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