Political Incentives to Suppress Negative Information: Evidence from Chinese Listed Firms

By Joseph D. Piotroski, T. Wong, Tianyu Zhang. Journal of Accounting Research, 2015

587 citations recorded by the source corpus.

ABSTRACT This paper tests the proposition that politicians and their affiliated firms (i.e., firms operating in their province) temporarily suppress negative information in response to political incentives. We examine the stock price behavior of Chinese listed firms around two visible political events—meetings of the National Congress of the Chinese Communist Party and promotions of high‐level provincial politicians—that are expected to asymmetrically increase the costs of releasing bad news. The costs create an incentive for local politicians and their affiliated firms to temporarily restrict the flow of negative information about the companies. The result will be fewer stock price crashes for the affiliated firms during these event windows, followed by an increase in crashes after the event. Consistent with these predictions, we find that the affiliated firms experience a reduction (an increase) in negative stock return skewness before (after) the event. These effects are strongest in the three‐month period directly preceding the event, among firms that are more politically connected, and when the province is dominated by faction politics and cronyism. Additional tests document a significant reduction in published newspaper articles about affected firms in advance of these political events, suggestive of a link between our observed stock price behavior and temporary shifts in the listed firms’ information environment.

Subfields: Accounting, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/c971e23f0f7e8cc2595baed96f90f41b347378fb

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In Accounting

  1. Xiaorong Li
  2. Xue Wang
  3. Steven Shuye Wang
  4. Jeong‐Bon Kim
  5. M. Maung
  6. Craig Wilson
  7. Donghua Chen
  8. Xiaobo Tang

In Strategy and Management

  1. Xiaorong Li
  2. Xue Wang
  3. Steven Shuye Wang
  4. Jeong‐Bon Kim
  5. M. Maung
  6. Craig Wilson
  7. Donghua Chen
  8. Xiaobo Tang

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