Deep Learning for Stock Market Prediction Exploiting Time Shifted Correlations of Stock Price Gradients
Although the application of machine learning to financial time series in stock markets, as an enhancement of technical analysis, experienced an increased interest in the last decades, research on more recent techniques from the area of deep learning for this purpose, and for the testing of economic theory, remains sparse. More generally, a surge in research on deep layered models for time series analysis led to applications in a variety of fields, establishing this topic as a challenging subject. The first part of the tested hypothesis states that deep layered feedforward artificial neural networks are able to learn complex time shifted correlations between step wise trends of a large number of noisy time series, using only the preceding time steps' gradients as inputs. The second part states that such correlations are present in stock prices, and that these models can be used to predict changes in a price's trend based on other stocks' trend gradients of the previous time step, delivering empirical evidence against both the random market hypothesis and the efficient market hypothesis. In more narrowly defined terms, this applied part is situated at the intersection of computational finance and financial econometrics. Using the stocks of the S&P 500 Index as an experimental dataset, the models developed for this thesis are able to successfully predict trend changes based solely on information about other stocks' preceding gradients, with accuracies above chosen market baselines and adhering to methods used for a rigorous statistical validation of the results. Apart from the applicability of the investigated approach to a vast array of problems dealing with complex relationships between numerous and noise laden time series, this thesis presents compelling evidence against both economic hypotheses.