S&P 500 Stock's Movement Prediction using CNN

Abstract: This paper is about predicting the movement of stock consist of S&P 500 index. Historically there are many approaches have been tried using various methods to predict the stock movement and being used in the market currently for algorithm trading and alpha generating systems using traditional mathematical approaches [1, 2]. The success of artificial neural network recently created a lot of interest and paved the way to enable prediction using cutting-edge research in the machine learning and deep learning. Some of these papers have done a great job in implementing and explaining benefits of these new technologies. Although most these papers do not go into the complexity of the financial data and mostly utilize single dimension data, still most of these papers were successful in creating the ground for future research in this comparatively new phenomenon. In this paper, I am trying to use multivariate raw data including stock split/dividend events (as-is) present in real-world market data instead of engineered financial data. Convolution Neural Network (CNN), the best-known tool so far for image classification, is used on the multi-dimensional stock numbers taken from the market mimicking them as a vector of historical data matrices (read images) and the model achieves promising results. The predictions can be made stock by stock, i.e., a single stock, sector-wise or for the portfolio of stocks.

• The paper introduces a deep 1D CNN architecture that processes raw OHLCV data, including dividend and split events, to forecast S&P 500 stock movements.
• It demonstrates up to 91% accuracy in predicting individual and sectoral stock movements, significantly outperforming traditional baseline models.
• The methodology employs sliding window augmentation and a binary classification approach to robustly capture localized market patterns.

Predicting S&P 500 Stock Movements Using Convolutional Neural Networks

Overview

This work explores the application of deep 1D Convolutional Neural Networks (CNNs) to the problem of forecasting individual and sectoral movements of stocks within the S&P 500 index. Unlike approaches that rely on engineered features or technical indicators, the proposed method directly leverages raw multivariate price and volume data — including unadjusted and adjusted OHLCV series, thus retaining the information contained in dividend and split events. The methodology focuses on using CNN architectures, more commonly associated with computer vision, to extract spatially local patterns across time-series embeddings of financial data.

Methodological Contributions

The primary methodological contributions of the paper are as follows:

• Raw Multivariate Input: Training is performed on raw daily OHLCV and adjusted OHLCV data (10 dimensions), bypassing feature engineering steps and instead incorporating dividend and split history directly.
• Deep 1D CNN Architecture: The model architecture employs eight convolutional layers (initial with ReLU, subsequent with Leaky ReLU and BatchNorm), followed by two fully connected layers, and a Softmax output.
• Sliding Window Augmentation: Data augmentation uses rolling windows, discretizing the time series into overlapping input segments to enhance pattern diversity and learning capacity.
• Binary Classification Output: The output is a two-class probability representing inferred bullish or bearish future movement, operationalized as a supervised classification task.
• Explicit Handling of Stock Corporate Actions: The approach explicitly tests modeling capacity for market responses to splits and dividends, information typically preprocessed out of most datasets.

Experimental Results

Empirical evaluation compared the enhanced CNN approach with a baseline model and previously published neural-network-based stock prediction results, many of which showed sub-50% accuracy for this domain given the absence of feature engineering. Key results and observations include:

• Substantial Accuracy Improvements: The redesigned architecture achieved up to 91% accuracy on specific stock movement forecasting tasks (e.g., JPMorgan — 30-day forward), far surpassing baseline models and prior published deep learning results in the 38-48% accuracy range.
• Robustness to Market Events: The model demonstrates strong tracking of price movements around dividends and splits, supporting the claim of capturing localized market structure unavailable to models discarding such events.
• Generalizability: Experiments covered single stocks (such as NVIDIA, BAC, JPM), sector-wide prediction, and variable lookahead windows (2–30 days), consistently achieving higher accuracy with increased window size and batch size optimization.
• Improvements with Batch Size and Depth: Superior performance observed with greater batch size (200–250) and increased model complexity, highlighting CNN capacity for deep representation learning from time series.

Implications and Positioning

The paper positions CNNs as not merely suitable but advantageous for financial time-series forecasting, especially under conditions where engineered features are unavailable or undesirable. The strong numerical results regarding classification accuracy directly challenge the view that short-term equity price movements are too noisy for meaningful supervised learning without technical or fundamental augmentation.

Practically, the demonstrated methodology lowers the barrier for deploying data-driven trading strategies directly on raw exchange data, with immediate applicability to portfolio management, sectoral rotation strategies, and risk management tools. The architecture’s ability to handle non-stationary events such as splits and dividends extends its utility to broader asset classes and international indices, pending retraining.

Theoretically, the work suggests that convolutive architectures — by virtue of their local receptive fields and translational invariance — can discover fundamental temporal motifs and regime shifts in asset prices, previously thought to be intractable to shallow or recurrent models when using only price and volume data.

Future Developments

The research suggests several avenues for extension and enhancement:

• Hybrid Architectures: Combining CNNs with LSTM/GRU cells could enable the capture of both short-term local patterns and long-term dependencies across financial regimes.
• Integration of NLP for Fundamental Analysis: Augmenting the CNN input channels with sentiment or event signals extracted via deep NLP models could further increase predictive performance, especially around macroeconomic events or earnings releases.
• Extension to Derivative and Cross-Asset Prediction: Application to options, futures, forex, or commodity markets, given the model’s explicit handling of event-driven non-stationarity, could yield substantial practical value.
• Portfolio Construction and Risk Allocation: The approach creates a foundation for deep learning-driven allocation and risk forecasting tools capable of proposing optimal portfolios or even synthetic asset generation.

Conclusion

This work demonstrates that deep CNNs, operating directly on multivariate, minimally processed market data, can surpass traditional and recent deep models in the task of S&P 500 stock movement prediction. The explicit use of raw, dividend/split-unadjusted data preserves critical event information, granting the model strong accuracy that bridges the gap between model-driven and data-driven financial forecasting. The approach is extensible to global assets and forms a promising component for future, more advanced multi-modal and hybrid financial prediction systems.