Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock

ArXiv ID: 2401.10903 “View on arXiv”

Authors: Unknown

Abstract

This document presents a stock market analysis conducted on a dataset consisting of 750 instances and 16 attributes donated in 2014-10-23. The analysis includes an exploratory data analysis (EDA) section, feature engineering, data preparation, model selection, and insights from the analysis. The Fama French 3-factor model is also utilized in the analysis. The results of the analysis are presented, with linear regression being the best-performing model.

Keywords: Fama French 3-factor model, Linear regression, Exploratory data analysis (EDA), Feature engineering, Stock market analysis, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 5.0/10
• Quadrant: Street Traders
• Why: The paper uses accessible, application-oriented machine learning (linear regression, random forest) and Fama-French factor modeling, which is standard and not mathematically dense; however, it is highly data-focused with explicit steps for EDA, feature engineering, and model evaluation on a specific dataset, indicating practical implementation readiness.

  flowchart TD
    A["Research Goal: Forecast Disney Stock Prices"] --> B["Dataset: 750 instances, 16 attributes"]
    B --> C["Key Methodology Steps<br>EDA & Feature Engineering"]
    C --> D["Computational Process<br>Apply Fama French 3-Factor Model"]
    D --> E["Model Training & Selection<br>Linear Regression vs. Others"]
    E --> F{"Key Findings/Outcomes"}
    F --> G["Linear Regression: Best Model<br>Applied to Disney Stock Data"]