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

By: Karasan, AbdullahMaterial type: TextTextPublisher: Sebastopol, CA : O'Reilly Media, 2022Edition: First editionDescription: xv, 314 pages : illustrations ; 24 cmISBN: 9781492085256; 9789355420923Subject(s): Financial risk management | Machine learning | Python (Computer program language) | Financial risk management | Machine learning | Python (Computer program language)DDC classification: 658.155
Contents:
Fundamentals of risk management -- Introduction to time series modeling -- Deep learning for time series modeling -- Machine learning-based volatilty prediction -- Modeling market risk -- Credit risk estimation -- Liquidity modeling -- Modeling operational risk -- A corporate governance risk measure: Stock price crash -- Synthetic data generation and the hidden markov model in finance
Summary: 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.
List(s) this item appears in: New Arrivals - August 1st to 31st 2023
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Item type Current location Call number Status Date due Barcode
Books Institute of Public Enterprise, Library
S Campus 		658.155 KAR (Browse shelf) Checked out 11/20/2023 47678
Books Institute of Public Enterprise, Library
S Campus 		658.155 KAR (Browse shelf) Checked out 02/11/2024 47677

Includes bibliographical references and index.

Fundamentals of risk management -- Introduction to time series modeling -- Deep learning for time series modeling -- Machine learning-based volatilty prediction -- Modeling market risk -- Credit risk estimation -- Liquidity modeling -- Modeling operational risk -- A corporate governance risk measure: Stock price crash -- Synthetic data generation and the hidden markov model in finance

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.

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