equity-to-book equity (i.e., a measure of growth stocks status) predicts negative earnings surprise. This means that the analysts overestimated earnings for growth stocks to a larger degree. When examining the abnormal earnings around the announcements of earnings (i.e., whether they did better than the average stock during the three days around the announcement), she showed that when used in isolation, dispersion of earnings had a statistically significant negative effect. This meant that stocks with a high dispersion of opinion about earnings tended to decline in price when the earnings were announced, presumably because the announcement reduced some of the dispersion of opinion. As might be expected from the above finding that analysts do not adequately adjust their estimates, the previous quarter’s revision has a powerful effect on the abnormal returns. If analysts have been revising returns upwards, the abnormal returns will be larger. Interestingly, when this variable is in the equation the measure of dispersion of opinion remains negative, but it is no longer statistically significant. This indicates a possible statistical problem. Since analysts revise their estimates at different times, when there is a trend in analysts estimates (presumably because new information is coming out), the dis- persion in analysts’ opinions may be related to the (absolute) value of this trend. Thus, on the positive side, for revisions, the two could be correlated for statistical reasons. On the negative side, there could be some additional negative correlation. Scherbina shows that an estimate of the effect of the missing ana- lysts’ forecasts is statistically significant in predicting abnormal returns when used alone, but its effect becomes insignificant when the last quar- ter’s revisions are included. 78 Anna Scherbina, “Analysts Disagreement, Forecast Bias and Stock Returns,” working paper, Harvard University, September 2003. 6-Miller-Implications Page 155 Thursday, August 5, 2004 11:11 AM 156 THEORY AND EVIDENCE ON SHORT SELLING Ackert and Athanassakos had earlier found that the bias in analysts’ forecasts increases with the dispersion in earlier analysts’ forecast for 1980–1991. 79 Their explanation is that analysts are under pressure to be optimistic (for instance to win favor with companies and potential investment banking clients). The greater the uncertainty about the pros- pects for the company’s earnings, the easier it is for them to be optimis- tic without risking too much embarrassment. Assuming that investors base investment decisions equally on all analysts (or on the mean of their opinions), stocks with optimistic earnings estimates (relative to what will actually happen) will perform worse. They test this by calcu- lating the standard deviation of analysts’ estimates of earnings (appar- ently not standardized). After grouping firms into quartiles they find that the quartile with the lowest dispersion of estimates outperforms the quartile with the highest dispersion of estimates by the equivalent of 11.35% per year. The effect is slightly less at 10.16% when adjusted for beta, indicating the high dispersion of opinion stocks are higher beta ones. These results are similar to those obtained from dispersion of opinion theory combined with restrictions on short selling. It should be noted, however, that they do have an alternative theory to explain the inverse correlation between divergence of opinion and future returns. In their model, the underperformance results from uncer- tainty (measured by the standard deviation of analysts’ estimates) which permits analysts to produce biased earnings estimates that then affect investor behavior. The work of Scherbina supports part of this model showing that there is a price decline around the earnings announcement that increases with the divergence of opinion. 80 This would logically be expected to be accompanied by increasing divergence of opinion in the period until the next announcement. By using a 60-day window follow- ing the announcement to calculate drift, they exclude the informational content of the earnings announcements. Thus, though appearing to interpret their work as supporting Varian rather than Miller, their work is compatible with both theories. Empirical work on the effect of diver- gence of opinion should include both the earnings around earning announcements and the period between announcements. Boehme, Danielson, and Sorescu, using data from January 1988 to July 1999, examined the effect of short sale constraints and divergence of opinion on returns. 81 The data sample was composed of all firms for 79 Lucy F. Ackert and George Athanassakos, “Prior Uncertainty, Analyst Bias, and Subse- quent Abnormal Returns,” Journal of Financial Research (Summer 1997), pp. 283–273. 80 Scherbina, “Stock Price and Differences of Opinion: Empirical Evidence that Pric- es Reflect Optimism,” cited in Diether et al. 81 Boehme, Danielson, and Sorescu, “Short Sale Constraints and Overvaluation.” 6-Miller-Implications Page 156 Thursday, August 5, 2004 11:11 AM Implications of Short Selling and Divergence of Opinion for Investment Strategy 157 which short interest data were available in electronic form, which meant using New York Stock Exchange data from 1988 and NASDAQ data from 1993. Their measure of short sales constraints was the relative short interest (the monthly short interest divided by the number of shares outstanding). They rely on research by D’Avolio to support this. 82 He showed that the costs of shorting stock (except for the least shorted stocks) rose with the short interest. Boehme et al. noted that using analysts’ earnings estimates to estimate divergence of opinion excluded the smallest firms, which were the ones where short con- straints were the most likely to be binding and divergence of opinion effects the strongest. Thus they chose to use volatility and turnover as measures of divergence of opinion. Two proxies for divergence of opinion were used. One was the stan- dard deviation of error terms from a market model estimated over the previous 100 days. They justify this by reference to theoretical models correlating belief dispersion with asset time-series volatility. They quote the Peterson and Peterson evidence of a positive relationship between return volatility and the dispersion of I/B/E/S forecasts. 83 As described above, there are other studies that show a positive correlation between return volatility and the dispersion of analysts’ forecasts. Using volatility as a surrogate for divergence of opinion in testing the Miller prediction that, all things equal, dispersion of opinion raises stock prices and lowers returns, puts the Miller hypothesis at a disad- vantage. This is because volatility is a direct measure of risk, and risk is generally regarded as undesirable and something that investors will avoid. As described below, divergence of opinion creates a situation in which nonsystematic risk should be priced. The other proxy used for divergence of opinion is turnover (trading volume over a 100-day period divided by number of shares outstanding). The argument is apparently that most trading consists of one who is rel- atively optimistic about a security selling to one who is more pessimistic, and thus the extent of turnover proxies for divergence of opinion. They do not use analysts’ divergence of opinion about earnings. One argument is that it is available only for the larger firms, and in their study they wanted to include smaller firms, firms too small to have opin- ions available on them. The two proxies for divergence of opinion used, turnover and standard deviation of error terms from a market model, can be calculated for all firms, including the smallest. 82 Gene D’Avolio, “The Market for Borrowing Stock,” Journal of Financial Eco- nomics (2002), pp. 271–306. 83 P. P. Peterson and D. R. Peterson, “Divergence of Opinion and Return,” Journal of Financial Research (1982), pp. 125–134. 6-Miller-Implications Page 157 Thursday, August 5, 2004 11:11 AM 158 THEORY AND EVIDENCE ON SHORT SELLING Their basic methodology was to calculate monthly deviations in returns from a four factor model in which three of the factors were those used by Fama and French, plus a momentum factor. 84 The three Fama and French factors reflected the influence of the market (i.e., the traditional return to beta), the return to small size, and the return to high versus low book-to-market stocks. The momentum factor was sug- gested by Carhart, 85 and supported by evidence 86 that this addition was needed to the Fama-French model. This is a relatively stringent test. These four factors are given the first chance to explain returns, and to the extent these factors are related to either constraints on short selling or divergence of opinion, the measured effect of the variables of interest are reduced. The abnormal returns relative to the four-factor model were calcu- lated resulting in 555,436 observations. For each month the stocks in the database (i.e., those with short interest data) were sorted into 64 mutually exclusive portfolios with four size categories, four categories of relative short interest, and four categories of a surrogate for diver- gence of opinion (volatility or turnover). Each of these 64 categories constituted a separate portfolio. Statistically significant negative abnor- mal returns were interpreted as evidence of overvaluation. As predicted, the most overvalued portfolios were those that were expensive to short (small size and being in the highest quartile of relative short interest) and possessed high (in the highest quartile) dispersion of investor beliefs, whether measured by volatility or turnover. The statistically significant effects were focused on firms outside of the quartile of the largest stocks. Firms in these categories were then combined into one portfolio for further tests. The reported results used volatility as the measure of divergence of opinion. The returns to these portfolios were abnormally negative (relative to the four-factor model) and statistically significant. For a one year horizon, the portfolios underperformed by monthly amounts equivalent to between 10.4% and 19.6%. For a one-month holding period the abnormal return was equiv- alent to –26.9% annually. For the practical investor, this procedure seems able to identify stocks that should be avoided, and possibly even sold short. 84 Eugene Fama and Kenneth French, “Common Risk Factors in Returns on Stocks and Bonds,” Journal of Financial Economics (1993), pp. 3–56. 85 Mark Carhart, “On the Persistence in Mutual Fund Performance,” Journal of Fi- nance (1997), pp. 57–82. 86 Eugene Fama and Kenneth French, “Multifactor Explanations of Asset Pricing Anomalies,” Journal of Finance (May 1996), pp. 55–84 6-Miller-Implications Page 158 Thursday, August 5, 2004 11:11 AM Implications of Short Selling and Divergence of Opinion for Investment Strategy 159 Further work showed that the effect required both high short inter- est and high dispersion of opinion as theory predicted. Both high rela- tive short interest and high volatility are relatively poor predictors of overvaluation, but are powerful when combined. The authors also show the results for the raw returns. The point estimates showed the returns to be negative over a one-month period, and to be less than the risk-free rate over a one-year period. These return differences were statistically significant when compared to a portfolio with high short sales constraints, and low volatility over one- month and one-year periods. They were also significant when compared with a portfolio with both low short sales constraints and low disper- sion of investors’ beliefs. Over one month this desirable value weighted portfolio earned 1.07%, while the short sale constrained, high diversity portfolio lost 1.38%. This is an economically significant difference. While Boehme et al. interpreted turnover (volume divided by num- ber of shares) as a measure of diversion of opinion and found that it lowered return for many classes of firms, there is another study that reached what appears to be a different conclusion. 87 Garfinkel and Sokobin argued that volume could be used as a measure of divergence of opinion. 88 In particular, they devise two measures of abnormal turnover around earnings announcements, which they plausibly argue measures divergence of opinion. They then examine the earnings drift after the announcement of earnings. Earlier research had established that there was a tendency for stocks experiencing unexpected earnings increases or decreases to continue moving in the same direction, an effect that is called “drift.” They were curious how this drift was affected by diver- gence of opinion as measured by abnormal volume. Their result was that abnormal volume was accompanied by positive drift, and that the higher the abnormal volume the more positive the drift. They inter- preted this as consistent with Varian’s work that interpreted divergence of opinion as a measure of risk, which should be rewarded by a higher rate of return. 89 Clearly that is possible, and it is indeed plausible that the higher divergence of opinion stocks are indeed riskier. However, there is another interpretation in terms of Miller’s theory that divergence of opinion in the presence of short selling can raise prices. 90 In the short and medium run, that theory predicts that rising divergence of opinion should raise prices, and falling divergence of 87 Boehme, Danielson, and Sorescu, “Short Sale Constraints and Overvaluation.” 88 John A. Garfinkel and Jonathan Sokobin, “Volume, Opinion Divergence and Re- turns: A Study of Post-Earnings Announcement Drift,” working paper, August 2003. 89 Varian, “Divergence of Opinion in Complete Markets: A Note.” 90 Miller, “Risk, Uncertainty, and Divergence of Opinion.” 6-Miller-Implications Page 159 Thursday, August 5, 2004 11:11 AM 160 THEORY AND EVIDENCE ON SHORT SELLING opinion should lower them, all things equal. Consider a stock where most of the relevant information about its value comes every quarter when the earnings (along with an income and balance sheet statement and management commentary) are released. This is believed to be plau- sible, especially for smaller companies. Of course, there is some other information (general economic data, etc.), and there is considerable uncertainty about just what future earnings will be. It is to be expected that there will be divergence of opinion about the value of this company and about future earnings. Each time earnings are announced some of this uncertainty is resolved, but there are always new events occurring that different investors interpret differently. Thus, we would expect that the divergence of opinion about the value of this stock would decline at the time of each earnings announcement and then gradually increase (as hard to interpret information became available). Prices should decline when earnings come out and then drift upwards. The greater the diver- gences of opinion, the greater this drift. The prediction of divergence of opinion theory is thus supported by this paper. Since the period from earnings announcement to earnings announce- ment averaged 90-plus days, the 60-day window of Garfinkel and Sokobin excludes the period immediately before a new earnings announcement. Other studies show that prices typically react before the announcement of earnings, and that such movements are in the direction of the unexpected component of the coming earnings announcement. 91 This is probably due to some combination of inside information leaking out and investors reacting to such information as other firms publishing quarterly and annual statements before the firm in question. Ideally, one paper would study the effect of divergence of opinion on returns over a period, and then break that down into the effect when earnings were announced, during the period between announcements, and just before announcements. Such a paper would combine the work of Garfinkel and Sokobin with that of Scherbina. 92 While it is intellectu- ally interesting to break returns down into the earnings announcement reaction, a drift period, and a prenew announcement period, most inves- tors will hold their positions for a long enough period to include all three periods. However, even if transaction costs prevent investors from planning to buy and sell within one period, having a little knowledge of 91 Richard J. Rendleman, Jr., Charles P. Jones, and Henry A. Latane, “Empirical Anomalies Based on Unexpected Earnings and the Importance of Risk Adjustment,” Journal of Financial Economics (1982), pp. 269–287. 92 See both Scherbina, “Stock Price and Differences of Opinion: Empirical Evidence that Prices Reflect Optimism;” and Scherbina, “Analysts Disagreement, Forecast Bias and Stock Returns.” 6-Miller-Implications Page 160 Thursday, August 5, 2004 11:11 AM Implications of Short Selling and Divergence of Opinion for Investment Strategy 161 returns over the next few days may help in timing transactions that would be made in any case. Such knowledge might help investors decide whether to trade before or after the next announcement, the details of which they cannot anticipate. It might be nice to control for the availability of nonearnings infor- mation. One surrogate might be how early in the earnings season earn- ings were announced (frequently a guess can be made at earnings from knowing what was announced by other firms in the industry). Another might be whether the firm was in an industry where there were monthly or weekly announcements of industry sales. A third might be whether there were frequently warnings or other announcements given (obvi- ously this study would be more labor intensive). In an appendix, Garfinkel and Sokobin report that they found the tendency reported by others for stocks with high analysts’ divergence of estimates to have lower returns, and that their abnormal volume mea- sures worked best for stocks without analysts forecasts (which tended to be smaller companies). In considering the wisdom of avoiding stocks with high volatility, it should be recalled that volatility is usually considered an aspect of risk and hence something to be avoid. In spite of the finance theory holding that there is tradeoff between risk and return, it appears that there is a strategy that is both higher return and lower risk, buying stocks with low dispersion of beliefs. The measure of volatility used (interpreted as a measure of divergence of opinion) was the residual from a market model. Thus, it measures what financial theorists call diversifiable (or nonsystematic risk). In the- ory such risk should not affect returns because investors can and have diversified it away. In practice, most institutional investors may be diver- sified enough to have diversified away most of this risk. Even this conclu- sion presumes that the institutions have not exposed themselves to a type of nonsystematic risk which has not been diversified away (such as a heavy emphasis on growth stocks or those exposed to another factor). Many individual investors are very poorly diversified, holding only a few individual issues. They are very definitely exposed to this volatility risk. For them, avoiding high dispersion of opinion stocks should both increase return and lower risk. Boehme, Danielson, and Sorescu did a second study. 93 The data ended in July 2000, slightly later than in their other study. The major 93 See both, Rodney D. Boehme, Bartley R. Danielson, and Sorin M. Sorescu, “The Valuation Effects of Dispersion of Opinion: Premium and Discount,” working pa- per, February 20, 2003, presented at FMA in October, 2003; and Boehme, Daniel- son, and Sorescu, “Short Sale Constraints and Overvaluation.” 6-Miller-Implications Page 161 Thursday, August 5, 2004 11:11 AM 162 THEORY AND EVIDENCE ON SHORT SELLING addition is considering the effect of options. As discussed above, Daniel- son and Sorescu had earlier shown that options served to make short sales constraints less binding. 94 In this study they added the availability of options as an additional indicator of whether the short sales con- straint was binding. They contrast the effects of divergence of opinion in the model of Miller where there are short sale constraints with the models of Merton and Varian, which they interpret as predicting that prices will be lower and returns higher in the presence of divergence of opinion. In Merton’s model investors only invest in securities they are familiar with, and thus hold nondiversified portfolios. 95 Thus, investors demand compensation for nonsystematic risk in their securities. In a market without obstacles to short selling, this results in higher returns for the high divergence of opinion stocks. Varian concluded that for plausible parameters of risk aversion that dispersion of opinion should lower asset prices in a com- plete market. 96 Presumably, the effects he considered could outweigh the effects of restrictions of short selling. Boehme et al. argue that the hypotheses of Merton, Varian, and Miller should be regarded as complementary. They argue that stocks differ in both the degree of divergence of opinion, and in the severity of their short sale constraints, and that the relative strength of the effects should depend on the stocks. They argue that for stocks with a high dis- persion of opinion, the Miller effect should dominate where there are strong constraints on short selling and the Merton-Varian ones where short selling is relatively unconstrained. As an indicator of the strength of short sale constraints they use size, the presence of options, and rela- tive short interest (the percentage of a firm’s shares that are short). It is argued that the costs of borrowing the stocks to deliver in a short sale rises as the number of shares borrowed increases. Thus, the level of short interest is viewed as an appropriate proxy for the marginal cost of shorting a security. The stocks of large firms are viewed as easier to bor- row because there is more stock available. If options on a stock are available, trading these (especially puts) provides an indirect equivalent to a short sale without borrowing the stock. Because of the way the option markets work, the result of a negative bet in the options market is often that an option dealer sells the stock short, but these dealers can 94 Bartley R. Danielson and Sorin M. Sorescu, “Why Do Option Introductions De- press Stock Prices? A Study of Diminishing Short-Sale Constraints,” Journal of Fi- nancial and Quantitative Analysis (December 2001), pp. 451–484. 95 Merton, “A Simple Model of Capital Market Equilibrium with Incomplete Infor- mation.” 96 Varian, “Divergence of Opinion in Complete Markets: A Note.” 6-Miller-Implications Page 162 Thursday, August 5, 2004 11:11 AM Implications of Short Selling and Divergence of Opinion for Investment Strategy 163 do this more cheaply than other investors. Thus, short selling is less constrained for stocks with options. One of the interesting results was that relative short interest helped predict returns. The higher the relative short interest, the lower the returns. When returns are expressed as deviations from the predictions of the four factor model, it was found that the quartile of firms with the highest relative short interest had statistically significant lower returns (by 0.394% per month) and the quartile with the lowest relative short interest had statistically higher than average returns (by 0.273% per month). These are large enough differences to be useful to investors. Because even the most heavily shorted stocks had positive returns, indi- vidual short sellers who typically do not get use of the proceeds, would lose money by shorting the most heavily shorted stocks. However, inves- tors could improve their returns by being long in the stocks with the lowest relative short interest. These results are similar to those found by other studies using short interest figures. The authors state that the neg- ative abnormal returns for highly shorted firms are driven by the highly negative returns among these stocks with high divergence of opinion. Although these authors interpret high short interest as evidence for relatively high costs to shorting, it can also be interpreted as direct evi- dence for divergence of opinion. Because of the costs of shorting, including the failure to get a market rate of the proceeds of short sales, only investors who expect the returns on a stock to be much lower than normal will short the stock. In fact, short sellers (except for those involved in some type of hedge) are typically selling short stocks that they expect to actually decline in price. As the divergence of opinion about the returns from a stock increases, the fraction of the investors who expect negative returns (or returns below any other low level) increases. Thus, the relative short interest is also a surrogate for diver- gence of opinion. In their main tests, the authors attempt to identify a set of stocks which are relatively short sale constrained. These are the stocks with no options (which make them likely to be among the smaller capitalization stocks) and are also among the quartile of firms with the highest relative short interests. They also identified a set of relatively short interest unconstrained stocks. These were the companies with options traded which were also in the highest quartile for capitalization. All other firms were in an unconstrained class. The reader may immediately note that the sizes of the highly constrained (primarily small firms) and the unconstrained firms (large firms) are quite different. If absolute returns were being studied, this might be a problem since a size effect could be confused with a short sales constraint-related effect. However, since the primary measure of returns was the deviation from the returns predicted 6-Miller-Implications Page 163 Thursday, August 5, 2004 11:11 AM 164 THEORY AND EVIDENCE ON SHORT SELLING by a 4-factor model (one of which factors was size), this appears to be less of a problem. Using volatility (standard deviation of the residuals from a market model for the last 100 days) as a measure of divergence of opinion, the abnormal returns (relative to a 4-factor model) were then calculated. For the unconstrained set of firms, the returns increased with the volatil- ity surrogate for divergence of opinion. They interpret this as being con- sistent with the prediction of the Merton-Varian theory. The simplest interpretation is that volatile firms are riskier, and the investors will only hold them if they get a higher return. This higher return is esti- mated at about 1% per month for the quartile of the most volatile firms. This is an important effect because most of the market value is in these relatively large firms. Logically, the presence of a reward to risk does not disprove that the prices are still not being set by the most optimistic investors, with the less optimistic investors holdings being at zero rather than the negative value a strict Markowitz optimization would imply. Remember these firms are in the least short sold group, which may imply that many hold- ers simply choose not to sell short. Another factor is that these are likely to be relatively large firms in which a significant fraction of institutional investors have taken a posi- tion. Working from the extreme right hand of the bell curve, one has to go further toward the average opinion to find sufficient investors to absorb the supply of stock. This would make the price-raising effect of divergence of opinion weaker (but not nonexistent) with a correspond- ingly small return lowering effect. One other possibility is that during the period in question (1992– July 2000 for NASDAQ, 1988–July 2000 for the NYSE) unusually vola- tile large capitalization stocks with options is a category that would pick up many of the large capitalization growth stocks that were doing very well during this period. Possibly the results would be different if the sample had been extended on either side. However, since the abnor- mal return was measured relative to the predictions of a multifactor model, including factors designed to control for growth versus value and size, this is less of a concern. The results for the highly short sale constrained firms were found to be consistent with Miller’s divergence-of-opinion theory. As the diver- gence of opinion increased the return declined. The short constrained firms with high divergence of opinion underperformed by 140 basis points per month. As Boehme et al. point out, this is a striking confir- mation of Miller’s predictions. Although they do not point it out, since volatility is usually considered a risk measure, to get such abnormally low returns, the divergence of opinion effects must overwhelm a strong 6-Miller-Implications Page 164 Thursday, August 5, 2004 11:11 AM [...]... measure of shorting demand is short interest, that is, the level of shares sold short Unfortunately, using short interest as a proxy for shorting demand is problematic, because the quantity of shorting represents the intersection of supply and demand Demand for shorting should respond to both the cost and benefit of shorting the stock, so that stocks that are very costly to short will have low short interest... are: short selling in the 1920s and 1930s; fights between short sellers and the companies they short; and Palm/3Com in the year 2000 I conclude with a discussion of the tech stock mania of 1998–2000, and whether the entire market (and especially the tech sector) was identifiably overpriced SHORT SALE CONSTRAINTS Many things constrain investors from going short First, there are mechanical impediments to short. .. in 19 95, the Finance Ministry proposed mandatory caning as the punishment for short sellers Governments often restrict short selling in an attempt to maintain high security prices Meeker reviews the attempts by a colorful cast of characters (from Napoleon to the New York state legislature) to ban short selling. 3 3 J Edward Meeker, Short Selling (New York: Harper & Brothers Publishers, 1932) Short. .. expected returns between date 0 and date 2 are low (as the value is expected to fall from 2 ,50 0 to 2,000), and thus a buy -and- hold strategy is a bad idea There are several ways to describe this result First, you could say that the reason NASDAQ trades at 50 0 above fundamental value at 186 THEORY AND EVIDENCE ON SHORT SELLING date 0 is that both A and B think there is a 50 % chance they will be able to... returns from the stocks that exhibited more short- term volatility 103 See Boehme, Danielson, and Sorescu, Short Sale Constraints and Overvaluation” and also, Rodney D Boehme, Bartley R Danielson, and Sorescu, “The Valuation Effects of Dispersion of Opinion: Premium and Discount.” 104 Bipin B Ajinkya, Rowland K Atiase, and Michael J Gift, “Volume of Trading and the Dispersion in Financial Analysts’ Earnings... impossible to short have an infinite shorting cost, yet the level of short interest is zero Thus it could be possible that short inter- 188 THEORY AND EVIDENCE ON SHORT SELLING est is negatively correlated with overpricing (we will see this issue arise below in the 3Com/Palm case) The problematic nature of short interest leads to weak empirical results An alternative measure of shorting demand is breadth... Jones and Lamont, Short Sale Constraints and Stock Returns.” Short Sale Constraints and Overpricing 189 time for individual stocks Furthermore, new stocks periodically appear in the loan crowd, and we are able to track the behavior of these stocks both before and after they first appear on the list Stocks appear on the list of loan crowd stock when shorting demand cannot be met by normal channels, and. .. Constraints and Overpricing 183 Short sellers face periodic waves of harassment from governments and society, usually in times of crisis or following major price declines as short sellers are blamed Short sellers are often thought to be in league with America’s enemies The general idea seems to be that short selling is bad, and when bad things happen (such as war) it probably involves short sellers... Exchange (NYSE) imposed special short selling regulations during World War I (in November 1917) in response to both a substantial market decline and a fear that the Kaiser would send enemy agents to drive down stock prices Jones and Lamont discuss the crackdown on short selling after 1929.4 Short sellers were extremely unpopular in 1930, and many politicians, journalists, and investors blamed them for... limit short selling Press reports indicate that authorities in Britain and Japan have sought to discourage shorting and securities lending A major lender of European stocks announced it was ceasing securities lending and urged others to do the same In addition to hostility from governments, short sellers also face hostility from the firms they short Managers of firms don’t like people 4 Charles M Jones and . Bias and Stock Returns,” working paper, Harvard University, September 2003. 6-Miller-Implications Page 155 Thursday, August 5, 2004 11:11 AM 156 THEORY AND EVIDENCE ON SHORT SELLING Ackert and. al. 81 Boehme, Danielson, and Sorescu, Short Sale Constraints and Overvaluation.” 6-Miller-Implications Page 156 Thursday, August 5, 2004 11:11 AM Implications of Short Selling and Divergence of Opinion. Peterson and D. R. Peterson, “Divergence of Opinion and Return,” Journal of Financial Research (1982), pp. 1 25 134. 6-Miller-Implications Page 157 Thursday, August 5, 2004 11:11 AM 158 THEORY AND