Deep learning for financial applications : A survey

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 atten...

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Published inApplied soft computing Vol. 93; p. 106384
Main Authors Ozbayoglu, Ahmet Murat, Gudelek, Mehmet Ugur, Sezer, Omer Berat
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.08.2020
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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. 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. •All searchable articles of deep learning (DL) for financial applications are reviewed.•DL for finance studies based on their application areas were clustered.•DL models according to their performances in different implementation areas were compared.•To best of our knowledge, this is the first comprehensive DL survey for financial applications.•Current status of DL in finance was provided, also the future opportunities were highlighted.
AbstractList 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. 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. •All searchable articles of deep learning (DL) for financial applications are reviewed.•DL for finance studies based on their application areas were clustered.•DL models according to their performances in different implementation areas were compared.•To best of our knowledge, this is the first comprehensive DL survey for financial applications.•Current status of DL in finance was provided, also the future opportunities were highlighted.
ArticleNumber 106384
Author Gudelek, Mehmet Ugur
Sezer, Omer Berat
Ozbayoglu, Ahmet Murat
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  givenname: Mehmet Ugur
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  fullname: Sezer, Omer Berat
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Snippet Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have...
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StartPage 106384
SubjectTerms Algorithmic trading
Computational intelligence
Deep learning
Finance
Financial applications
Fraud detection
Machine learning
Portfolio management
Risk assessment
Title Deep learning for financial applications : A survey
URI https://dx.doi.org/10.1016/j.asoc.2020.106384
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