Anticipating The Stock Market Trend Using Natural Language Processing Techniques

Abstract

Anticipating stock market fluctuations is a crucial routine that investors must engage in when participating in the stock trading market, making it an intriguing research area. The stock market is subjected to the impact of multiple factors, such as news events, economic data, and investor sentiments. Nevertheless, the intricate relationship between news and stock prices contains hidden trends contributing towards the trading recommendations.This study aims to anticipate stock market trends using Natural Language Processing (NLP) techniques, with a particular fo- cus on the Pakistan Stock Market. By creating sequential snapshots of news along with financial data, and employing sentiment analysis to capture market sentiment, this research goes beyond traditional methodologies.Additionally, this research examines the correlation between stock market patterns and news events by employing features that are specifically tailored to analyse the distinct market dynamics of the Pakistan Stock Exchange. The results of our study reveal a prominent ratio of 1:2 between negative and positive news events, underscoring the substantial influence of negative events on market volatility. Consequently, this work represents progress in the direction of automating data-driven and well-informed trading recommendations.