Categories Categories

Free Shipping Free Shipping

Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk 1st Edition

MRP: Rs.1,300.00
Price in points: 1300 points
9789355420923
In stock
(ships in 1-2 days)
+

Minimum quantity for "Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk 1st Edition" is 1.

All Indian Reprints of O'Reilly are printed in Grayscale

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

Books

Author:
Abdullah Karasan
Binding:
Paperback
Condition Type:
New
Country Origin:
India
Edition:
First
Gift Wrap:
N
Leadtime to ship in days (default):
ships in 1-2 days
Leadtime to ship in days(if not in stock):
ships in 12-15 days
Pages:
356
Publication Date:
16/12/2021
Publisher:
Shroff/O'Reilly
Year:
2021

Dimensions

Dimensions (W x H x D):
7 x 9 x 0.7 inch

Table of Contents (9789355420923_toc.pdf, 173 Kb) [Download]