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Deep Learning for Financial Applications : A Survey

Statistical Finance 2020-02-17 v1 Machine Learning Machine Learning

Abstract

Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.

Keywords

Cite

@article{arxiv.2002.05786,
  title  = {Deep Learning for Financial Applications : A Survey},
  author = {Ahmet Murat Ozbayoglu and Mehmet Ugur Gudelek and Omer Berat Sezer},
  journal= {arXiv preprint arXiv:2002.05786},
  year   = {2020}
}

Comments

13 Figures, 15 Tables, submitted to Applied Soft Computing

R2 v1 2026-06-23T13:41:25.034Z