Abstract
In this paper, predictions of future price movements of a major American stock index were made by analyzing past movements of the same and other correlated indices. A model that has shown very good results in audio and speech generation 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, 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 on 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% and 37%, respectively, when predicting the next day’s closing price and the next day’s trend.
Highlights
Deep learning has brought a new paradigm into machine learning in the past decade and has shown remarkable results in areas such as computer vision, speech recognition and natural language processing
Researchers and market participants (Market participants is a general expression for individuals or groups who are active in the market, such as banks, investors, investment funds, or traders; often, we use the term trader as a synonym for market participant [2]) are still, to the most part, sticking to more historically well known and tested approaches, but there has been a slight shift of interest to deep learning methods in the past years [3]
The p is again the length of the input time series, while l is the number of layers in the residual part of the network, and f is the number of filters used in each convolutional layer
Summary
Deep learning has brought a new paradigm into machine learning in the past decade and has shown remarkable results in areas such as computer vision, speech recognition and natural language processing. One of the areas where it is yet to become a mainstream tool is in forecasting financial time series This despite the fact that time series does provide a suitable data representation for deep learning methods such as a convolutional neural network (CNN) [1]. The reason behind the shift, apart from the structure of the time series, is that the financial market is an increasingly complex system. This means that there is a need for more advanced models, such as deep neural networks, that do a better job in finding the nonlinear relations in the data
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