衍生工具应用能改善资本市场信息环境吗?——基于分析师预测行为的视角

Can Derivative Usage Improve the Information Environment of Capital Market? From the Perspective of Analysts’ Forecasting Behavior

本文通过文本搜索获得企业衍生工具运用情况,在此基础上研究了企业衍生工具应用对分析师跟踪和预测信息结构的影响,并且进一步区分了明星分析师和非明星分析师预测行为的差异。研究结果表明,企业运用衍生工具会导致分析师跟踪数量显著减少,公共信息精度下降,从而对资本市场信息环境产生负面影响。但是,进一步我们发现分析师跟踪数量的减少和公共信息精度的下降是由非明星分析师所致,明星分析师跟踪数量和公共信息精度没有显著变化,私有信息精度反而增加;而非明星分析师跟踪数量显著减少,公共信息精度明显下降。本文的结论有助于我们进一步理解衍生工具应用对资本市场信息环境的影响,以及分析师在信息传递中的作用机制。

Derivatives are important tools by which companies hedge risks related to exchange rates, raw materials, finished products, or interest rate fluctuations. In recent years, derivatives have been applied on an increasingly large scale. However, it is difficult for investors and analysts to understand the nature of derivative usage, including the diverse motivations for using derivatives, the complexity of their economic substance, and the complexity of accounting treatment of derivatives, especially hedge accounting. Analysts’ following and predicting behavior determines whether a company’s management capabilities and earning performance are smoothly and adequately transferred to the capital market. Therefore, the influence of derivative usage on analysts’ forecasts is an urgent issue. In addition, compared with non-star analysts, star analysts are equipped with superior information resources and professional capabilities, which allow them to release more accurate earnings forecasts. However, they also tend to be overconfident. Therefore, the dual complexity of derivatives provides a test for the difference between star analysts and non-star analysts’ forecasting behavior. Based on the annual reports of Chinese A-share listed non-financial companies, we utilize the textual analysis method to construct data on derivative usage from 2007 to 2017. The rationale for our sample is as follows. First, the financial instrument standards used by listed companies beginning in 2007 require enterprises to disclose quantitative and qualitative information on derivatives in detail in their financial reports. Second, financial firms’ motivations for applying derivatives and relevant regulations may be different from those of their non-financial counterparts. We further analyze whether star analysts and non-star analysts have different forecast behavior when facing complex derivative usage information. Our findings show that analyst coverage and public information precision declines significantly for companies that use derivatives. This indicates that derivative usage affects analysts’ ability to interpret information and has a negative impact on the information environment. We also find that relative to non-star analysts, star analysts’ coverage and public information precision change insignificantly, and their private information precision increases significantly. This indicates that star analysts can use their professional advantages and information interpreting capabilities to supplement private information in response to reputational or economic incentives. For non-star analysts, coverage and public information precision decline significantly, which indicates that non-star analysts are less willing to take risks in forecasting these firms. We perform several sensitivity tests and the conclusions remain unchanged.The study makes two main contributions to the literature. First, compared with the forecast error and dispersion measure, the measure of public and private information precision used in this paper(Barron et al., 1998) reveals additional information on analysts’ forecasting behavior, which is helpful for understanding the mechanism of analysts’ predictions and opens the "black box." Second, previous studies of star and non-star analysts pay little attention to whether cross-sectional differences in companies have different influences on star versus non-star analysts. We test whether star and non-star analysts have different forecast behavior, especially in relation to public and private information precision, when they face derivative usage with complex economic substance and accounting information. Our findings have several implications for policymakers. The complexity of derivative accounting standards may affect the transparency and understandability of financial reports and reduce analysts’ public information precision. Therefore, it is important to improve the capital market information environment by modifying and simplifying derivative accounting standards.