Determining the Impact of Events on Pakistan Stock Prices and their Prediction using Urdu News Articles

Abstract

Investing in stock market investments involves inherent risks. An accurate forecast of stock market movement is important as it aids investors in identifying potentially profitable stocks, or ones that might incur loss. However, training a model capable of performing this task with reasonable accuracy remains a significant challenge. Each country’s stock market is different in that the factors that influence it, such as certain economic factors, the laws that govern that country, or the sentiments of the people, may be unique to that country. In Pakistan, where the stock market is quite volatile due to the heavy influence of the country's politics on its economy, building a good prediction model becomes even more of a challenge. Urdu being the national language of Pakistan, is used by a majority of the country’s news agencies. This research study aims to create structured datasets to help investigate the relationship between Urdu news events and the performance of Pakistan’s stock market. Initially, this study focuses on the cement and oil & gas sectors of Pakistan. The study uses Natural Language Processing (NLP) by leveraging a combination of machine learning and deep learning techniques to extract insights from Urdu news articles, encompassing sentiment analysis, named entity recognition, keyword extraction, and event extraction. These insights are subsequently linked to corresponding data from the Pakistan Stock Exchange (PSX) to create standardized datasets for analysis and prediction. The findings of this study hold significant implications for investors, financial analysts and policy makers in Pakistan, as well as NLP enthusiasts for the Urdu language. The ability to quantify the impact of news events on stock prices can guide investment strategies and risk management decisions.