Machine learning for financial risk management with python.

Format:

Book

Author/Creator:

Karasan, Abdullah.

Language:

English

Subjects (All):

Financial risk management.

Machine learning.

Python (Computer program language).

Physical Description:

1 online resource (334 pages)

Edition:

1st ed.

Place of Publication:

Sebastopol : O'Reilly Media, Incorporated, 2022.

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.

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

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781492085201

1492085200

9781492085225

1492085227

9781492085249

1492085243

OCLC:

1289513846