The impacts of political uncertainty on asset prices: Evidence from the Bo scandal in China

By Laura Xiaolei Liu, Haibing Shu, K. Wei. 2017

315 citations recorded by the source corpus.

Models of political risk predict that increases in political uncertainty cause stock prices to fall, especially for politically sensitive firms. We use the event of the Bo Xilai political scandal in 2012 in China as an exogenous shock to identify the impact of political uncertainty on asset prices. We document that the Bo scandal caused a significant drop in stock prices, especially for firms that are more politically sensitive. Further analysis shows that the stock price drop is mainly driven by a change in discount rate, providing strong support for the existence of priced political risk.

Subfields: Economics and Econometrics, Finance, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/8fe08eacde54c25571bb4f8c5ad9343ccead4e63

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Citations in the corpus

Textual analysis in finance (2019, background): “Some authors uses quantitative variables as a proxy for uncertainty (Carrière-Swallow and Céspedes, 2013; Segal, Shaliastovich, and Yaron, 2015) or events that cause uncertainty shocks (Liu, Shu, and Wei, 2017).”
Investor Rewards to Climate Responsibility: Stock-Price Responses to the Opposite Shocks of the 2016 and 2020 U.S. Elections (2021, unknown): “In the COVID-19 crash, companies fared better if they had strong environmental and social (ES) performances (Albuquerque et al., 2020; Garel and Petit-Romec, 2021), though this result has been the subject of some debate (Demers et al., 2020; Mahmoud and Meyer, 2020).”
The Pollution Premium (2023, background): “With respect to political uncertainty, Santa-Clara and Valkanov (2003) relate the equity risk premium to political cycles, and Liu et al. (2017) provide direct evidence that stock prices of politically sensitive firms respond more to political uncertainty.”
On the fast track: Information acquisition costs and information production (2020, result): “comparable to those reported by Liu et al. (2017). In comparison, firms in the forecast-errors sample (Panel B) are larger (23.”