A Regression-Based Share Market Prediction Model for Bangladesh
This work addresses stock market prediction for investors in Bangladesh, but it is incremental as it applies standard machine learning methods to a specific dataset without major innovations.
The paper tackled predicting stock prices in the Bangladesh share market using linear regression and random forest models on Dhaka Stock Exchange data, finding that random forest performed better but linear regression helped identify key factors influencing price variability.
Share market is one of the most important sectors of economic development of a country. Everyday almost all companies issue their shares and investors buy and sell shares of these companies. Generally investors want to buy shares of the companies whose market liquidity is comparatively greater. Market liquidity depends on the average price of a share. In this paper, a thorough linear regression analysis has been performed on the stock market data of Dhaka Stock Exchange. Later, the linear model has been compared with random forest based on different metrics showing better results for random forest model. However, the amount of individual significance of different factors on the variability of stock price has been identified and explained. This paper also shows that the time series data is not capable of generating a predictive linear model for analysis.