Forecasting with Deep Learning: S&P 500 index

TL;DR

This paper introduces a convolutional neural network model for predicting the next-day direction of the S&P 500 index, demonstrating improved accuracy over benchmarks in stock price forecasting.

Contribution

The paper presents a novel convolution-based neural network specifically designed for stock index direction prediction, advancing deep learning applications in financial forecasting.

Findings

Model achieves over 55% accuracy in predicting index direction

Outperforms several benchmark models

Demonstrates effectiveness of convolutional neural networks in stock prediction

Abstract

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%.