The use of neural networks for tendency prediction in financial series

Abstract

In the present project, a comparison of di erent types of arti cial neu- ral networks has been used to analyze their behavior with noisy time series prediction, with the goal of maximizing the bene t obtainable by investing in them. To do so, a wide range of datasets has been used, containing stock market prices until September 2014 and starting from January 2000 on- wards. The starting experiment has been a regular multilayer perceptron using a sliding window of the latest values as the input of the network and three outputs representing three possible actions as buy, sell or keep. Fur- ther experiments have been tested, such as the replacement of the three outputs classi er by a single one, converting the system in a forecasting model with only one output, or the use of di erent averages of recent val- ues instead of a simple sliding window as the network's input. Also, it has been tested the use of a single dataset from where each sample is used rst to test and validate, and to train the network later on in a new step instead of the traditional way of training-validation-test splitting of data. Finally, two new models that seize all the data have been tested, one with a speci c period of data validation, and the other one with an implicit period, as it has been skipped by doing some networks pre-training. After a comprehensive applying of these methods to the time series, certain pre- dictability was found. Some networks were able to predict the direction of change for the next day with an error rate of around the 40%, which in some optimistic cases decreases to about 30% when rejecting examples where the system has low con dence in its prediction. A practical simu- lation has been explained, showing an average gain close to the 0.33% by acting the half of the times.