Algorithmic stock trading based on ensemble deep neural networks trained with time graph

Abstract

Financial forecasting is generally implemented by analyzing the time series data related to the stock. This is accomplished widely with deep neural networks (DNNs) since DNNs can directly extract the related information that is otherwise hard to obtain. Time series is the core data representation of financial forecasting problem since it comes naturally. However recent studies show that even if time series representation is necessary, it still lacks certain aspects related to the problem. One of them is the relationship between the stocks of the market which can be captured through graph representation. Therefore, DNNs might solve the financial forecasting problem better when graph and time series representations are combined. In this study, we present different graph representations that can be used for this purpose. We also present an ensemble network that gives an investment strategy related to the stock market from stock predictions. Our proposed model returns an average of 20.09% annual profit on DOW30 dataset through daily buy-sell decisions based on close prices. Therefore, it can serve as a daily financial investment strategy, offering higher annual returns than conventional heuristic approaches.