Predicting Stock Market with Recurrent Neural Networks: Analysis of CS429 Course Practice

This article analyzes the 4th assignment of CS429 course, discussing how to use Recurrent Neural Networks (RNN) for stock price prediction, covering data preprocessing, model architecture design, training strategies, and key considerations in practical applications. The content is sourced from the project "4-Intro-Machine-Learning" published by GitHub user jjavier-chool on June 12, 2026 (link: https://github.com/jjavier-chool/4-Intro-Machine-Learning). It focuses on the application of RNN/LSTM in financial time series prediction, combining technical principles and practical details.

Stock market prediction is highly challenging due to the non-linearity and noise of data, and traditional time series methods are limited. As a neural network for processing sequence data, RNN achieves "memory" capability through recursive updates of hidden states (formula: h_t = tanh(W_{hh}*h_{t-1}+W_{xh}*x_t +b_h); y_t=W_{hy}*h_t +b_y), making it suitable for time series. However, standard RNN has gradient vanishing/exploding problems. LSTM solves this through cell states and gating mechanisms (forget gate, input gate, output gate), becoming the preferred architecture for financial prediction.

Financial data preprocessing is crucial: 1. Normalization (Min-Max or Z-score) to unify data range; 2. Construct sequence windows (determine historical time step length and prediction span);3. Introduce time features (week, month, etc.) to capture periodicity;4. Add technical indicators (MA, RSI, MACD, etc.) to enhance feature engineering.

The model in CS429 assignment uses multi-layer LSTM stacking + fully connected layers. Regularization methods include Dropout (randomly discarding connections), L2 regularization (restricting weights), and early stopping (terminating training when validation set performance stops improving). Loss functions: MSE/MAE for regression, cross-entropy for classification. Adam is commonly used as the optimizer, combined with learning rate scheduling (decay or cosine annealing) to improve performance.

Applying RNN to stock prediction faces multiple challenges: the market is highly random and affected by unpredictable factors; distribution drift (evolution of participant behavior); historical patterns may not predict the future. Attention should be paid to data leakage (avoid using future information, adopt strict time series cross-validation); transaction costs (fees, slippage) can erode profits, and backtesting needs to consider real transaction cost models.

RNN provides a powerful tool for financial time series analysis, but rational expectations are needed: the model can identify historical patterns but cannot predict black swan events or market structure changes. The CS429 assignment not only teaches technical implementation but also cultivates critical thinking—understanding model boundaries, recognizing prediction uncertainty, and carefully evaluating results. Technology should assist decision-making rather than replace judgment.