Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management

TL;DR

This paper explores the use of deep learning techniques combined with online sentiment analysis to improve financial portfolio management, providing insights, methodologies, and practical guidance for deploying such models.

Contribution

It introduces a comprehensive review of deep learning methods for financial trading, integrating sentiment analysis, and discusses training challenges and solutions.

Findings

Deep learning models can effectively incorporate sentiment data for better trading decisions.

Supervised and reinforcement learning schemes both benefit from sentiment analysis.

The paper highlights common training issues and practical solutions for financial agents.

Abstract

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