Abstract
The stock market forecasting is one of the most challenging application of machine learning, as its historical data are naturally noisy and unstable. Most of the successful approaches act in a supervised manner, labeling training data as being of positive or negative moments of the market. However, training machine learning classifiers in such a way may suffer from over-fitting, since the market behavior depends on several external factors like other markets trends, political events, etc. In this paper, we aim at minimizing such problems by proposing an ensemble of reinforcement learning approaches which do not use annotations (i.e. market goes up or down) to learn, but rather learn how to maximize a return function over the training stage. In order to achieve this goal, we exploit a Q-learning agent trained several times with the same training data and investigate its ensemble behavior in important real-world stock markets. Experimental results in intraday trading indicate better performance than the conventional Buy-and-Hold strategy, which still behaves well in our setups. We also discuss qualitative and quantitative analyses of these results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.