Common institutional investors and the quality of management earnings forecasts—Empirical and machine learning evidences
Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model a...
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| Veröffentlicht in: | PloS one 2023-10, Vol.18 (10), p.e0290126-e0290126 |
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| creator | Yang, Shanshan Li, Xiaohan Jiang, Zhenhua Xiao, Man |
| description | Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management. |
| doi_str_mv | 10.1371/journal.pone.0290126 |
| format | Article |
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The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0290126</identifier><identifier>PMID: 37844110</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Artificial intelligence ; Behavior ; Biology and Life Sciences ; Capital market ; Capital markets ; China ; Competition ; Computer and Information Sciences ; Corporate governance ; Deep learning ; Disclosure ; Economic development ; Evaluation ; Executive compensation ; Graph neural networks ; Hypotheses ; Influence ; Information management ; Institutional investments ; Machine learning ; Management ; Market positioning ; Neural networks ; Physical Sciences ; Profits ; Regression models ; Research and Analysis Methods ; Securities markets ; Social Sciences ; Stock exchanges ; Stockholders ; Sustainable development ; Variables</subject><ispartof>PloS one, 2023-10, Vol.18 (10), p.e0290126-e0290126</ispartof><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Yang et al. 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The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Behavior</subject><subject>Biology and Life Sciences</subject><subject>Capital market</subject><subject>Capital markets</subject><subject>China</subject><subject>Competition</subject><subject>Computer and Information Sciences</subject><subject>Corporate governance</subject><subject>Deep learning</subject><subject>Disclosure</subject><subject>Economic development</subject><subject>Evaluation</subject><subject>Executive compensation</subject><subject>Graph neural networks</subject><subject>Hypotheses</subject><subject>Influence</subject><subject>Information management</subject><subject>Institutional investments</subject><subject>Machine 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one</jtitle><date>2023-10-16</date><risdate>2023</risdate><volume>18</volume><issue>10</issue><spage>e0290126</spage><epage>e0290126</epage><pages>e0290126-e0290126</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>37844110</pmid><doi>10.1371/journal.pone.0290126</doi><tpages>e0290126</tpages><orcidid>https://orcid.org/0000-0001-6470-0557</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Artificial intelligence Behavior Biology and Life Sciences Capital market Capital markets China Competition Computer and Information Sciences Corporate governance Deep learning Disclosure Economic development Evaluation Executive compensation Graph neural networks Hypotheses Influence Information management Institutional investments Machine learning Management Market positioning Neural networks Physical Sciences Profits Regression models Research and Analysis Methods Securities markets Social Sciences Stock exchanges Stockholders Sustainable development Variables |
| title | Common institutional investors and the quality of management earnings forecasts—Empirical and machine learning evidences |
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