Machine learning in finance: from theory to practice (Record no. 2820)

MARC details
000 -LEADER
fixed length control field 02723nam a22002297a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220718160602.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220718b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030410704
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 330.0285631
Item number DIX
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Dixon, Matthew F.
245 ## - TITLE STATEMENT
Title Machine learning in finance: from theory to practice
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Springer
Place of publication, distribution, etc. Switzerland
Date of publication, distribution, etc. 2020
300 ## - PHYSICAL DESCRIPTION
Extent xxv, 548 p.
365 ## - TRADE PRICE
Price type code EURO
Price amount 79.99
520 ## - SUMMARY, ETC.
Summary, etc. About this book<br/>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.<br/><br/>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 Machine learning
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Finance--Data processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Finance--Mathematical models
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 Dewey Decimal Classification
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Total Renewals Full call number Accession Number Date last seen Date checked out Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Finance & Accounting TB842 30-06-2022 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 07/18/2022 Technical Bureau India Pvt. Ltd. 4523.03 2 1 330.0285631 DIX 002837 12/28/2022 12/02/2022 1 6819.14 07/18/2022 Book

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