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S&P 500 Stocks Movement Prediction using Deep Learning

Author(s): Rahul Gupta


This paper is about predicting the movement of stock consisting of the S&P 500 index. Historically there are many approaches that have been tried using various methods to predict the stock movement and are being used in the market currently for algorithm trading and alpha generating systems using traditional mathematical approaches. The success of artificial neural networks recently created a lot of interest and paved the way to enable prediction using cutting-edge research in machine learning and deep learning. Some of these papers have done a great job in implementing and explaining the benefits of these new technologies. Although most of 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, we are trying to use multivariate raw data instead of engineered matrices data that considers stock split/dividend events (as-is) present in real-world market data. Convolution Neural Network (CNN) so far, the best known tool for image classification model as of today, is used on the multi-dimensional stock numbers taken from the market, mimicking them as vector/matrix of 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.

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