CNN for Stock Prediction

CURRENT POSITION

I believe applying CNNs to raw stock prices and volume, structured as image-like tensors, can improve stock movement prediction by capturing local temporal patterns without heavy feature engineering.

KEY ASSUMPTIONS

• Local temporal patterns in stock data are predictive
• CNNs can generalize from raw financial data without handcrafted indicators

SUPPORTING EVIDENCE

• Recent research proposes treating multivariate stock time series as image-like inputs for CNNs
• CNN-based approaches have been explored for financial time-series by representing multivariate stock data in structured tensors
• The paper reports up to ~91% directional accuracy for certain individual S&P 500 stocks using CNNs on historical data

OPEN QUESTIONS

• Whether CNN-based models generalize across different market regimes
• Risk of overfitting when using sliding windows
• Whether the CNN-based strategy can sustain performance in live or paper trading over several months with real transaction costs

WANT TO EXPLORE DEEPER?

by parag