Predict financial text sentiment: an empirical examination - Ruchi Kejriwal; Monika Garg; Gaurav Sarin (Vilakshan (XIMB Journal of Management))
Purpose – Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively. Design/methodology/approach – The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix. Findings – Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement. Originality/value – This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.