Machine learning for financial risk management with Python : (Record no. 91492)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01851nam a22001817a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240110b2022 |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789355420923 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 658.155 KAR-A |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Karasan, Abdullah |
| 245 ## - TITLE STATEMENT | |
| Title | Machine learning for financial risk management with Python : |
| Remainder of title | algorithms for modeling risk / |
| Statement of responsibility, etc. | Abdullah Karasan |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | SPD |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 314 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 1400.00 |
| 500 ## - GENERAL NOTE | |
| General note | 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.<br/><br/>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:<br/><br/>* Review classical time series applications and compare them with deep learning models<br/>* Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning<br/>* Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension<br/>* Develop a credit risk analysis using clustering and Bayesian approaches<br/>* Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model<br/>* Use machine learning models for fraud detection<br/>* Predict stock price crash and identify its determinants using machine learning models |
| 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 |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Checked out | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 650 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 10/01/2024 | 2 | 658.155 KAR-A | 47541 | 12/08/2025 | 23/05/2025 | 23/05/2025 | 10/01/2024 | Books |