Forecasting with Deep Learning: S&P 500 index
This addresses stock market forecasting for investors, but it is incremental as it applies a known deep learning method to a specific dataset.
The paper tackles stock price prediction for the S&P 500 index using a convolution-based neural network model, achieving an accuracy rate of over 55% in predicting the next-day direction and outperforming benchmarks.
Stock price prediction has been the focus of a large amount of research but an acceptable solution has so far escaped academics. Recent advances in deep learning have motivated researchers to apply neural networks to stock prediction. In this paper, we propose a convolution-based neural network model for predicting the future value of the S&P 500 index. The proposed model is capable of predicting the next-day direction of the index based on the previous values of the index. Experiments show that our model outperforms a number of benchmarks achieving an accuracy rate of over 55%.