T.J. Wong

We develop an empirical proxy for the differential treatment of local and foreign investors using translation differences in public disclosure as observable indicators that reflect both public and non-public interactions. We validate this measure with a field experiment that examines how companies respond to private inquiries from local and foreign investors. We show that differential treatment results in significant information asymmetry between local and foreign investors, as measured with stock illiquidity. It also leads foreign analysts to possess lower quality information than local analysts following the same firm, as manifested in their forecast errors. Firms respond to strategic incentives for differential treatment relating to government subsidies and capital raising, and thus differential treatment is not just a byproduct of resource constraints. Our results highlight the role of differential treatment as one driver of local information advantage.

Relational economies rely on locally-based social networks for contracting. We posit that auditor locality specialization is an important competency for understanding client firms’ social networks and auditing their relational contracts. However, specializing in a locality may lead to collusion with clients, thereby weakening auditor independence and reducing audit quality. Using Chinese data, we find that the clients of engagement partners with greater locality specialization are less likely to report financial reporting irregularities (as captured by misstatements and accounting-related fraud). Further, this result is stronger for clients with more relational contracts and clients who are subject to greater external monitoring. In addition, locality specialists are more accurate in issuing modified opinions to clients that subsequently report financial reporting irregularities. Taken together, our findings suggest that the improvements to auditor competency from locality specialization outweigh the potential costs of reduced independence.

This paper examines whether social media moderates the information bias in the market by generating less optimistic information when the state-controlled traditional media is positively biased. Using a comprehensive sample of corporate news articles of Chinese newspapers and posts of an online stock forum, East Guba, from 2009 to 2016, we find that East Guba’s tone is less positively associated with that of the newspapers for the same firm on the same day when the newspapers are expected to be more optimistically biased. This decline in association in tone is significantly larger since the 2015 political shock which increased the suppression of negative corporate news by traditional media. Finally, when the tone of the newspapers deviates positively from that of East Guba, the newspaper articles are perceived by the market to be less credible as reflected in the significantly attenuated stock return response.

Prior research documents that official newspapers in China publish business news articles that are more positively biased and contain greater political content, and as a result, are relatively less informative than articles published by non-official newspapers. We posit and find that despite these political biases, official newspapers serve an important informational role by providing more value-relevant industry and market wide information to the market than non-official newspapers. We also show that the strength of this information role varies based on the political proximity of the newspaper to the central government and intensifies during highly politicized time periods.

When emerging market firms disclose relationship-based transactions, they face a trade-off in which greater transparency may help lower their cost of capital at the cost of revealing proprietary information. We find that firms overcome this challenge by relying on analysts within their private networks (i.e., connected analysts) who, through repeated interaction, can better verify relationship-based transactions. Using Chinese firms, we show that firms with more connected analysts have more accurate consensus forecasts and lower forecast dispersion. When a connected analyst departs and stops covering a firm, the accuracy and informativeness of the unconnected analysts’ forecasts decrease, suggesting information spillovers from a connected analyst to analysts outside the private network. We find a potential mechanism through which information spillover from a connected analyst occurs: through common institutional clients. The findings suggest that embedded financial analysts—those who share close connections with firms and analysts—serve as a channel for disseminating proprietary, hard-to-verify information.