Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk

Author

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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ISBN-10

1492085251

ISBN-13

978-1492085256

Publisher

OReilly Media

Price

49.99

File Type

PDF

Page No.

334

Abdullah Karasan

Abdullah Karasan was born in Berlin, Germany. After studying economics and business administration, ...

From the Back Cover

Nowadays, Python undoubtedly is the No. 1 programming language in the financial industry. At the same time, Machine Learning has become a key technology for the industry. The book by Abdullah Karasan does a great job in showing the capabilities of Machine Learning with Python in the context of financial risk management -- a function vital to any financial institution.

Dr. Yves J. Hilpisch

This book is a comprehensive and practical presentation of a wide variety of methods, drawn from both the statistical and machine learning traditions, for the analysis of financial risk. It includes practical code snippets and charts to illustrate the methods used on real data. If you need a go-to guide to the application of these methods to data, this is a great place to start."
Graham L Giller, author of Adventures in Financial Data Science.

About the Author

Abdullah Karasan was born in Berlin, Germany. After studying economics and business administration, he obtained his master's degree in applied economics from the University of Michigan, Ann Arbor, and his PhD in financial mathematics from the Middle East Technical University, Ankara. He is a former Treasury employee of Turkey and currently works as a principal data scientist at Magnimind and as a lecturer at the University of Maryland, Baltimore. He has also published several papers in the field of financial data science.

Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk

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