A Regression-Based Share Market Prediction Model for Bangladesh

TL;DR

This paper compares linear regression and random forest models for predicting stock prices in Bangladesh, finding that random forest performs better, but linear models are limited for time series data.

Contribution

It introduces a comparative analysis of regression and random forest models for stock prediction in Bangladesh, highlighting the limitations of linear models for time series data.

Findings

Random forest outperforms linear regression in stock prediction.

Linear models are inadequate for time series forecasting in this context.

Significance of different factors on stock price variability is identified.

Abstract

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.