What is the project about?
DeepFinance aims to develop a complete platform for semantic and sentiment analysis from social media streams using Deep Learning. This platform can be then used to further develop unified tools for financial portfolio management that can effectively fuse multi-modal information that is extracted from various sources (including social media streams). More specifically, DeepFinance aims to:
[G1] Develop deep learning tools for automated portfolio management, by SpeedLab (SL), aiming to achieve better performance (according to various financial metrics) compared to the currently used strategies that mainly consist of handcrafted decision rules. Research will focus on developing robust deep learning methods. Furthermore, DeepFinance will research on developing robust agents, using deep learning and deep reinforcement learning methods, aiming to directly maximize the profit, as well as using novel price control strategies, e.g., directly placing limit orders, where the agent decides for the quantity, price and time to place an order at the same time.
[G2] Develop a platform for semantic analysis of social media streams, e.g., twitter, blogs, etc., by DataScouting (DS), in order to provide semantic and sentiment analysis services for specific stocks, financial indices, etc. It is worth noting that large media companies, e.g., Bloomberg, already provide similar services. The developed platform will integrate state-of-the-art deep learning and natural language processing tools, allowing for semantic and sentiment analysis from heterogeneous data streams. At the same time, the developed platform will allow for further finding cause-effect correlations between various events, providing an additional tool which DS can integrate in its products.
[G3] Integrate the semantic and sentiment analysis services from DS to the products of SL in order to develop portfolio management products that take into account the information, regarding the market's sentiment, that can be extracted from social media. Develop and integrate multi-modal deep learning and deep reinforcement learning methods for portfolio management that take into account additional heterogeneous information (limit order book, stock/index prices, sentiment, etc.).