A Study on Information Value of Analyst Report Text Based on the Analysts’ Herding Behavior Under Globalized Financial Markets

Abstract

Recently, the investment information is spreading in near real-time due to the global integration of the international financial market. At this point, it is essential to resolve the information asymmetry in the capital market to vitalize domestic and foreign investment. Analysts improve market efficiency, by performing information intermediary role between the capital market and investors. However, the herding behavior among analysts in the market can weaken the information quality and lead to inefficiency in the financial market. In this study, the relationship between analyst herding behavior and the text of the report is analyzed. In addition, the effect of the text of the report body on the information power according to the analyst's herding behavior was verified. To this end, the positive and negative emotions shown in the body of the analyst report were extracted as words of up to 2-gram units and merged with the list of positive and negative words of Loughran and McDonald(2011), a sentiment dictionary widely used in the financial field. As a result of the analysis, when an analyst presents a 'bold' opinion that deviates from the consensus, the sentiment of the report body is more extreme than that of an ‘herd’ opinion. In addition, it was confirmed that positive emotion weakens the report information power according to the analyst's herding behavior. This study is expected to alleviate information asymmetry between investors and the financial market. Furthermore, the text mining methodology used in this study can be readily applied to any other kind of text, including News, Social Network Services, or IR reports.