Predictability of stock returns using neural networks: Elusive in the long term

Abstract

The rapidly growing neural network literature continually reports successful stock price forecasting results. Many of these studies use relatively short evaluation periods, spanning only a couple of years. In this paper, sustainability of the neural network forecast quality over the long term is analysed. Feedforward and recurrent networks are used to predict the direction of monthly stock price movements, with past price data as predictors. The analysis is conducted on the NYSE stocks over the 1971–2015 period and all the evaluations are performed out-of-sample. Statistically significant directional predictability for selected assets is found. However, the trading simulations reveal that directional predictability does not guarantee trading performance better than the benchmark buy-and-hold strategy. The opportunities for investors to use the tested models for profit appear to be episodic and periodically enhanced e.g. in periods of recession.