Paper: arXiv 2507.18643

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.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: 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.

Research Flowchart

  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"]

Cited References (38)

  1. Zur Elektrodynamik bewegter K "o rper . ( German ) [ On the electrodynamics of moving bodies] (1905)
  2. ANNUAL FLOOD REPORT 2014 (2014)
  3. Multi-variate flood damage assessment: a tree-based data-mining approach (2013)
  4. Flood damage and influencing factors: New insights from the August 2002 flood in Germany (2005)
  5. Modeling flood damage: case of Tokai Flood 2000 (2005)
  6. Flood hazard and risk analysis in the southwest region of Bangladesh (2005)
  7. Evaluating flood hazard for land-use planning in Greater Dhaka of Bangladesh using remote sensing and GIS techniques (2007)
  8. Estimation of flood damage functions for river basin planning: a case study in Bangladesh (2015)
and 30 more
  1. Factors of influence on flood damage mitigation behaviour by households (2014)
  2. Long-term development and effectiveness of private flood mitigation measures: an analysis for the German part of the river Rhine (2012)
  3. Perception and communication of flood risks: a systematic review of empirical research (2013)
  4. Floods and poverty traps: Evidence from Bangladesh (2007)
  5. Coping with floods: preparedness, response and recovery of flood-affected residents in Germany in 2002 (2007)
  6. Distribution of impacts of natural disasters across income groups: A case study of New Orleans (2007)
  7. What determines vulnerability to floods; a case study in Georgetown, Guyana (1997)
  8. Flood damage, vulnerability and risk perception–challenges for flood damage research (2006)
  9. Methods and criteria for model selection (2004)
  10. The assumptions of the linear regression model (1971)
  11. Exploratory data analysis (1977)
  12. Testing for serial correlation in least squares regression. II (1951)
  13. Random forests (2001)
  14. Boosted trees for ecological modeling and prediction (2007)
  15. The perceptron: A probabilistic model for information storage and organization in the brain. (1958)
  16. Learning internal representations by error propagation (1985)
  17. Regression diagnostics; identifying influential data and sources of collinearity (1980)
  18. Regression analysis by example (1977)
  19. Factors Affecting the Stock Price Variability of Dhaka Stock Exchange (DSE) (2014)
  20. Stock market prediction using machine learning techniques (2016)
  21. Stock market prediction system with modular neural networks (1990)
  22. Factors Affecting the Stock Price Movement: A Case Study on Dhaka Stock Exchange (2015)
  23. (12 October 2011) Bangladesh starts market rescue fund — link
  24. Exploratory data analysis (1977)
  25. LankaBangla Financial Portal — link
  26. Dhaka Stock Exchange Ltd. — link
  27. Trading Economics — link
  28. Time series analysis (1994)
  29. Regression and time series model selection in small samples (1989)
  30. Problems That May Occur in Time Series Multiple Regression — link

0 of 38 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.