Stock Trading Using a Deep Reinforcement Learning and Text Analysis
| Autor: | Bc. Dominik Benk |
|---|---|
| Rok: | 2022 - letní |
| Vedoucí: | doc. PhDr. Jozef Baruník Ph.D. |
| Konzultant: | |
| Typ práce: | Diplomová Finance, finanční trhy a bankovnictví |
| Jazyk: | Anglicky |
| Stránky: | 99 |
| Ocenění: | Ocenění DOT Award za vynikající magisterskou práci. |
| Odkaz: | https://dspace.cuni.cz/handle/20.500.11956/176640 |
| Abstrakt: | The thesis focuses on exploiting imperfections on the stock market by utilizing state-of-the-art learning methods and applying them to algorithmic trading. The automated decisions are expected to have the capability of outperforming professional traders by considering much more information, reacting almost instantly and being unaffected by emotions. As an alternative to traditional supervised learning, the proposed model of reinforcement learning employs a principle of trial-and-error, which is essential for learning behaviours of all organisms. In the context of stocks, this allows to consider the involved uncertainty and therefore more precisely estimate the long-run returns. To collect the most relevant information for each trading decision, additionally to technical indicators the models build on investor’s opinion - financial sentiment. This is derived from two textual sources, news and social media, and the main goal is to compare their relative contribution to trading. Models are applied to 11 different stocks and later combined into portfolio for greater robustness of results. The textual analysis proves to be important for the learning process, especially in case of stocks with good media coverage. The Twitter is found to provide more valuable information compared to news, but their combination shows even higher predictive potential. Nevertheless, proposed models have difficulties to reliably outperform passive strategies or the market. |
