Professors Xiaolei Liu and Yingguang Zhang from Peking University’s Guanghua School of Management have recently published a paper in the Review of Finance, a prestigious academic journal. Their coauthored work, titled CEO Turnover, Sequential Disclosure, and Stock Returns, offers novel insights into the impact of CEO changes on stock prices, a long-debated topic within the financial sector. This study combines classical econometric approaches with generative AI（ChatGPT） to shed light on how firms’ expectation management influence stock valuation.

Research Background

The effects of CEO turnover on company stock prices have been a contentious topic in financial research, with varying conclusions in existing literature. The paper addresses the strategic information disclosure behaviors of incoming CEOs and their impact on stock price movements. In particular, it highlights the importance of the first information disclosure events following a CEO turnover, which serve as a critical window for understanding stock price movements.

Study Overview

The study innovatively combines classical financial econometrics with text analysis techniques using large language models like ChatGPT. By analyzing Management Discussion and Analysis (MD&A) sections in company 10-K/Q filings, the researchers quantify the short-term and long-term optimism of CEOs and their strategic disclosure tendencies. The study finds that following forced CEO turnovers, stock prices exhibit a unique V-shaped reaction pattern around initial disclosure events: significant declines during the disclosure, followed by substantial rises over the subsequent year.

Innovative Contributions

The research reveals that new CEOs often engage in strategic disclosure behaviors. Initially, they release negative news to lower expectations, attributing poor short-term performance to previous management. Subsequently, they release optimistic long-term growth prospects, leading to gradual stock price recovery. This pattern is more pronounced in companies with high stock price volatility or those experiencing high returns before disclosures. The findings challenge the efficient market hypothesis by showing that investors may systematically overlook strategic motivations behind company disclosures.

Conclusions

The study concludes that strategic information disclosure by new CEOs significantly influences stock returns. Investors often fail to recognize the strategic motives behind disclosures, and this form of investor gullibility can lead to significant distortion in stock prices. This insight broadens the understanding of behavioral finance, emphasizing the role of bounded rationality in market dynamics. The research provides valuable implications for understanding the market impact of managerial communication strategies and offers methodological advancements in analyzing unstructured financial texts using large language models.

About the Authors

Xiaolei Liu is a Professor in the Finance and Accounting Departments at Peking University's Guanghua School of Management. She holds a Ph.D. from the University of Rochester, where her dissertation won the Best Corporate Finance Paper Award from the Western Finance Association and the Best Dissertation Award from the Southwestern Finance Association. Her research focuses on financial markets and corporate finance, with publications in leading journals such as the Journal of Political Economy, Journal of Finance, and Review of Financial Studies. Before joining Guanghua, she taught at the Hong Kong University of Science and Technology as an Associate Professor and was a visiting professor at Cheung Kong Graduate School of Business.

Yingguang Zhang is an Assistant Professor of Finance at Peking University, Guanghua School of Management. He received his Ph.D. in Finance from the Marshall School of Business at the University of Southern California in 2019. Prior to his doctoral studies, he obtained dual honors bachelor's degrees in Economics and Statistics from the University of California, Berkeley. His research interests include asset pricing, behavioral finance, and AI/machine learning, with a focus on the impact of market participants' expectations on asset prices. His research has been published in reputable academic journals such as the Review of Financial Studies, Review of Finance, and Pacific-Basin Finance Journal.