Saved in:
Bibliographic Details
Main Authors: Mironowicz, Piotr, H., Akshata Shenoy, Mandarino, Antonio, Yilmaz, A. Ege, Ankenbrand, Thomas
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.10119
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866911878753550336
author Mironowicz, Piotr
H., Akshata Shenoy
Mandarino, Antonio
Yilmaz, A. Ege
Ankenbrand, Thomas
author_facet Mironowicz, Piotr
H., Akshata Shenoy
Mandarino, Antonio
Yilmaz, A. Ege
Ankenbrand, Thomas
contents Machine learning and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems across various domains. This work gives an extensive overview of QML uses in quantitative finance, an important discipline in the financial industry. We examine the connection between quantum computing and machine learning in financial applications, spanning a range of use cases including fraud detection, underwriting, Value at Risk, stock market prediction, portfolio optimization, and option pricing by overviewing the corpus of literature concerning various financial subdomains.
format Preprint
id arxiv_https___arxiv_org_abs_2405_10119
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Applications of Quantum Machine Learning for Quantitative Finance
Mironowicz, Piotr
H., Akshata Shenoy
Mandarino, Antonio
Yilmaz, A. Ege
Ankenbrand, Thomas
Quantum Physics
Machine learning and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems across various domains. This work gives an extensive overview of QML uses in quantitative finance, an important discipline in the financial industry. We examine the connection between quantum computing and machine learning in financial applications, spanning a range of use cases including fraud detection, underwriting, Value at Risk, stock market prediction, portfolio optimization, and option pricing by overviewing the corpus of literature concerning various financial subdomains.
title Applications of Quantum Machine Learning for Quantitative Finance
topic Quantum Physics
url https://arxiv.org/abs/2405.10119