paragQuestion / Claim
Representing raw multivariate stock data as image-like inputs enables CNNs to learn meaningful market patterns.
Key Assumptions
- Local temporal patterns in stock data are predictive(medium confidence)
- CNNs can generalize from raw financial data without handcrafted indicators(medium confidence)
Evidence & Observations
- Recent research proposes treating multivariate stock time series as image-like inputs for CNNs(citation)
- CNN-based approaches have been explored for financial time-series by representing multivariate stock data in structured tensors(citation)
- The paper reports up to ~91% directional accuracy for certain individual S&P 500 stocks using CNNs on historical data(citation)
- Independent studies report CNNs outperform traditional models like ARIMA and SVM on stock time-series benchmarks(citation)
- Many academic CNN-based stock prediction results suffer from data-snooping bias, improper time-series validation, and overfitting, requiring rigorous walk-forward testing on live data(citation)
- Financial ML strategies should be simulated with realistic transaction costs and evaluated over several months of live or paper trading before claiming deployable performance(citation)
Open Uncertainties
- 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
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.
This is work-in-progress thinking, not a final conclusion.
References(5)
- 1.^"S&P 500 Stock’s Movement Prediction using CNN"↗arxiv.org— Research paper proposing CNN-based prediction using raw multivariate stock data structured as image-like input
- 2.^"Deep Learning Models for Stock Market Prediction"↗mdpi.com— Peer-reviewed MDPI study comparing CNNs with traditional and recurrent models for stock prediction
- 3.^"Stock Price Forecasting Using CNN"↗mdpi.com— MDPI paper showing CNN-based models reduce forecasting error compared to classical methods
- 4.^"The Probability of Backtest Overfitting"↗papers.ssrn.com— Foundational paper explaining data-snooping bias and the need for robust validation in financial ML
- 5.^"Backtesting Pitfalls in Algorithmic Trading"↗quantstart.com— Practitioner-focused explanation of why strategies must be tested on real or paper-traded data with costs
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