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讲座:Going Digital: The Causal Effect of Information Technology on Financial Reporting

发布者:会计系    发布时间:2020-11-18

题 目:Going Digital: The Causal Effect of Information Technology on Financial Reporting

嘉 宾:Yibin Liu, Ph.D. Candidate, University of California, San Diego

主持人:夏立军 教授  上海交通大学安泰经济与管理学院会计系

时 间:2020年11月23日(周一)10:00-11:30

会议方式:ZOOM 会议(校内师生如需会议号和密码,请于11月22日中午12点前发送电邮至accounting@acem.sjtu.edu.cn获取)

内容简介:

The paper studies the causal effect of modern information technologies on financial reporting. Ten groups of public firms were mandated by the U.S. SEC to transition from paper to electronic filings on the EDGAR system from 1993 to 1996, which provides exogenous variations in investors' information acquisition cost. Firms with electronic filings on EDGAR receive higher public scrutiny, which deters managers from biasing earnings. On the other hand, the paper documents a significant increase in the earnings response coefficient for firms on EDGAR,  which boosts up managers' marginal benefit of biasing earnings. The two countervailing effects of higher public scrutiny facilitated by the EDGAR system result in an insignificant change in total earnings management after firms were phased-into EDGAR. However, firms substitute away from opportunistic discretionary accruals to real activity based earnings manipulations (e.g., overproducing inventory, cutting R&D and advertising expenses), which is an important unintended consequence of the EDGAR system. Lastly, firms with low (high) ex-ante public scrutiny increase (does not change) total earnings management, consistent with the prediction of an inverse-U relation between ex-ante public scrutiny and reporting bias by Samuels, Talyor, and Verrecchia (2020). The results shed light on a crucial trade-off faced by securities regulators worldwide in informing corporate outsiders and incentivizing corporate insiders to make socially optimal operational decisions. 

演讲人简介:

Liu works on a wide range of applied microeconomics issues. A unifying theme of my research has been using machine learning techniques, modern econometric methods for causal inference, and novel data and quasi-experimental settings to answer policy-relevant questions including: factors that shape economics agents' processing of financial information; the consequences of economics agents' information processing costs on corporate actions; CEO partisanship and management forecasts.

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