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Machine learning for financial risk management with Python :algorithms for modeling risk /

紀錄類型:	書目-語言資料,印刷品 : 單行本
Title/Author:	Machine learning for financial risk management with Python :/ Abdullah Karasan.
Reminder of title:	algorithms for modeling risk /
Author:	Karasan, Abdullah.
Published:	Cambridge :O'Reilly,c2022.
Description:	xv, 314,[1] p. :ill. ;24 cm.
Subject:	Financial risk management. -
ISBN:	9781492085256
ISBN:	1492085251

Machine learning for financial risk management with Python :algorithms for modeling risk /
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020 $a 1492085251 $q (pbk.)
020 $z 1492085227 $q (ebk.)
020 $z 9781492085225 $q (ebk.)
035 $a (OCoLC)1289513846
040 $a N $b eng $e aacr2 $e pn $c N $d N $d UKAHL $d ORMDA $d OCLCO
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082 0 4 $a 658.155 $2 23
100 1 $a Karasan, Abdullah. $3 1000147920
245 1 0 $a Machine learning for financial risk management with Python : $b algorithms for modeling risk / $c Abdullah Karasan.
260 $a Cambridge : $b O'Reilly, $c c2022.
300 $a xv, 314,[1] p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references and index.
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. $3 172029
650 0 $a Machine learning. $3 147118
650 0 $a Python (Computer program language) $3 163241