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

Description

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 Review: Highly recommended - A new perspective to financial risk management. This book provides original approaches to the old problems and it is easy to follow as author gives all python codes. I highly recommend the book. Review: One of a kind. - Great way to present financial risk management via machine learning based algorithms. I learnt new stuff in every single chapter. All the python codes along with the explanations are there, which makes the code easy to understand and replicate. Worth the money!