Intelligent trading of seasonal effects: A decision support algorithm based on reinforcement learning

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. We document the performance on two major stock indices.