Central Library, Indian Institute of Technology Delhi
केंद्रीय पुस्तकालय, भारतीय प्रौद्योगिकी संस्थान दिल्ली

Machine learning in finance (Record no. 243623)

MARC details
000 -LEADER
fixed length control field 02946aam a2200301 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 221123b2020 |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030410704
072 ## - SUBJECT CATEGORY CODE
Subject category code MS
080 ## - UNIVERSAL DECIMAL CLASSIFICATION NUMBER
Universal Decimal Classification number 681.3:336
Item number DIX-M
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Dixon, Matthew F.
245 ## - TITLE STATEMENT
Title Machine learning in finance
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Springer
Date of publication, distribution, etc 2020
Place of publication, distribution, etc Cham
300 ## - PHYSICAL DESCRIPTION
Extent xxv, 548p.
365 ## - TRADE PRICE
Price amount 6674.37 (Each copy)
Price type code INR
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes index (p. 543-548)<br/>
520 ## - SUMMARY, ETC.
Summary, etc This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Financial mathematics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Financial econometrics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Neural networks
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Bayesian neural networks
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Reinforcement learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Investment management
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Wealth Management
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Time series modeling
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Halperin, Igor
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Bilokon, Paul
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Checked out Date last seen Date last borrowed Cost, replacement price Koha item type
          General Indian Institute of Technology Delhi - Central Library Indian Institute of Technology Delhi - Central Library Central Library 22/11/2022 1821 5139.26 3 681.3:336 DIX-M 177388 22/12/2023 08/12/2023 08/12/2023 6856.74 Book
          General Management Studies Management Studies Management Studies 22/11/2022 1821 5139.26   681.3:336 DIX-M 177389   22/11/2022   6856.74 Book
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