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前瞻性信息披露与分析师盈余预测——基于文本分析和机器学习的证据 被引量:3

Forward-looking Information Disclosure and Analysts’Earnings Forecasts:Evidence from Text Analysis and Machine Learning
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摘要 前瞻性信息是管理层对公司未来发展前景进行展望的预测性信息。本文借助文本分析和机器学习量化年报的前瞻性信息,研究其对分析师盈余预测行为的影响。研究发现,年报前瞻性信息披露频率越高,分析师盈余预测误差和分歧度越低,即前瞻性信息披露能够提高分析师盈余预测准确性。分组检验显示,在盈余质量和文本可读性较高公司,前瞻性信息披露对分析师盈余预测准确性的积极效应更加显著。机制检验表明,前瞻性信息披露主要通过缓解信息不对称、促进分析师实地调研,来提高分析师盈余预测准确性。进一步研究发现,前瞻性信息能够预测公司未来三年的基本面变化,如盈余增长、创新产出和投资规模。研究结果表明,前瞻性信息披露是有信息含量的,能够为分析师等信息使用者提供决策有用的增量信息,提高资本市场信息效率。研究结论在一定程度上肯定了年报前瞻性信息披露的积极效应,为监管机构和上市公司改进信息披露提供了经验证据。 Information disclosure is the institutional basis of the healthy development of the capital market.With the increasing improvement of investors’ability,information disclosure based solely on historical operations cannot fully meet their information needs.Consequently,CSRC revised the information disclosure principles in 2007,which required listed companies to provide outlooks and forecasts on their future development in annual reports.Forward-looking information can forecast a company’s future development strategies,business plans,earnings risks,and opportunities and challenges,which contains a significant value for investors to estimate future cash flows and risks.Compared with historical information and quantitative information,it is more obscure and complex,so it requires more professional ability to interpret such information.As professional information users and important information intermediaries of the capital market,analysts have been proven to have advantages in information mining and interpretation.Therefore,can analysts effectively mine and interpret forward-looking information?Taking A-share listed companies from 2007 to 2020 as the sample and using text analysis and machine learning to measure forward-looking information disclosure,this paper investigates the above question.The results show that the higher the frequency of forward-looking information disclosure,the higher the analysts’earnings forecast accuracy.In firms with high earnings quality and readability,the above positive relationship is more significant.The mechanism test indicates that forward-looking information disclosure can improve the earnings forecast accuracy by alleviating information asymmetry and increasing analysts’site visits.Further research also reveals that forward-looking information can successfully predict firms’fundamental changes in the future,which indicates that such disclosure has its information value and can provide incremental information for analysts and other users of the capital market,which in turn can improve the information efficiency of the capital market.The possible contributions of this paper are as follows:First,based on the perspective of analysts’earnings forecast,it expands the research on the economic consequences of forward-looking information disclosure.Currently,there is no academic consensus on whether such disclosures are informative,and the findings can promote the understanding of this issue.Second,from the forward-looking disclosure perspective,it enriches the literature on the influencing factors of analysts’earnings forecasts.Research from abroad suggests that forward-looking disclosure can improve the quality of analysts’earnings forecasts,but whether the conclusion applies to China’s capital market has not been verified.Therefore,this paper supplements the empirical evidence from China.Third,it has certain enlightenment for listed companies to regulate forward-looking information disclosure,for regulators to deepen the reform of capital market information disclosure,and for analysts to focus on forward-looking information in annual reports when making earnings forecasts.
作者 刘瑶瑶 路军伟 Liu Yaoyao;Lu Junwei(School of Economics and Management,China University of Petroleum,Qingdao 266580,China;School of Management,Shandong University,Jinan 250100,China)
出处 《外国经济与管理》 CSSCI 北大核心 2023年第10期101-115,共15页 Foreign Economics & Management
基金 国家自然科学基金项目(72072102) 山东大学人文社科青年团队项目(IFYT2001)。
关键词 前瞻性信息披露 分析师盈余预测 信息不对称 文本分析 机器学习 forward-looking information disclosure analysts’earnings forecast information asymmetry text analysis machine learning
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