This paper addresses the issue of whether investors with acirc;not;SnaAtilde;macr;veacirc;not;? earnings expectations (i.e., earnings forecasts that are systematically less accurate than other publicly available predictions) have sufficient market power to affect common stock prices. The results clearly indicate that when security analysts predict quarterly earnings increases (decreases), from the same fiscal quarter of the prior year, that the abnormal return around the upcoming earnings announcement tends to be positive. When the data are formed into 50 portfolios, about 66% of the abnormal return variation around earnings announcements is explained by the predicted earnings change. This is surprising since the forecasts used are dated from one to thirteen weeks before the earnings announcement.

Profit from earnings announcements, by taking targeted, short-term option positions explicitly timed to exploit them! Based on rigorous research and huge data sets, this book identifies the specific earnings-announcement trades most likely to yield profits, and teaches how to make these trades--in plain English, with real examples! Trading on Corporate Earnings News is the first practical, hands-on guide to profiting from earnings announcements. Writing for investors and traders at all experience levels, the authors show how to take targeted, short-term option positions that are explicitly timed to exploit the information in companies' quarterly earnings announcements. They first present powerful findings of cutting-edge studies that have examined market reactions to quarterly earnings announcements, regularities of earnings surprises, and option trading around corporate events. Drawing on enormous data sets, they identify the types of earnings-announcement trades most likely to yield profits, based on the predictable impacts of variables such as firm size, visibility, past performance, analyst coverage, forecast dispersion, volatility, and the impact of restructurings and acquisitions. Next, they provide real examples of individual stocks-and, in some cases, conduct large sample tests-to guide investors in taking advantage of these documented regularities. Finally, they discuss crucial nuances and pitfalls that can powerfully impact performance.

Using a unique data set of complete trade records, we find that large individual investors are successful at picking stocks. Large individual investors' correlated trades not only can move synchronous stock prices but also can positively predict future returns. More importantly, large individual investors tend to trade before major earnings announcements and large price changes, suggesting that they are able to exploit value-relevant information. In contrast to large individual investors, small retail investors' correlated trades are inversely associated with synchronous and future stock returns, indicating that small retail investors are uninformed and naïve. The differential information content between large individual and small retail investors highlights the need to classify individual investors according to their investment amount when examining their role in stock pricing.

Individual investors are believed to trade on noise. Based on this assumption, this paper investigates whether noisy variables, such as price trends and market sentiment, attract more attention from these investors than value-related information such as the price-earnings ratio. The results suggest that price-earnings dynamics are more important in explaining changes in attention than noisy variables. Moreover, the negative sign exhibited by the value-attention relationship indicates that individual investors are more (less) attentive to stocks when they become cheaper (more expensive). I also demonstrate that this association is more representative during down markets but absent during positive periods, contradicting the stylized fact that retail traders are a driving force of bubbles. Furthermore, I find that these patterns are observable in all G7 countries. Overall, the results do not show that individual investors are consistent proxies for noise traders.

This study provides evidence on the effects of online trading on stock price and trading volume reactions to quarterly earnings announcements. We test for differences in stock price and volume reactions to quarterly earnings announcements between a period with a significant amount of online trading (1996-1999) and a period without online trading (1992-1995). We conjecture that online trading has increased the proportion of naive investors in the market. We predict that this will result in (i) a decrease in the average precision of investor information prior to earnings announcements implying higher ERCs, (ii) an increase in differential interpretation of earnings leading to higher trading volume reactions that are unrelated to price change, and (iii) a decrease in differential prior precision leading to a decrease in the association between trading volume and absolute price change. We find evidence consistent with all three predictions. Our findings are relevant for assessing the validity of concerns about online trading expressed by regulators and the validity of theoretical models of trade with asymmetrically informed investors.

This paper examines the ability of hypotheses based on naive investor expectations to explain the higher returns to contrarian investment strategies. Inconsistent with Lakonishok, Shleifer and Vishny (1995), we find no systematic evidence that stock prices naively reflect extrapolation of past trends in earnings and sales growth. Consistent with Bauman and Dowen (1988) and La Porta (1994), we find that stock prices appear to naively reflect analysts' biased forecasts of future earnings growth. Further, we show that naive reliance on analysts' forecasts of future earnings growth can explain over half the higher returns to contrarian investment strategies.