Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk by Karasan, Abdullah

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

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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

Author: Abdullah Karasan
Publisher: O'Reilly Media
Published: 01/11/2022
Pages: 331
Binding Type: Paperback
Weight: 1.17lbs
Size: 9.19h x 7.00w x 0.70d
ISBN: 9781492085256

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.

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Latin America 10-14 days 4-6 days
Middle East 16-20 days 4-6 days

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