# Leveraging Fundamental Analysis for Stock Trend Prediction for Profit

**Authors:** John Phan, Hung-Fu Chang  
**arXiv:** [2410.03913](https://arxiv.org/abs/2410.03913)  
**Date:** 2024-10-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2410.03913v1/

## Key points

- Paper title: "Leveraging Fundamental Analysis for Stock Trend Prediction for Profit" by John Phan and Hung-Fu Chang
- Application of machine learning models to forecast stock trends based on fundamental analysis
- Dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023
- Utilization of key financial ratios and the Discounted Cash Flow (DCF) model for prediction tasks: ASPD and DCSPIV
- Logistic Regression (LR) models outperform Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models with average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV
- Contribution to integrating fundamental analysis into machine learning for stock prediction, providing insights for academic research and investment strategies
- Highlighting potential for long-term stock trend prediction by leveraging fundamental data
- Importance of combining structured financial data with unstructured textual information to enhance stock market trend prediction accuracy

## Summary

The paper titled "Leveraging Fundamental Analysis for Stock Trend Prediction for Profit" by John Phan and Hung-Fu Chang explores the application of machine learning models to forecast stock trends based on fundamental analysis. The study utilizes a dataset of 269 data points from publicly traded companies across various sectors spanning from 2019 to 2023 and incorporates 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). The findings reveal that Logistic Regression (LR) models outperform Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV. This research contributes significantly to the limited literature on integrating fundamental analysis into machine learning for stock prediction, providing valuable insights for both academic research and practical investment strategies. By leveraging fundamental data, this approach highlights the potential for long-term stock trend prediction, supporting portfolio managers in their decision-making processes. The authors also discuss previous studies on sentiment analysis using financial statements and social media data but note challenges such as bias in professional sources' sentiments. Ultimately, this paper emphasizes the importance of combining structured financial data with unstructured textual information to enhance stock market trend prediction accuracy.