Machine Learning for Financial Risk Management with Python is popular PDF and ePub book, written by Abdullah Karasan in 2021-12-07, it is a fantastic choice for those who relish reading online the Business & Economics genre. Let's immerse ourselves in this engaging Business & Economics book by exploring the summary and details provided below. Remember, Machine Learning for Financial Risk Management with Python can be Read Online from any device for your convenience.
Machine Learning for Financial Risk Management with Python Book PDF Summary
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.
Detail Book of Machine Learning for Financial Risk Management with Python PDF
- Author : Abdullah Karasan
- Release : 07 December 2021
- Publisher : "O'Reilly Media, Inc."
- ISBN : 9781492085225
- Genre : Business & Economics
- Total Page : 334 pages
- Language : English
- PDF File Size : 10,9 Mb
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