Business School, Chengdu University, Chengdu, China.
Sichuan Provincial Housing Provident Fund Management Center, Chengdu, China.
PLoS One. 2023 Oct 16;18(10):e0290126. doi: 10.1371/journal.pone.0290126. eCollection 2023.
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.
基于中国上海和深圳证券交易所的中国 A 股上市公司 2014 年至 2021 年的数据，本文探讨了共同机构投资者与管理盈余预测质量之间的关系。研究采用多元线性回归模型，实证发现共同机构投资者对盈余预测的精确性有积极影响。本文还使用图神经网络来预测盈余预测的精确性。我们的研究结果表明，共同机构投资者对管理层发布广泛盈余预测的行为形成外部监督，有助于提高盈余预测的风险预警功能，促进中国资本市场管理层信息披露的可持续发展。本文的边际贡献之一是丰富了共同机构持股的经济后果相关文献。然后，用于预测管理预测质量的神经网络方法增强了对机构投资者行为和管理盈余预测的研究。第三，本文呼吁加强机构投资者之间的信息共享和流通，以减少投资者和管理层之间的信息不对称。
