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Discretionary-accruals models and audit qualifications
The primary goal of this study is to evaluate the ability of the Cross-sectional Jones Model and the Cross-sectional Modified Jones Model to detect earnings management vis-à-vis their time-series counterparts by examining the association between discretionary accruals and audit qualifications. These two cross-sectional models have not been formally evaluated by prior research, and their use may offer certain advantages to investors and researchers over their time-series counterparts. A sample of 173 distinct firms with qualified audit reports and a matched-pair control sample with clean audit reports are used. Only the two cross-sectional models are consistently able to detect earnings management. One limitation of this study is that its findings merely indicate the superiority of the cross-sectional models vis-à-vis their time-series counterparts in an audit qualification setting, not validate either the former or the latter.
Does Income Statement Placement Matter to Investors? The Case of Gains/Losses from Early Debt Extinguishment
Does the placement of a line item in the income statement matter to investors? The passage of Statement of Financial Accounting Standards (SFAS) No. 145 (Financial Accounting Standards Board [FASB] 2002) affords a quasi-experimental setting to answer this question, because pre-SFAS No. 145, gains/losses from early debt extinguishments were reported below the line, while post-SFAS No. 145, they were reported above the line. After controlling for other identified changes that occur during our sample period, we find that, pre-SFAS No. 145, the market does not respond to these gains/losses, whereas post-SFAS No. 145, it does. This suggests that the market response to gains/losses is associated with their placement in the income statement. Our findings contribute to the literature on the importance of income statement presentation by demonstrating that a line-item position in the income statement has important valuation implications.
Post loss/profit announcement drift
We document a market failure to fully respond to loss/profit quarterly announcements. The annualized post portfolio formation return spread between two portfolios formed on extreme losses and extreme profits is approximately 21 percent. This loss/profit anomaly is incremental to previously documented accounting-related anomalies, and is robust to alternative risk adjustments, distress risk, firm size, short sales constraints, transaction costs, and sample periods. In an effort to explain this finding, we show that this mispricing is related to differences between conditional and unconditional probabilities of losses/profits, as if stock prices do not fully reflect conditional probabilities in a timely fashion.
Corporate Social Responsibility and the Market Reaction to Negative Events: Evidence from Inadvertent and Fraudulent Restatement Announcements
We advance a theory asserting that CSR performance may exacerbate, not necessarily moderate, a company's negative stock price response to negative events. In testing this theory, we hypothesize and find that CSR performance alleviates (magnifies) the immediate negative stock price response to inadvertent (fraudulent) restatement announcements, and that these findings are robust to specifications that consider alternative CSR measures and a multitude of control variables shown by prior research to have explanatory power for the cross-sectional variation in stock returns. Overall, using restatement announcements as a channel through which CSR performance may affect company value, we show, in contrast to prior research, that depending on management conduct leading to the restatement, a company's CSR performance may destroy, not necessarily enhance, firm value. Our findings may, thus, inform researchers, market participants, and regulators. Data Availability: The data used in this study are publicly available from sources indicated in the text.
Investor Sophistication and Patterns in Stock Returns after Earnings Announcements
This study tests whether the observed patterns in stock returns after quarterly earnings announcements are related to the proportion of firm shares held by institutional investors, a variable used by prior research to proxy for investor sophistication. Our findings show that the institutional holdings variable is negatively correlated with the observed post-announcement abnormal returns. Our findings also show that traditional proxies for transaction costs (i.e., trading volume, stock price) as well as firm size have little incremental power to explain post-announcement abnormal returns when institutional holdings is an explanatory variable. If institutional ownership is a valid proxy for investor sophistication, these findings suggest that the trading activity of unsophisticated investors underlies the predictability of stock returns after earnings announcements. However, tests evaluating the validity of institutional holdings as a proxy for investor sophistication yield only mixed results. This calls for caution in interpreting our findings.
Can Twitter Help Predict Firm-Level Earnings and Stock Returns?
Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole. We test whether opinions of individuals tweeted just prior to a firm's earnings announcement predict its earnings and announcement returns. Using a broad sample from 2009 to 2012, we find that the aggregate opinion from individual tweets successfully predicts a firm's forthcoming quarterly earnings and announcement returns. These results hold for tweets that convey original information, as well as tweets that disseminate existing information, and are stronger for tweets providing information directly related to firm fundamentals and stock trading. Importantly, our results hold even after controlling for concurrent information or opinion from traditional media sources, and are stronger for firms in weaker information environments. Our findings highlight the importance of considering the aggregate opinion from individual tweets when assessing a stock's future prospects and value.