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

Name: Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk
Price: 49.99 USD
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$49.99

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Description

Book Synopsis: 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

Details

Are you ready to revolutionize your financial risk management strategies? Introducing Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk, a comprehensive guide that will help you harness the power of artificial intelligence to enhance your risk assessment abilities. Whether you're a developer, programmer, engineer, financial analyst, or risk analyst, this practical book is a must-have in your arsenal.

Learn how to replace outdated and unreliable financial risk models with cutting-edge ML models that provide accurate predictions and insights. Author Abdullah Karasan takes you on a journey through the theory behind financial risk modeling before delving into hands-on ways of implementing ML models using Python.

Discover the potential of deep learning models in volatility modeling and explore techniques like support vector regression and neural networks to accurately measure degrees of risk. Enhance your market risk models by incorporating ML techniques, such as VaR and ES, and gain a deeper understanding of liquidity dimension.

Uncover hidden patterns and trends in credit risk analysis using clustering and Bayesian approaches. Effectively analyze liquidity risk using advanced models like the Gaussian mixture model and Copula model.

Don't miss the opportunity to leverage machine learning models for fraud detection and predicting stock price crashes. Identify key determinants of stock price crashes and make informed investment decisions.

Upgrade your financial risk management skills today and stay ahead of the game in this ever-evolving industry. Start your journey with Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk!

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