A Regression-Based Share Market Prediction Model for Bangladesh

ArXiv ID: 2507.18643 “View on arXiv”

Authors: Syeda Tasnim Fabiha, Rubaiyat Jahan Mumu, Farzana Aktar, B M Mainul Hossain

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.

Keywords: Linear Regression, Random Forest, Stock Price Prediction, Market Liquidity, Dhaka Stock Exchange, Equities

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Philosophers
  • Why: The paper uses standard regression and random forest without advanced mathematical derivations, but it applies these methods to real stock data with performance metrics, placing it in the low-math, low-rigor quadrant.
  flowchart TD
    A["Research Goal: Predict Share Market Prices for DSE"] --> B["Data Collection & Preparation"]
    B --> C["Methodology: Linear Regression & Random Forest"]
    C --> D{"Model Evaluation & Comparison"}
    D --> E["Linear Regression Model"]
    D --> F["Random Forest Model"]
    E --> G["Outcomes: Low R2 for Linear Model<br/>Time Series Unsuitable for Linear Prediction"]
    F --> H["Outcomes: Better Performance for Random Forest<br/>Identified Key Market Factors"]
    G & H --> I["Conclusion: Factors Identified, RF Superior for Prediction"]