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Machine Learning for Financial Risk Management with Python
Machine Learning for Financial Risk Management with Python
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A01=Abdullah Karasan
Age Group_Uncategorized
Age Group_Uncategorized
AI artificial intelligence Machine Learning
Author_Abdullah Karasan
automatic-update
Category1=Non-Fiction
Category=UN
COP=United States
Deep Learning
Delivery_Delivery within 10-20 working days
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Finance
Format=BC
Format_Paperback
Keras
Language_English
Matplotlib
Numpy
PA=Available
Pandas
Price_€50 to €100
PS=Active
Python
Risk Management
Scikit-Learn
Seaborn
softlaunch
Supervised Learning
Tensorflow
Unsupervised Learning
Product details
- ISBN 9781492085256
- Format: Paperback
- Dimensions: 178 x 232mm
- Publication Date: 31 Dec 2021
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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
Abdullah Karasan was born in Berlin, Germany. After he studied Economics and Business Administration at Gazi University-Ankara, he obtained his master's degree from the University of Michigan-Ann Arbor and his PhD in Financial Mathematics from Middle East Technical University (METU)-Ankara. He worked as a Treasury Controller at the Undersecretariat of Treasury of Turkey. More recently, he has started to work as a Senior Data Science consultant and instructor for companies in Turkey and the USA. Currently, he is a Data Science consultant at Datajarlabs and Data Science mentor at Thinkful.
Machine Learning for Financial Risk Management with Python
€84.99
