Machine learning models based on financial data for stock trend predictions

This paper investigates the application of long short-term memory (LSTM), one-dimensional convolutional neural networks (1D CNN), and logistic regression (LR), for predicting stock trends based on fundamental analysis. This research emphasises a company's financial statements and its intrinsic value for stock price 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 for two tasks: annual stock price difference (ASPD) and difference between current stock price and intrinsic value (DCSPIV). Assessing the likelihood of profitability from relationship between financial data and price action, and the current discrepancy between 'true value' and market price, 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, highlighting the benefits for portfolio managers in their decision-making processes.