Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management
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.
Keywords
Cite
@article{arxiv.2309.16679,
title = {Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management},
author = {Paraskevi Nousi and Loukia Avramelou and Georgios Rodinos and Maria Tzelepi and Theodoros Manousis and Konstantinos Tsampazis and Kyriakos Stefanidis and Dimitris Spanos and Manos Kirtas and Pavlos Tosidis and Avraam Tsantekidis and Nikolaos Passalis and Anastasios Tefas},
journal= {arXiv preprint arXiv:2309.16679},
year = {2023}
}