Leveraging Fundamental Analysis for Stock Trend Prediction for Profit

TL;DR

This study explores using fundamental financial data and machine learning models, especially Logistic Regression, to predict stock trends and profitability, emphasizing long-term investment insights over technical analysis.

Contribution

It introduces a novel approach combining fundamental analysis with machine learning for stock trend prediction, demonstrating LR's superior performance on financial statement data.

Findings

LR outperforms CNN and LSTM in accuracy

Achieves 74.66% accuracy for ASPD

Achieves 72.85% accuracy for DCSPIV

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.