Machine learning for factor investing: (Record no. 7510)

MARC details
000 -LEADER
fixed length control field 02347nam a22002297a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20241108130457.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241108b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780367639723
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 332.6420285
Item number COQ
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Coqueret, Guillaume
245 ## - TITLE STATEMENT
Title Machine learning for factor investing:
Remainder of title python version
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. CRC Press
Place of publication, distribution, etc. Boca Raton
Date of publication, distribution, etc. 2023
300 ## - PHYSICAL DESCRIPTION
Extent xvii, 339 p.
365 ## - TRADE PRICE
Price type code GBP
Price amount 68.99
490 ## - SERIES STATEMENT
Series statement Chapman and Hall/CRC Financial Mathematics Series
520 ## - SUMMARY, ETC.
Summary, etc. Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics.<br/><br/>The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees and causal models.<br/><br/>All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.<br/><br/>(https://www.routledge.com/Machine-Learning-for-Factor-Investing-Python-Version/Coqueret-Guida/p/book/9780367639723?srsltid=AfmBOooIOBp6ih8X6ME3blopu09hDWDV554uI0wqDB1anEalyaBRwCZ6)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Investments -- Data processing
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)
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Guida, Tony
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification
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 Checked out Date last seen Date checked out Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Finance & Accounting COR/IN/25/6570 25-10-2024 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 11/08/2024 CBS Publishers & Distributors Pvt. Ltd. 5183.91 2 2 332.6420285 COQ 006366 01/21/2025 12/22/2024 12/22/2024 1 7975.24 11/08/2024 Book

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