A Decision Support System for Identifying the Existence of Information Asymmetry in Regulated Stock Markets

Abstract The regulatory news announcements (RNAs) are established to ensure the unbiased access to business related information by all members of the investment community. We develop a decision support system for evaluating the level of information asymmetry in regulated stock markets. First, we perform sentiment analysis for classifying the types of announcements. Then, we use the level of trading volume, as a proxy for measuring the level of information asymmetry in regulated stock markets. In a fair market, without any form of information asymmetry, we should expect that the preannouncement ( t-1 ) impact of an informationally-rich RNA should be zero, as new information, is disclosed to all market participants simultaneously at day ( t ). We find that there is statistically significant higher extreme fluctuation of the trading volume the day before ( t-1 ) the official announcement of an informationally-rich RNA, indicating that there is some sort of information asymmetry in the UK stock market.