The Information Content of Operational Effectiveness
with Mary E. Barth and Doron Israeli
Published in Journal of Business Finance and Accounting
Selected for the Journal of Business Finance and Accounting 2025 Capital Markets Conference
Abstract: We address whether and why a firm’s operational effectiveness, OpEff, has information content for investors and what role that information plays in the price discovery process at quarterly earnings announcements. We measure OpEff using the cash conversion cycle (CCC) multiplied by –1, such that higher OpEff reflects better operational effectiveness. Higher OpEff is associated with higher abnormal stock returns and trading volume at earnings announcements and with higher future earnings and cash flows, which helps explain the positive return and volume relations. Higher OpEff also is associated with larger post-earnings-announcement drift and less timely incorporation of information in earnings announcements into stock prices. However, this relation largely is attributable to firms that announce bad earnings news. Together, we infer that operational effectiveness is informative to investors because it comprises forward-looking information about earnings and cash flows and that announcements of improvements in OpEff along with bad earnings news impede the price discovery process.
Does CSR engender trust? Evidence from investor reactions to corporate disclosures
with Cassandra Estep, Doron Israeli, and Suhas A. Sridharan
Abstract: We investigate whether investor perceptions of firm’s corporate social responsibility activity (CSR) affects investor trust as reflected in disclosure credibility. We use stock price discovery as a proxy for disclosure credibility and, therefore, examine the relation between CSR and stock price discovery at earnings announcements. We find robust evidence that firms with stronger CSR enjoy faster incorporation of earnings news into stock prices. This faster price discovery exists only for positive earnings news, reinforcing the perspective that CSR leads investors to maintain a positive view of the firm. In further analyses, we show that this effect is primarily driven by the environmental dimension of CSR, consistent with CSR enhancing trust through credible risk mitigation and disciplined management. We also distinguish between trust that is externally endowed by the surrounding social environment and trust that is earned through firm-level actions, and show that our results are consistent with CSR fostering earned trust rather than reflecting broader social capital. We also conduct an experiment that provides evidence of the causal effects of a firm’s CSR on investor perceptions of credibility. We also show that high CSR firms experience lower investor uncertainty, more trading volume, and stronger earnings response coefficients.
Firm's Tweets and Stock Price Discovery
with Doron Israeli and Venkat Subramanian
Abstract: Do firms’ tweets improve stock price discovery at quarterly earnings announcements? We address this question using a comprehensive sample of 148,656 tweets released by 855 S&P 1500 firms from 2008 through 2021. Firms’ tweets are associated with stronger stock price and volume reactions to earnings announcements. In addition, firms’ tweets reduce investor uncertainty, increase the timeliness and efficiency with which stock prices reflect information in earnings announcements, and reduce the post-earnings-announcement drift. We document that firms’ tweets improve stock price discovery by enhancing firm visibility and increasing retail investor trading, which facilitates faster incorporation of information into stock prices. Our inferences hold in a propensity score matched sample, where firms that use Twitter are matched with similar firms that do not. Our findings are of interest to regulators who wish to improve the informativeness of security prices, investors who are interested in information that affects prices and volume, and managers who seek channels to communicate with investors.
Sentiment Management: AI-based Evidence from Earnings Guidance
with Doron Israeli and Ron Kasznik
Abstract: We investigate whether firms manage sentiment within their disclosures, and whether such sentiment management plays a role in shaping investor reactions to information. Using FinBERT, an AI-based pre-trained large language model, we measure sentiment separately in the title, various portions of the text, and the full text of earnings guidance. We focus on earnings guidance because firms have considerable discretion over the type, level, and structure of sentiment they include in these disclosures. Our findings indicate firms amplify (suppress) the tone of positive (negative) information in the title and enhance the positive sentiment in the first portions of earnings guidance text. We do not find similar patterns in Wall Street Journal (WSJ) articles or titles generated by ChatGPT-4o for the same earnings guidance text. We show that this sentiment management is more pronounced among firms that miss analysts’ earnings forecasts and firms with lower scrutiny and litigation risk. We also document that sentiment in the title, as well as in the first portions of the text, helps explain investor reactions to information in earnings guidance. Together, our findings suggest firms strategically embed sentiment within corporate disclosures, and investors incorporate this sentiment into stock prices and trading activity.
Disclosure in the face of peer scandals: evidence from the Volkswagen emissions scandal
with Claudia Imperatore and Saverio Bozzolan