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This project is addressing the problem of portfolio optimization by using reinforcement learning, an area of machine learning that has recently attracted many researchers. Its advantages compared to the conventional models of portfolio optimization are coming from its ability in incorporating many features of the assets into the asset allocation problem without relying on the predicted returns outputted from another model. This model is able of considering risk measures and providing asset allocation strategies that are in line with the risk preferences of the investors. It also considers exogenous market constraints like the transaction costs, which are very important factors in the performance of the suggested strategies of the model.
Erick Delage;Jonathan Yumeng Li
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