Article
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Preserved in Portico This version is not peer-reviewed
Why Does Cross-Sectional Analyst Coverage Incorporate Market-Wide Information?
Version 1
: Received: 7 March 2024 / Approved: 8 March 2024 / Online: 8 March 2024 (15:31:13 CET)
Version 2 : Received: 12 March 2024 / Approved: 12 March 2024 / Online: 12 March 2024 (08:43:58 CET)
Version 2 : Received: 12 March 2024 / Approved: 12 March 2024 / Online: 12 March 2024 (08:43:58 CET)
A peer-reviewed article of this Preprint also exists.
Hou, Y.; Hu, C. Why Does Cross-Sectional Analyst Coverage Incorporate Market-Wide Information? Entropy 2024, 26, 285. Hou, Y.; Hu, C. Why Does Cross-Sectional Analyst Coverage Incorporate Market-Wide Information? Entropy 2024, 26, 285.
Abstract
This paper shows that the empirical distribution of cross-sectional analyst coverage in China's stock markets follows an exponential law in a given month from 2011 to 2020. The findings hold in both the emerging (Shanghai) and the developed market (Hong Kong). Moreover, the unique distribution parameter (i.e., mean) is directly related to the amount of market-wide information. Average analyst coverage exhibits a significant negative predictive power for stock-market uncertainty, highlighting the role of security analysts in diminishing the total uncertainty. The exponential law can be derived from the maximum entropy principle (MEP). When analysts, who are constrained by average ability in generating information (i.e., the first-order moment), strive to maximize the amount of market-wide information, this objective yields the exponential distribution. Contrary to the conventional wisdom that security analysts specialize in the generation of firm-specific information, empirical findings suggest that analysts primarily produce market-wide information for 25 countries. Nevertheless, it remains unclear why cross-sectional analyst coverage reflects market-wide information, this paper provides an entropy-based explanation.
Keywords
Analyst coverage; Exponential distribution; Market-wide information; Maximum entropy
Subject
Social Sciences, Decision Sciences
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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