HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model

TL;DR

This paper proposes a stock market forecasting model that combines historical data and sentiment analysis using LSTM to improve prediction accuracy, leveraging the correlation between news sentiment and stock movements.

Contribution

It introduces a novel approach integrating historical stock data with sentiment analysis for more accurate stock price prediction using LSTM.

Findings

Sentiment data improves prediction accuracy

Historical and sentiment data combined yields better results

Strong correlation between news sentiment and stock prices

Abstract

One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a prediction model which can accurately predict future stock prices. With the help of machine learning, it is not an impossible task as the various machine learning techniques if modeled properly may be able to provide the best prediction values. This would enable the investors to decide whether to buy, sell or hold the share. The aim of this paper is to predict the future of the financial stocks of a company with improved accuracy. In this paper, we have proposed the use of historical as well as sentiment data to efficiently predict stock prices by applying LSTM. It has been found by analyzing the existing research in the area of sentiment analysis that there is a strong correlation between the movement of stock prices and the publication of news articles. Therefore, in this paper, we have integrated these factors to predict the stock prices more accurately.