Building a Calendar of Events Database by Analyzing Financial Spikes

Abstract

An event is a piece of news that triggers a change in stock prices. Here, an event study is undertaken to capture the effect of abnormal returns due to an event. The event can affect the stock market in the long term or short term. Event research is relevant to both the efficient market hypothesis and behavioral finance. In this study, we collected data from websites that manage financial and economic data, performed a sentiment analysis, and correlated news article data with changes in a particular company’s stock prices in the stock market. Data were collected from two well-known financial news websites. We observed a correlation between stock prices and news items. An event period of one day was considered for the study. The regression equation determined the relationship between stock returns and polarity and subjectivity. Bayesian model averaging was performed to identify the effects of polarity and subjectivity on stock returns. Time-series data were decomposed into components and detrended via regression. Prominent keywords and their polarity values for a particular day were plotted. An event enhanced some stock returns while adversely affecting other stocks. We found a variation range of −400 to 200 for different company stocks for the selected period.

Keywords

• Event database
• social media data
• sentiment analysis
• data analytics
• event studies