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Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management

Author

Listed:
  • Paraskevi Nousi
  • Loukia Avramelou
  • Georgios Rodinos
  • Maria Tzelepi
  • Theodoros Manousis
  • Konstantinos Tsampazis
  • Kyriakos Stefanidis
  • Dimitris Spanos
  • Manos Kirtas
  • Pavlos Tosidis
  • Avraam Tsantekidis
  • Nikolaos Passalis
  • Anastasios Tefas

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.

Suggested Citation

  • Paraskevi Nousi & Loukia Avramelou & Georgios Rodinos & Maria Tzelepi & Theodoros Manousis & Konstantinos Tsampazis & Kyriakos Stefanidis & Dimitris Spanos & Manos Kirtas & Pavlos Tosidis & Avraam Tsa, 2023. "Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management," Papers 2309.16679, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:2309.16679
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    References listed on IDEAS

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    1. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
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    3. Markowitz, Harry M, 1991. "Foundations of Portfolio Theory," Journal of Finance, American Finance Association, vol. 46(2), pages 469-477, June.
    4. Alireza Namdari & Tariq S. Durrani, 2021. "A Multilayer Feedforward Perceptron Model in Neural Networks for Predicting Stock Market Short-term Trends," SN Operations Research Forum, Springer, vol. 2(3), pages 1-30, September.
    5. Lin Li, 2023. "Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach," Journal of Asset Management, Palgrave Macmillan, vol. 24(4), pages 255-267, July.
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