Machine Learning in Investment Portfolio Management: Applications in Algorithmic Trading Strategies

Student: Davydov Dmitry

Educational Programme: Financial Engineering (Master)

Year of Graduation: 2020

This paper presents options for applying deep learning in the framework of investment business processes in the Russian stock market. Based on the Actor-Critic (Q-Learning) architecture, a model was developed that solves the problem of optimizing the selection of the investment portfolio structure based on predictive factors. The results of numerical experiments demonstrated the superiority of this approach over a number of empirical practices used by the investor in selecting the optimal asset structure. It was concluded that future developments in this field based on high-frequency data are promising.

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