This paper empirically investigates the relation between investor wealth and the value of annual and quarterly earnings announcements as implied by (1) the behavior of mean stock trade transaction sizes at announcement dates, and (2) transaction size-stratified trading activity in postannouncement time periods. Announcement-period mean transaction sizes are found to exceed expected mean transactions sizes estimated from trading activity occurring in nonannouncement periods. This result is attributed to a greater relative trading response to earnings announcements by wealthier investors, consistent with Ohlson's [1975] proposition that information value increases with investor wealth in a security market setting. Further evidence of a positive relation between value and wealth is found when postearnings announcement stock transactions are stratified by share size into three strata, where the first stratum (small stratum) is transactions of 100 and 200 shares, the second stratum (large stratum) is transactions of 300 to 900 shares, and the third stratum (institutional

[This research examines and interprets the security market response around annual report release dates. Annual reports contain data beyond that reported in preliminary earnings announcements. While prior research has concentrated on the price response to annual report releases and the issue of information content with mixed results, this paper extends earlier work by employing both price and trading measures. The trading measures, which include number of transactions, size-stratified transactions, and mean transaction size, enable the analysis to address not only information content issues, but also social welfare issues in the sense of Lev and Ohlson (1982) and to coarsely identify what type of investor responds to the annual report by trading. Inferences as to the relative usefulness of annual reports to different investor groups are also made based on these trading measures. A number of studies address, often indirectly, annual report informativeness by examining the price response accompanying the report's release. Of these, studies by Wilson (1987) and Lobo and Song (1989) suggest a price response to the earlier of the annual report or 10-K. However, other studies, including Foster et al. (1986), Mynatt (1988), and Bernard and Stober (1989), fail to detect such a price response. The informativeness of annual reports from a price-based perspective remains an open question and from a trading-based perspective it is an unaddressed question. In this study, price response to the annual report is measured using both absolute value and squared unexpected returns. A market model approach is used to estimate unexpected returns. A simulation analysis is included to substantiate the ability of these two metrics to detect a price response. Unexpected volume and unexpected number of transactions, both in general and for size-stratified trading, are estimated via market model regressions. Unexpected mean transaction sizes are estimated from a temporal drift regression. No evidence of a price response and little evidence of a volume of shares response at annual report dates is found. Number of transactions, however, increases significantly around annual reports, peaking four to five trading days after the annual report release date. This trading response suggests that investors find annual reports informative. The simultaneous absence of an economically significant price response is interpreted as indicating that annual reports provide social welfare value in the sense proposed by Lev and Ohlson (1982). Most notably, the analysis of trading response stratified by transaction size suggests that the trading response occurs mostly within the smallest size strata (i.e., 100 and 200 share transactions, transactions of less than $30,000 in value). Consistent with this, mean transaction sizes during the annual report period are less than expected. This is contrary to mean transaction size behavior at earnings announcement dates (Cready 1988) and suggests that annual reports are of greater value to less wealthy individual investors relative to wealtheir individual and institutional investors. Such a finding is consistent with Hakansson (1977) in that it suggests that "small" investors rely on the public information system (i.e., the annual report) while "large" investors rely more on predisclosure information in making investment decisions.]

[This paper presents a replication and correction of a 1978 paper concerning the security market reaction to discretionary accounting changes (a switch to LIFO). The paper contains a flaw in the experimental design. Once the design is corrected, some of the conclusions reached in the original paper must be revised. However, the corrected results do not negate the finding that the security market reaction to a LIFO adoption is conditional on earnings.]

This paper shows that the simulation-based rejection percentages for detecting abnormal log-transformed volume reported in Ajinkya and Jain [AJ] (1989) are sensitive to the method of inducing abnormal volume. We present an alternative inducement method that possesses desirable distributional properties. Under this method, for example, with fifty firm portfolios and one-day event periods a 20% volume increase is detected just over 40% of the time, while AJ suggest a detection rate of over 90%. Rejection percentages for abnormal volume at earnings announcement dates also are more consistent with our alternative method than with AJ's method.

Comments on the failure of Earl K. Stice (1991) to distinguish between residuals and forecast errors in computing Z-statistics derived from squared unexpected returns. Meaning of residual; Distinction of residual from forecast error; Importance of the distinction between residuals and forecast errors.

Lobo and Mahmoud [1989] (henceforth LM) draw two conclusions about empirical distributions of a normal theory test statistic (ZWr) derived from two-day squared standardized unexpected returns: (1) the empirical distribution of test statistic are lower than their normal theory means and (2) the probability of obtaining large values of those statistics is greater for firms with few analysts' forecasts or for small firms. We replicate portion of LM study that classifies firms according to firm size and find that of LM test statistics are not, on average, lower than their normal theory means. Our results on probability of obtaining large values of these statistics as a function of firm size are inconclusive. The LM findings, if true, have significant implications for how empirical research employing squared unexpected returns should be conducted and interpreted. They imply that it is inappropriate to rely on theoretically derived means, as is done by Dodd et al. [1984], in ascertaining presence or nonpresence of an increase in squared returns. It is also imperative to control for firm size when conducting comparative examinations of squared returns, something Cready and Mynatt [1991], for instance, fail to do when they employ an out-of-sample squared return simulation to interpret sample results.

Using returns to scale as a conceptual foundation, we explore how R&D-related earnings performance and earnings variability depend upon firm size. We find that the positive association between the level of future earnings and R&D intensity increases with firm size, and that the positive association between the volatility of future earnings and R&D intensity decreases with firm size, consistent with R&D productivity increasing with scale. We also show that R&D scale is associated with lower market returns, consistent with the idea that R&D investment risk declines with scale.