Stock Return Prediction Using Recurrent Neural Networks: Practical Application of Deep Learning in Financial Time Series

This article introduces the open-source project Stock-Returns-Predictor-with-RNN, which uses Recurrent Neural Networks (RNN) and its variants (LSTM, GRU, etc.) to predict the logarithmic returns of Apple Inc. (AAPL) stock. The project covers time-series feature engineering, multi-model comparison, and finance-specific evaluation metrics, demonstrating application methods and practical experience of deep learning in financial time-series prediction.

Stock market prediction is a challenge in the fields of financial engineering and machine learning. Traditional models like ARIMA and GARCH have limitations in handling non-linearity, long-range dependencies, and noise in financial data. This project focuses on using RNN to address this issue, selecting AAPL stock (with sufficient data and high liquidity) as the research object, and using logarithmic returns (which have advantages such as time additivity and better stationarity) for prediction.

The project implements multiple RNN architectures: basic RNN (prone to gradient vanishing), LSTM (uses gating mechanisms to solve long-range dependency issues), GRU (simplified LSTM with fewer parameters and faster training), bidirectional RNN, and multi-layer stacking. In terms of feature engineering, price/volume features (raw prices, trading volume, technical indicators like MA/RSI/MACD) are constructed, sliding windows are used to design samples, and features are normalized.

Training strategies: Loss functions include MSE, MAE, or Huber loss; regularization uses Dropout, L2 weight decay, and early stopping; learning rate scheduling gradually reduces the learning rate. Evaluation metrics include statistical indicators (RMSE, MAPE, R²), direction accuracy (the proportion of correct predictions of price rise/fall direction), and strategy backtesting (comparing cumulative returns of the prediction strategy vs. the buy-and-hold strategy).

The experiments yielded the following insights: 1. LSTM and GRU are significantly better than basic RNN; 2. Bidirectional RNN provides limited improvement (since there is no future information in actual trading); 3. Feature engineering is more important than model complexity; 4. Prediction difficulty increases with time scale; 5. The model performs well in stable markets but its predictive ability decreases during periods of high volatility (such as earnings reports or macroeconomic events).

Current limitations: Non-stationarity of financial time series, low signal-to-noise ratio, overfitting risk, and inability to predict black swan events. Future improvements: Introduce attention mechanisms, integrate multi-source data (news sentiment, macroeconomic indicators), use ensemble methods, and explore Transformer architectures.

Practical insights: 1. Start with simple models to establish a baseline; 2. Emphasize evaluation from a financial perspective; 3. Recognize model limitations; 4. Continuously iterate and update models. Project summary: This project has a clear structure, forming a complete process from feature engineering to evaluation. It provides a reusable framework for the AI finance field and is an excellent resource for learning the application of deep learning in finance.