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                    【金融系學術講座】Politics and Specificity of Information
                    日期: 2019-04-30

                    嘉賓簡介:

                    Prof. Zhang Tianyu is a Professor of School of Accountancy at The Chinese University of Hong Kong (CUHK). He got his BA from Nankai University, MA from Shanghai University of Finance and Economics and PhD from Hong Kong University of Science and Technology. Before joining CUHK, Prof. Zhang worked for City University of Hong Kong from 2005-2010. Prof. Zhang has published research articles in finance, accounting and economics journals such as Journal of Finance, Journal of Financial Economics, The Accounting Review, Journal of Accounting and Economics, Journal of Accounting Research, Contemporary Accounting Research, and Journal of Law, Economics and Organisation. His research was featured in the Wall Street Journal, Associated Press, Business Wire, and Caixin. Prof. Zhang sits on the editorial board of The Accounting Review, China Journal of Accounting Research, and China Journal of Accounting Studies.

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                    講座摘要

                    This paper examines whether and how politics shapes the kind of information that enhances analysts’ forecast accuracy in a relational economy. Since political influence is exerted on firms primarily through relationships, information about firms’ performance is highly specific. Even for firms that are within the same industry, these relationships can differ significantly. We posit that politics increases the idiosyncrasy of analysts’ information that is accuracy-enhancing. Using Latent Dirichlet Allocation (LDA), a topical modeling method, on a comprehensive sample of 87,332 reports of Chinese financial analysts from 2010 to 2015, we find that when political influence on firms increases, the idiosyncratic topics in the analysts’ reports are more positively associated with their relative forecast accuracy. However, we do not find that politics influences the relation between industry-specific topics and forecast accuracy. Finally, we validate our LDA measures using the earnings component model in Ball and Brown (1967) and the stock return synchronicity model in Morck et al. (2000).


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