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论文编号：	15476
作者编号：	2120233683
上传时间：	2025/6/12 15:10:51
中文题目：	企业数据资产信息非对称性披露对分析师预测行为的影响研究
英文题目：	Research on the Impact of Asymmetric Disclosure of Corporate Data Asset Information on Analysts'''' Forecasting Behavior
指导老师：	李姝
中文关键字：	数据资产；非对称性信息披露；“言多数穷”；分析师预测行为
英文关键字：	Data Assets; Asymmetric Information Disclosure; “More Textual Information but Less Actual Value”; Analysts’ Forecasting Behavior
中文摘要：	在“大智移云”技术深度融合的时代背景下，数据资源成为企业价值体系中的关键要素，数据要素市场化进程也在不断加快。2023年，随着《企业数据资源相关会计处理暂行规定》和《数据资产评估指导意见》的出台，数据资产被正式确认为资产负债表项目。然而，作为一种新兴的资产类型，数据资产的信息披露机制尚未成熟，企业在披露相关信息时，可能存在文本陈述与实际价值不符的现象，即“言多数穷”的非对称性披露模式，这一复杂的信息结构会误导相关信息的使用者，使其对企业的盈利能力和潜在价值产生认知偏差。随着数字中国建设的深入发展和数据资产化进程的不断推进，企业数据资产的信息披露成为学术界和实务界关注的重点议题，而分析师作为资本市场中的“信息中介”，其发布的盈余预测和投资建议受到广泛关注，也成为学术研究中的重要研究对象。因此，本文将探讨数据资产信息非对称性披露与分析师预测行为的关系，试图对相关研究进行有益拓展。 本文在梳理相关文献和理论的基础上，以2007-2023年沪深A股上市公司为样本，研究了企业数据资产信息非对称性披露对分析师预测行为的影响。研究结果发现：第一，企业数据资产信息的非对称性披露显著降低了分析师关注度、预测准确度，并加剧预测分歧度。进一步机制分析表明，数据资产信息的非对称性披露通过加剧信息不对称程度和企业经营风险，迫使分析师规避对企业的跟踪，并且可能误导分析师的盈余预测行为，导致其预测质量下降。第二，本文在调节效应分析中发现，数据资产信息非对称性披露的影响在不同企业内外部环境和分析师特性下有着显著差异：数字化转型程度较高、企业整体信息披露质量较差、媒体报道较频繁、明星分析师跟踪数量较多的企业，数据资产信息的非对称性披露更容易对分析师预测行为造成负面影响。本文的研究为理解企业数据资产信息披露的经济后果提供了新的视角，并为监管机构制定数据资产信息披露规范，推动数据资产入表提供了理论支持，对于帮助分析师识别企业非对称性信息披露，进而提升资本市场资源配置效率具有重要意义。
英文摘要：	In the context of the deep integration of “Big Data, Artificial Intelligence, Mobile Internet, and Cloud Computing” technologies, data resources have become a key element in the composition of enterprise value, and the process of marketization of data elements is accelerating. 2023, with the introduction of the “Interim Provisions on Accounting Treatment for Enterprise Data Resources” and the “Guidance on Data Asset Valuation”, data assets were formally recognized as balance sheet items. However, as an emerging asset type, the information disclosure mechanism of data assets has not yet matured, and when enterprises disclose relevant information, there may be discrepancies between textual statements and actual values, i.e., an asymmetric disclosure pattern of “more textual information but less actual value”, which is a complex information structure that can mislead the users of relevant information and create cognitive bias on the profitability and potential value of the enterprise. This complex information structure may mislead users of relevant information, causing them to have a biased perception of the profitability and potential value of the enterprise. Under the background of vigorously promoting the construction of digital China and continuously promoting the realization of data assets, the information disclosure of enterprise data assets has become one of the focuses of academic and practical circles, and analysts, as an “information intermediary” in the capital market, have been widely concerned about their release of corporate surplus forecasts and investment recommendations, which has become an important object of research in academic studies. It has also become an important research object in academic research. Therefore, this paper explores the relationship between asymmetric disclosure of data asset information and analysts' forecasting behavior, and tries to expand the relevant research. Based on combing relevant literature and theories, this paper investigates the impact of asymmetric disclosure of corporate data asset information on analysts' forecasting behavior with a sample of A-share listed companies in China from 2007 to 2023. The results of the research find that, first, asymmetric disclosure of corporate data asset information significantly reduces analysts' attention, forecast accuracy, and increases forecast divergence. Further mechanism analysis shows that asymmetric disclosure of data asset information forces analysts to avoid tracking firms by exacerbating the degree of information asymmetry and increasing firms' business risks, and may mislead analysts' surplus forecasting behavior, leading to a decline in their forecasting quality. Second, in the moderating effect analysis, this paper finds that the impact of asymmetric disclosure of data asset information varies significantly across firms' internal and external environments and analysts' characteristics: firms with a higher degree of digital transformation, poorer firms' overall disclosure quality, more frequent media reports, and a higher number of star analysts' tracking, asymmetric disclosure of data asset information is more likely to have a negative impact on analysts' forecasting behavior. The research provides a new perspective for understanding the economic consequences of corporate data asset disclosure, and provides theoretical support for regulators to formulate norms for data asset disclosure and to promote the inclusion of data assets in the balance sheet, which is of great significance in helping analysts to identify and deal with the asymmetric disclosure structure of corporations, and then to enhance the efficiency of resource allocation in the capital market.
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