Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk (Record no. 45925)
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000 -LEADER | |
---|---|
fixed length control field | 01777 a2200169 4500 |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9789355420923 |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.31 KAR |
100 ## - MAIN ENTRY--PERSONAL NAME | |
Personal name | Karasan, Abdullah |
245 ## - TITLE STATEMENT | |
Title | Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Name of publisher, distributor, etc | Shroff Publishers & Distributors Pvt. Ltd. |
Place of publication, distribution, etc | Mumbai |
Date of publication, distribution, etc | 2022 |
300 ## - PHYSICAL DESCRIPTION | |
Extent | 314 |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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<br/> |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Machine learning |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Python (Computer program language) |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Financial Risk Management |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | Book |
Source of classification or shelving scheme | Dewey Decimal Classification |
No items available.