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Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk
Abdullah KarasanQuanto ti piace questo libro?
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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. With this book, you'll learn • 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
Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. With this book, you'll learn • 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
Categorie:
Anno:
2022
Edizione:
1
Casa editrice:
O'Reilly Media
Lingua:
english
Pagine:
334
ISBN 10:
1492085251
ISBN 13:
9781492085256
File:
PDF, 24.96 MB
I tuoi tag:
IPFS:
CID , CID Blake2b
english, 2022
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