On Quantifying Sentiments of Financial News -- Are We Doing the Right Things?
This addresses the problem of inaccurate sentiment analysis for financial news, which is crucial for investors and researchers predicting stock market movements, though it is incremental as it builds on existing methods with domain-specific customization.
The paper critiques existing sentiment analysis libraries like Vader and Loughran-McDonald for financial news, finding they are unreliable, and introduces SENTInews, a customized analyzer for the Indian context to improve accuracy.
Typical investors start off the day by going through the daily news to get an intuition about the performance of the market. The speculations based on the tone of the news ultimately shape their responses towards the market. Today, computers are being trained to compute the news sentiment so that it can be used as a variable to predict stock market movements and returns. Some researchers have even developed news-based market indices to forecast stock market returns. Majority of the research in the field of news sentiment analysis has focussed on using libraries like Vader, Loughran-McDonald (LM), Harvard IV and Pattern. However, are the popular approaches for measuring financial news sentiment really approaching the problem of sentiment analysis correctly? Our experiments suggest that measuring sentiments using these libraries, especially for financial news, fails to depict the true picture and hence may not be very reliable. Therefore, the question remains: What is the most effective and accurate approach to measure financial news sentiment? Our paper explores these questions and attempts to answer them through SENTInews: a one-of-its-kind financial news sentiment analyzer customized to the Indian context