Machine learning for financial risk management with Python: algorithms for modeling risk
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for a...
Gespeichert in:
Beteilige Person: | |
---|---|
Format: | Elektronisch E-Book |
Sprache: | Englisch |
Veröffentlicht: |
Cambridge
O'Reilly
2022
|
Schlagwörter: | |
Links: | https://learning.oreilly.com/library/view/-/9781492085249/?ar |
Zusammenfassung: | Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models. |
Beschreibung: | Print version record |
Umfang: | 1 Online-Ressource (1 volume) |
ISBN: | 9781492085225 1492085227 9781492085201 1492085200 |
Internformat
MARC
LEADER | 00000cam a22000002 4500 | ||
---|---|---|---|
001 | ZDB-30-ORH-062309390 | ||
003 | DE-627-1 | ||
005 | 20240228121528.0 | ||
007 | cr uuu---uuuuu | ||
008 | 210316s2022 xx |||||o 00| ||eng c | ||
020 | |a 9781492085225 |c electronic bk. |9 978-1-4920-8522-5 | ||
020 | |a 1492085227 |c electronic bk. |9 1-4920-8522-7 | ||
020 | |a 9781492085201 |c electronic bk. |9 978-1-4920-8520-1 | ||
020 | |a 1492085200 |c electronic bk. |9 1-4920-8520-0 | ||
035 | |a (DE-627-1)062309390 | ||
035 | |a (DE-599)KEP062309390 | ||
035 | |a (ORHE)9781492085249 | ||
035 | |a (DE-627-1)062309390 | ||
040 | |a DE-627 |b ger |c DE-627 |e rda | ||
041 | |a eng | ||
082 | 0 | |a 658.155 |2 23 | |
100 | 1 | |a Karasan, Abdullah |e VerfasserIn |4 aut | |
245 | 1 | 0 | |a Machine learning for financial risk management with Python |b algorithms for modeling risk |c Abdullah Karasan |
264 | 1 | |a Cambridge |b O'Reilly |c 2022 | |
300 | |a 1 Online-Ressource (1 volume) | ||
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
500 | |a Print version record | ||
520 | |a Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models. | ||
650 | 0 | |a Financial risk management | |
650 | 0 | |a Machine learning | |
650 | 0 | |a Python (Computer program language) | |
650 | 4 | |a Finances ; Gestion du risque | |
650 | 4 | |a Apprentissage automatique | |
650 | 4 | |a Python (Langage de programmation) | |
650 | 4 | |a Financial risk management | |
650 | 4 | |a Machine learning | |
650 | 4 | |a Python (Computer program language) | |
776 | 1 | |z 9781492085256 | |
776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781492085256 |
966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781492085249/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
912 | |a ZDB-30-ORH | ||
912 | |a ZDB-30-ORH | ||
951 | |a BO | ||
912 | |a ZDB-30-ORH | ||
049 | |a DE-91 |
Datensatz im Suchindex
DE-BY-TUM_katkey | ZDB-30-ORH-062309390 |
---|---|
_version_ | 1821494834376998912 |
adam_text | |
any_adam_object | |
author | Karasan, Abdullah |
author_facet | Karasan, Abdullah |
author_role | aut |
author_sort | Karasan, Abdullah |
author_variant | a k ak |
building | Verbundindex |
bvnumber | localTUM |
collection | ZDB-30-ORH |
ctrlnum | (DE-627-1)062309390 (DE-599)KEP062309390 (ORHE)9781492085249 |
dewey-full | 658.155 |
dewey-hundreds | 600 - Technology (Applied sciences) |
dewey-ones | 658 - General management |
dewey-raw | 658.155 |
dewey-search | 658.155 |
dewey-sort | 3658.155 |
dewey-tens | 650 - Management and auxiliary services |
discipline | Wirtschaftswissenschaften |
format | Electronic eBook |
fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03043cam a22005052 4500</leader><controlfield tag="001">ZDB-30-ORH-062309390</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228121528.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210316s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781492085225</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-4920-8522-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1492085227</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-4920-8522-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781492085201</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-4920-8520-1</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1492085200</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-4920-8520-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)062309390</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP062309390</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781492085249</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)062309390</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">658.155</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Karasan, Abdullah</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning for financial risk management with Python</subfield><subfield code="b">algorithms for modeling risk</subfield><subfield code="c">Abdullah Karasan</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">O'Reilly</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1 volume)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Print version record</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Financial risk management</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Finances ; Gestion du risque</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Financial risk management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">9781492085256</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781492085256</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781492085249/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
id | ZDB-30-ORH-062309390 |
illustrated | Not Illustrated |
indexdate | 2025-01-17T11:20:40Z |
institution | BVB |
isbn | 9781492085225 1492085227 9781492085201 1492085200 |
language | English |
open_access_boolean | |
owner | DE-91 DE-BY-TUM |
owner_facet | DE-91 DE-BY-TUM |
physical | 1 Online-Ressource (1 volume) |
psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
publishDate | 2022 |
publishDateSearch | 2022 |
publishDateSort | 2022 |
publisher | O'Reilly |
record_format | marc |
spelling | Karasan, Abdullah VerfasserIn aut Machine learning for financial risk management with Python algorithms for modeling risk Abdullah Karasan Cambridge O'Reilly 2022 1 Online-Ressource (1 volume) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Print version record Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models. Financial risk management Machine learning Python (Computer program language) Finances ; Gestion du risque Apprentissage automatique Python (Langage de programmation) 9781492085256 Erscheint auch als Druck-Ausgabe 9781492085256 |
spellingShingle | Karasan, Abdullah Machine learning for financial risk management with Python algorithms for modeling risk Financial risk management Machine learning Python (Computer program language) Finances ; Gestion du risque Apprentissage automatique Python (Langage de programmation) |
title | Machine learning for financial risk management with Python algorithms for modeling risk |
title_auth | Machine learning for financial risk management with Python algorithms for modeling risk |
title_exact_search | Machine learning for financial risk management with Python algorithms for modeling risk |
title_full | Machine learning for financial risk management with Python algorithms for modeling risk Abdullah Karasan |
title_fullStr | Machine learning for financial risk management with Python algorithms for modeling risk Abdullah Karasan |
title_full_unstemmed | Machine learning for financial risk management with Python algorithms for modeling risk Abdullah Karasan |
title_short | Machine learning for financial risk management with Python |
title_sort | machine learning for financial risk management with python algorithms for modeling risk |
title_sub | algorithms for modeling risk |
topic | Financial risk management Machine learning Python (Computer program language) Finances ; Gestion du risque Apprentissage automatique Python (Langage de programmation) |
topic_facet | Financial risk management Machine learning Python (Computer program language) Finances ; Gestion du risque Apprentissage automatique Python (Langage de programmation) |
work_keys_str_mv | AT karasanabdullah machinelearningforfinancialriskmanagementwithpythonalgorithmsformodelingrisk |