LEADER 00000cam a2200553 i 4500 
003    OCoLC 
005    20240129213017.0 
006    m     o  d         
007    cr cnu---unuuu 
008    211220s2022    enk     o     000 0 eng d 
020    9781492085225|q(electronic bk.) 
020    1492085227|q(electronic bk.) 
020    9781492085201|q(electronic bk.) 
020    1492085200|q(electronic bk.) 
029 1  AU@|b000071520760 
035    (OCoLC)1289513846 
037    9781492085249|bO'Reilly Media 
037    AD6B7CB1-E911-4362-9DC4-AA5F1BDE86E3|bOverDrive, Inc.
       |nhttp://www.overdrive.com 
040    N$T|beng|erda|epn|cN$T|dN$T|dUKAHL|dORMDA|dOCLCO|dOCLCQ
       |dOCLCO|dTEFOD|dOCLCQ|dOCLCO|dOCLCL 
049    INap 
082 04 658.155 
082 04 658.155|223 
099    eBook O'Reilly for Public Libraries 
100 1  Karasan, Abdullah,|eauthor. 
245 10 Machine learning for financial risk management with Python
       :|balgorithms for modeling risk /|cAbdullah Karasan.
       |h[O'Reilly electronic resource] 
264  1 Cambridge :|bO'Reilly,|c2022. 
300    1 online resource (1 volume) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
520    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. 
588 0  Print version record. 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Financial risk management. 
650  0 Machine learning. 
650  0 Python (Computer program language) 
650  6 Finances|xGestion du risque. 
650  6 Apprentissage automatique. 
650  6 Python (Langage de programmation) 
650  7 Financial risk management|2fast 
650  7 Machine learning|2fast 
650  7 Python (Computer program language)|2fast 
776 08 |iPrint version:|aKarasan, Abdullah.|tMachine learning for
       financial risk management with Python.|dCambridge : 
       O'Reilly, 2022|z9781492085256|w(OCoLC)1285701278 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781492085249/?ar
       |zAvailable at O'Reilly for Public Libraries 
938    Askews and Holts Library Services|bASKH|nAH39686216 
938    Askews and Holts Library Services|bASKH|nAH39547785 
938    EBSCOhost|bEBSC|n3115911 
994    92|bJFN