Stock performance modeling using neural networks: A comparative study with regression models

Abstract We examine the use of neural networks as an alternative to classical statistical techniques for forecasting within the framework of the APT (arbitrage pricing theory) model for stock ranking. We show that neural networks outperform these statistical techniques in forecasting accuracy terms, and give better model fitness in-sample by one order of magnitude. We identify intervals for the network parameter values for which these performance figures are statistically stable. Neural networks have been criticised for not being able to provide an explanation of how they interact with their environment and how they reach an outcome. We show that by using sensitivity analysis, neural networks can provide a reasonable explanation of their predictive behaviour and can model their environment more convincingly than regression models.