主管:中国科学院
主办:中国优选法统筹法与经济数学研究会
   中国科学院科技战略咨询研究院

• •    

数据分析技术进步、信息获取与资本市场效率 ——基于投入驱动与环境驱动的对比分析

王柯蕴, 徐凤敏, 梁循, 卫丽君   

  1. 西安交通大学经济与金融学院, 陕西 710049 中国
    中国人民大学信息学院, 北京 100872 中国
  • 收稿日期:2025-06-23 修回日期:2026-07-30 接受日期:2026-08-03
  • 通讯作者: 徐凤敏
  • 基金资助:
    几类稀疏二次约束二次规划问题的理论、算法与绿色金融应用研究(12471297)

  1. , 710049, China
    , 100872, China
  • Received:2025-06-23 Revised:2026-07-30 Accepted:2026-08-03

摘要: 探究数据分析技术进步如何影响投资者信息获取动机与资本市场效率是落实金融“五篇大文章”的内在要求,更是推动金融高质量发展的关键途径。本文按驱动力不同,将数据分析技术进步分为两类:一类是投资者增加研发投入的微观“投入驱动”型技术进步且研发结果具有不确定性;另一类是完善数据基础设施、健全数据共享机制的宏观“环境驱动”型技术进步。本文构建理性预期模型以对比这两类技术进步对信息获取与资本市场效率产生何种影响。研究发现:(1)在机会成本上升与搭便车效应强化的双重作用下,“投入驱动”型技术进步抑制信息获取并削弱市场效率。(2)“环境驱动”型技术进步虽同受搭便车冲击,但仍可通过提升对资产收益的预测精度来增强信息获取并改善市场效率。(3)将传统技术与数据分析技术在信息获取中的替代关系纳入模型后发现,两类技术进步对资本市场效率的影响方向不变,但风险厌恶、搭便车以及机会成本等因素削弱了投资者对数据分析技术的采纳意愿,进而减弱了两类技术进步对资本市场效率的影响幅度。释放数据分析技术红利,不能仅依赖微观投入驱动,更需在数据制度健全与数据基础设施完善等宏观层面提供系统支撑,方能构建具有中国特色的高质量现代化资本市场。

关键词: 数据分析技术, 投入驱动, 环境驱动, 信息获取, 资本市场效率

Abstract: Exploring how advancements in data analysis technologies influence investors' information acquisition motivations and capital market efficiency is an inherent requirement for the implementation of the "Five Major Articles" in finance and a key pathway to promoting high-quality financial development. This paper categorizes the progress of data analysis technologies into two types based on their driving forces: one is the micro-level "input-driven" technological progress, where investors increase research and development investments, and the results of such investments are uncertain; the other is the macro-level "environment-driven" technological progress, which involves improving data infrastructure and enhancing data-sharing mechanisms. Using a rational expectations model, this paper compares the impacts of these two types of technological progress on information acquisition and capital market efficiency. The findings are as follows: (1) Under the dual effects of rising opportunity costs and intensified free-rider effects, "input-driven" technological progress suppresses information acquisition and weakens market efficiency. (2) Although "environment-driven" technological progress is also impacted by free-rider effects, it can still enhance information acquisition and improve market efficiency by improving the accuracy of asset return predictions. (3) After incorporating the substitution relationship between traditional technologies and data analysis technologies in information acquisition into the model, the direction of the impact on capital market efficiency remains unchanged for both types of technological progress. However, factors such as risk aversion, free-riding, and opportunity costs reduce investors' willingness to adopt data analysis technologies, thereby diminishing the impact of both types of technological progress on capital market efficiency. To fully realize the dividends of data analysis technology, it is not sufficient to rely solely on micro-level input-driven progress; comprehensive support is also needed at the macro level through the improvement of data systems and data infrastructure, which is essential for building a high-quality modern capital market with Chinese characteristics.

Key words: data analytics technologies, input-driven, environment-driven, information acquisition, capital market efficiency