Predicting the Effects of News Sentiments on the Stock Market

TL;DR

This paper develops a sentiment analysis model tailored for financial news to predict stock market trends, achieving over 70% accuracy in short-term pharmaceutical stock movements.

Contribution

It introduces a new financial sentiment dictionary and a dictionary-based analysis model specifically for stock market prediction.

Findings

Achieved 70.59% directional accuracy in stock trend prediction.

Validated the model on pharmaceutical market stocks.

Demonstrated the impact of news sentiments on stock movements.

Abstract

Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market. Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market. Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.