Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3851
Title: Deep learning for financial applications: A survey
Authors: Özbayoğlu, Ahmet Murat
Güdelek, M. U.
Sezer, O. B.
Keywords: Algorithmic trading
computational intelligence
deep learning
finance
financial applications
fraud detection
machine learning
portfolio management
risk assessment
Issue Date: Aug-2020
Publisher: Elsevier Ltd
Source: Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 106384.
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. © 2020 Elsevier B.V.
URI: https://hdl.handle.net/20.500.11851/3851
https://www.sciencedirect.com/science/article/pii/S1568494620303240?via%3Dihub
ISSN: 15684946
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering

Show full item record

CORE Recommender

SCOPUSTM   
Citations

21
checked on Jul 13, 2022

WEB OF SCIENCETM
Citations

42
checked on Jul 14, 2022

Page view(s)

82
checked on Aug 8, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.