文库 行业资料 AI

Machine Learning for Financial Risk Management with Python_ Algorithms for Modeling Risk_Abdullah Karasan.pdf

AI PDF   334页   下载0   2026-01-25   浏览10   收藏0   点赞0   评分-   576318字   免费文档
温馨提示:当前文档最多只能预览 5 页,若文档总页数超出了 5 页,请下载原文档以浏览全部内容。
Machine Learning for Financial Risk Management with Python_ Algorithms for Modeling Risk_Abdullah Karasan.pdf 第1页
Machine Learning for Financial Risk Management with Python_ Algorithms for Modeling Risk_Abdullah Karasan.pdf 第2页
Machine Learning for Financial Risk Management with Python_ Algorithms for Modeling Risk_Abdullah Karasan.pdf 第3页
剩余329页未读, 下载浏览全部
Abdullah Karasan Machine Learning for Financial Risk Management with Python Algorithms for Modeling Risk Karasan MACHINE LEARNING / DATA “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 Founder and CEO of The Python Quants and The AI Machine “If you need a go-to guide about the application of statistical and machine learning methods to analysis of financial risk, this is a great place to start.” —Graham L. Giller Author of Adventures in Financial Data Science Machine Learning for Financial Risk Management with Python ISBN: 978-1-492-08525-6 US $79.99 CAN $105.99 Twitter: @oreillymedia linkedin.com/company/oreilly-media youtube.com/oreillymedia 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 mo
Machine Learning for Financial Risk Management with Python_ Algorithms for Modeling Risk_Abdullah Karasan.pdf