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Machine Learning for Financial Risk Management with Python: Algorithms for Model
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eBay-Artikelnr.:156979497504
Artikelmerkmale
- Artikelzustand
- ISBN-13
- 9781492085256
- Book Title
- Machine Learning for Financial Risk Management with Python
- ISBN
- 9781492085256
Über dieses Produkt
Product Information
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. 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 Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk
Product Identifiers
Publisher
O'reilly Media, Inc, USA
ISBN-13
9781492085256
eBay Product ID (ePID)
5049971355
Product Key Features
Publication Name
Machine Learning for Financial Risk Management with Python
Subject
Computer Science
Publication Year
2021
Type
Textbook
Format
Paperback
Language
English
Number of Pages
350 Pages
Dimensions
Item Height
232 mm
Item Width
178 mm
Additional Product Features
Country/Region of Manufacture
United States
Artikelbeschreibung des Verkäufers
Rechtliche Informationen des Verkäufers
USt-IdNr.: AU 82107909133, GB 293967539
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