S&P 500 Stock's Movement Prediction using CNN

TL;DR

This paper explores using convolutional neural networks on raw multivariate stock data, including events, to predict S&P 500 stock movements with promising results, advancing financial prediction methods.

Contribution

It introduces a CNN-based approach on real-world, multivariate financial data including events, moving beyond single-dimension data used in prior research.

Findings

Model achieves promising prediction accuracy.

Uses raw market data including stock split/dividend events.

Applicable to individual stocks, sectors, or portfolios.

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.