【摘要】 上市公司信息质量影响资本市场资源配置效率。管理层讨论与分析(MD&A)是极其重要的上市公司信息,其语言表述灵活多变,不同语言特征必然带来不同的经济后果。因而管理层通过不同的语言表述形式,向市场传递公司价值的信号。本文以哈贝马斯言语有效理论为基础,借助文本挖掘技术,对MD&A中语言特征进行了探索性研究和系统分析,提出五种语言特征测度指标——可读性、业绩自利性归因、匹配信息密度、自我指涉度、前瞻性深度,以此衡量MD&A言语有效性。通过选择2010年188家中国创业板上市公司MD&A为样本,实证检验五种指标与公司未来财务业绩相关性。研究发现:①MD&A可读性、匹配信息密度、前瞻性深度与未来财务业绩正相关。②业绩自利性归因与未来财务业绩负相关。③自我指涉度与未来财务业绩关系不显著。④管理层并不是孤立的运用单一MD&A语言表述形式,而是融为整体战略性传递公司相关信息。本文从跨学科的角度,在上市公司信息披露的研究中引入言语有效交流理论,揭示了文字背后管理层微妙的心理动机,为把握管理层的心理和行为规律提供实证支持。更多还原

【Abstract】 The information quantity of listed companies influences the resource allocation efficiency of capital market. " Management discussion and analysis",flexibility and multi-meanings,plays a very important role in the listed companies.Different level of linguistic feature provides different incremental information.Managements transfer signal related to company value by passing different level of linguistic feature.Based on the Theory of Communicative Action and text mining technology,this paper puts forward five indexes——readability、the self-serving attribution,the density of prospective information、the self-reference index、the depth of prospective information——to measure the effective of language.Using the data of 188 growth enterprise in China,we take an empirical test of the relationship of the five indexes and future performance.The results indicate that ①the readability,the density of prospective information,the depth of prospective information has a positive impact on future performance;②The self-serving attribution has a negative impact on future performance;③The self-reference index has a not significant impact on future performance;④Management transmit a signal for corporate value using the overall linguistic features strategic.In this paper,we analyze the information disclosure of listed companies by the introduction of speech communication theory from an interdisciplinary perspective.The result reveals the subde psychological motivation behind the words and provides empirical support for grasping psychological and pattern of management.更多还原