Comparing Neural Networks for Predicting Stock Markets

Abstract

In this paper we compare a selection of artificial neural networks when applied for short-term stock market price prediction. The networks are selected due to their expected relevance to the problem. Further, the work aims at covering recent advances in the field of artificial neural networks. The networks considered include: Feed forward neural networks, echo state networks, conditional restricted Boltzmann machines, time-delay neural networks and convolutional neural networks. These models are also compared to another type of machine learning algorithm, support vector machine. The models are trained on daily stock exchange data, to make short-term predictions for one day and two days ahead, respectively. Performance is evaluated by following the models directly in a simple financial strategy; trade every prediction they make once during each day.

Possibly due to the noisy nature of stock data, the results are slightly inconsistent between different data sets. If performance is averaged across all the data sets, the feed forward network generates most profit during the three year test period: 23.13% and 30.43% for single-step and double-step prediction, respectively. Convolutional networks get close to the feed forward network in terms of profitability, but are found unreliable due to their unreasonable bias towards predicting positive price changes. The support vector machine delivered average profits of 17.28% for single-step and 11.30% for double-step, respectively. Low profits or large deviations were observed for the other models.