Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks

In this paper, predictions of future price movements of a major American stock index was made by analysing past movements of the same and other correlated indices. A model that has shown very good results in speech recognition was modified to suit the analysis of financial data and was then compared to a base model, restricted by assumptions made for an efficient market. The performance of any model, that is trained by looking at past observations, is heavily influenced by how the division of the data into train, validation and test sets is made. This is further exaggerated by the temporal structure of the financial data, which means that the causal relationship between the predictors and the response is dependent in time. The complexity of the financial system further increases the struggle to make accurate predictions, but the model suggested here was still able to outperform the naive base model by more than 20 percent. The model is, however, too primitive to be used as a trading system, but suitable modifications, in order to turn the model into one, will be discussed in the end of the paper