NDLI: Optimal Reporting Systems with Investor Information Acquisition

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Optimal Reporting Systems with Investor Information Acquisition

Content Provider	Scilit
Author	Huang, Zeqiong
Copyright Year	2016
Description	Journal: SSRN Electronic Journal This paper analyzes a manager’s optimal ex-ante reporting system using a Bayesian persuasion approach (Kamenica and Gentzkow (2011)) in a setting where investors affect cash flows through their decision to finance the firm’s investment opportunities, possibly assisted by the costly acquisition of additional information (inspection). I examine how the informativeness and the bias of the optimal system are determined by investors’ inspection cost, the degree of incentive alignment between the manager and investors, and the prior belief that the project is profitable. I find that a mis-aligned manager’s system is informative only when the market prior is pessimistic and is always positively biased. As investors’ inspection cost decreases, this bias decreases and the optimal system becomes more conservative. In contrast, a well-aligned manager’s system is fully revealing when investors’ inspection cost is high, and is counter-cyclical to the market belief when the inspection cost is low: It is positively (negatively) biased when the market belief is pessimistic (optimistic). I explore the extent to which the results generalize to a case with managerial manipulation and discuss the implications for investment efficiency. Overall, the analysis describes the complex interactions among determinants of firm disclosures and governance, and offers explanations for the mixed empirical results in this area.
Related Links	https://dukespace.lib.duke.edu/dspace/bitstream/10161/12214/1/Huang_duke_0066D_13423.pdf https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2799421
ISSN	10914358
e-ISSN	15565068
DOI	10.2139/ssrn.2799421
Journal	SSRN Electronic Journal
Language	English
Publisher	Elsevier BV
Publisher Date	2016-07-01
Access Restriction	Open
Subject Keyword	Journal: SSRN Electronic Journal Reporting System Information Acquisition
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Psychiatry and Mental Health

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