CNN-LSTM Hybridisation in S&P500 Stock Prediction
Title
Mastering S&P500 Stock Prediction with CNN-LSTM: A Deep Dive into Hybrid AI
Introduction
Predicting stock prices is one of the most challenging tasks in financial analytics due to the complex, volatile, and nonlinear nature of financial markets. In this article, we explore how a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model can outperform traditional machine learning algorithms like Random Forest in predicting S&P500 stock prices. Using a dataset spanning five years, we’ll walk through data preprocessing, model construction, training, and evaluation, revealing why CNN-LSTM is a game-changer in time-series forecasting.
The Dataset
We used the S&P500 5-Year Stock Price Dataset, sourced from Kaggle. It includes daily data on open, high, low, volume, and close prices for multiple stocks. Our target variable is the closing price, which provides critical insights for investors. Here's how we prepared the data for machine learning:
Data Cleaning: Handled missing values using forward fill and removed duplicates.
Feature Selection: Focused on key numerical features (
open,high,low,volume,close).Scaling: Applied MinMaxScaler to normalize features, crucial for deep learning models.
Sequence Generation: Transformed the data into sequences of 10-day intervals for LSTM input.
The Models: Random Forest vs. CNN-LSTM
Random Forest:
A robust ensemble-based algorithm.
Excels at capturing nonlinear relationships in tabular data but lacks the temporal awareness necessary for sequential tasks.
CNN-LSTM Hybrid:
Combines CNN’s ability to extract local features (e.g., price trends) with LSTM’s expertise in sequential data modeling.
Tuned with 64 and 128 neurons in CNN layers and 128 and 64 neurons in LSTM layers for optimal performance.
Model Training and Evaluation
The models were evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² metrics. The CNN-LSTM was trained for 20 epochs with a batch size of 32, while the Random Forest used default parameters optimized for regression tasks.
| Metric | CNN-LSTM | Random Forest | Naïve Benchmark |
| MSE | 0.008 | 0.01 | 0.02 |
| RMSE | 0.09 | 0.11 | 0.14 |
| R² | 0.89 | 0.76 | 0.65 |
Key Insights
CNN-LSTM Superiority:
Outperformed Random Forest by 20% lower RMSE and 25% lower MSE, demonstrating its capacity to model dependencies over time.
Its hybrid architecture effectively captured the nuances in stock price fluctuations.
Random Forest Limitations:
- While robust to noise, it struggled with sequential dependencies, leading to less precise predictions during volatile periods.
Naïve Benchmark:
- Provided a baseline for comparison but failed to adapt to stock market complexities.
Visual Analysis
The graph below highlights the performance difference:
CNN-LSTM Predictions: Closely align with actual prices, especially during volatile periods.
Random Forest Predictions: Show lagging responses and oversimplified trends, unable to capture the intricacies of sudden market shifts.
Challenges and Learnings
Handling data volatility required preprocessing techniques like scaling and outlier retention to preserve market dynamics.
Model tuning was critical, as CNN-LSTM required careful adjustment of neurons and layers to avoid overfitting.
Interpretability remains a challenge for deep learning models compared to Random Forest, which offers clear feature importance.
Future Work
Incorporate sentiment analysis from financial news to enrich the dataset.
Explore Transformer-based architectures for their state-of-the-art performance in time-series tasks.
Model uncertainty quantification using probabilistic approaches like Bayesian deep learning.
Conclusion
This project showcases the power of hybrid deep learning models like CNN-LSTM in addressing the inherent challenges of stock price prediction. By leveraging the strengths of both CNN and LSTM, the model outperforms traditional algorithms like Random Forest, offering a robust solution for sequential financial forecasting. With further refinement and incorporation of external features, CNN-LSTM holds the potential to revolutionize decision-making in the stock market.
Call to Action
If you’re intrigued by the potential of AI in finance, try implementing your own CNN-LSTM model using the S&P500 dataset. Share your results, and let’s push the boundaries of financial forecasting together!