Leveraging Fundamental Analysis for Stock Trend Prediction for Profit

ArXiv ID: 2410.03913 “View on arXiv”

Authors: Unknown

Abstract

This paper investigates the application of machine learning models, Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based on fundamental analysis. Unlike most existing studies that predominantly utilize technical or sentiment analysis, we emphasize the use of a company’s financial statements and intrinsic value for trend forecasting. Using a dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023, we employ key financial ratios and the Discounted Cash Flow (DCF) model to formulate two prediction tasks: Annual Stock Price Difference (ASPD) and Difference between Current Stock Price and Intrinsic Value (DCSPIV). These tasks assess the likelihood of annual profit and current profitability, respectively. Our results demonstrate that LR models outperform CNN and LSTM models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV. This study contributes to the limited literature on integrating fundamental analysis into machine learning for stock prediction, offering valuable insights for both academic research and practical investment strategies. By leveraging fundamental data, our approach highlights the potential for long-term stock trend prediction, supporting portfolio managers in their decision-making processes.

Keywords: Long Short-Term Memory, Logistic Regression, Fundamental Analysis, Discounted Cash Flow, Financial Ratios, Equities

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced machine learning models (CNN, LSTM) and foundational quantitative finance concepts like DCF, but uses a very small dataset (269 data points) with no mention of rigorous backtesting or transaction costs, placing it in a controlled experimental setting rather than a production-ready environment.

  flowchart TD
    A["Research Goal"] --> B["Data & Feature Engineering"]
    B --> C["Model Training"]
    C --> D["Model Evaluation & Comparison"]
    D --> E["Key Findings"]

    subgraph B ["Data & Inputs"]
        B1["Dataset: 269 data points<br>2019-2023"] --> B2["Features: Financial Ratios &<br>Discounted Cash Flow DCF"]
        B2 --> B3["Targets: Annual Stock Price<br>Difference & Current vs Intrinsic Value"]
    end

    subgraph C ["Computational Processes"]
        C1["LSTM"] --> C2["1D CNN"]
        C2 --> C3["Logistic Regression LR"]
    end

    subgraph E ["Outcomes"]
        E1["LR Outperforms LSTM & CNN"]
        E2["Accuracy: ASPD 74.66%<br>DCSPIV 72.85%"]
        E3["Validated Fundamental Analysis<br>for Long-Term Prediction"]
    end

    style A fill:#e1f5e1,stroke:#333,stroke-width:2px
    style E fill:#ffebee,stroke:#333,stroke-width:2px