S&P 500 Stock's Movement Prediction using CNN
S&P 500 Stock's Movement Prediction using CNN
This paper presents a novel approach to predicting stock price movements for S&P 500 index constituents using Convolutional Neural Networks (CNNs). Unlike traditional methods that rely on engineered financial features or single-dimensional time series, the author uses multivariate raw market data—including stock splits and dividend events—as direct input. The historical data is structured as 2D matrices, analogous to images, allowing CNN's spatial feature extraction capabilities to capture complex patterns. The model is trained and tested on real-world data, achieving promising results for predicting directional movements at individual stock, sector, and portfolio levels. The work highlights the potential of deep learning in financial prediction without extensive feature engineering, contributing to algorithmic trading and alpha generation systems.
Highlights
- 1Applies CNN to multivariate raw stock market data including stock splits and dividends, treating historical data matrices as images.
- 2Demonstrates prediction capability at single stock, sector, and portfolio levels.
- 3Uses real-world market data without engineered financial features, preserving data complexity.
- 4Achieves promising results for S&P 500 stock movement prediction.
Methods
- MConvolutional Neural Network (CNN) for time-series classification.
- MMultivariate raw data representation as 2D matrices (images) from historical stock data.
- MData preprocessing including stock split/dividend events as-is.
Results
- RCNN model successfully predicts stock movement direction for S&P 500 stocks.
- RModel generalizes across individual stocks, sectors, and portfolios.
- RPromising accuracy compared to traditional methods, though specific metrics not detailed in abstract.
- RDemonstrates feasibility of using raw multivariate financial data with CNN.
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