We show the chances of a trading system based on seasonalities in financial markets. We introduce a decision support algorithm to filter trading signals. The algorithm is based on reinforcement learning and weka stock trading networks.

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We improve the reward to risk ratios of the seasonality strategy. Seasonalities and empirical regularities on financial markets have been well documented in the literature for three decades. While one should suppose that documenting an arbitrage opportunity makes it vanish there are several regularities that have persisted over the years. These include, for example, upward biases at the turn-of-the-month, during exchange holidays and the pre-FOMC announcement drift. Trading regularities is already in and of itself an interesting strategy. However, unfiltered trading leads to potential large drawdowns. In the paper we present a decision support algorithm which uses the powerful ideas of reinforcement learning in order to improve the economic benefits of the basic seasonality strategy.

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We document the performance on two major stock indices. Check if you have access through your login credentials or your institution. Bachelor Student at Leibniz University of Hanover, Germany. His research interests include machine learning algorithms for intelligent trading strategies. Banking and Finance from Liverpool John Moores University. He currently works as Risk Manager at a Swiss private bank.

His research interests include quantitative trading strategies and artificial intelligence. Decision Support Systems at Leibniz University of Hanover, Germany. His research interests include complex systems and forecasting. Information Systems Research at Leibniz University of Hanover, Germany. His research interests include artificial intelligence, especially artificial neural networks. Unsourced material may be challenged and removed. 4,000 locations in over 105 countries.