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Financial portfolio management describes the task of distributing funds andconducting trading operations on a set of financial assets, such as stocks,index funds, foreign exchange or cryptocurrencies, aiming to maximize theprofit while minimizing the loss incurred by said operations. Deep Learning(DL) methods have been consistently excelling at various tasks and automatedfinancial trading is one of the most complex one of those. This paper aims toprovide insight into various DL methods for financial trading, under both thesupervised and reinforcement learning schemes. At the same time, taking intoconsideration sentiment information regarding the traded assets, we discuss anddemonstrate their usefulness through corresponding research studies. Finally,we discuss commonly found problems in training such financial agents and equipthe reader with the necessary knowledge to avoid these problems and apply thediscussed methods in practice.

Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management