Predicting Stock Returns Using Neural Networks

Abstract

A single hidden layer neural network can be trained to predict whether a stock will be in the top, middle, or bottom third of sample stocks based on its return over the next month based on return, trading volume, and volatility measures available at the end of this month. In my preliminary work using S&P 500 stocks, the network has limited success in predicting which stocks are likely to go up but the prediction strength is not strong enough to help build profitable portfolios. While neural networks have pushed artificial intelligence forward in many fields, and while the investment industry has been shifting more towards quantitative prediction using neural networks and other machine learning models, their place in empirical finance research has been limited. My work aims to contribute to this growing literature.