Name: Machine Learning for Asset Management: New Developments and Financial Applications
ISBN: 9781119751182

Book information

Authors: Emmanuel Jurczenko verified identity
First published: 2020
Editions known: 4
Publishers: Wiley
Languages: eng
ISBN-13: 9781119751182, 9781786305442
Main topics: Financial Machine Learning
Related site topics: Machine Learning for Market Forecasting

Overview

Machine Learning for Asset Management is a book by Emmanuel Jurczenko, first published in 2020. In this catalogue it is classified mainly under Financial Machine Learning.

Audience and depth. The rule-based model places its mathematical difficulty at intermediate level (6/10); readers are typically expected to know calculus, linear algebra, machine learning, optimization, probability theory and statistics.

Orientation. The theory-versus-practice balance could not be determined from metadata. The archive holds 6 papers whose topics and vocabulary overlap with this book; those links are inferred (see the research connections below).

How to read this page. Topics, difficulty, prerequisites and orientation are model-derived (rule-based, rule_based_v1) from bibliographic metadata such as title, subject headings and edition data. They were not produced by reading the book and are not a review. Relevance confidence is 87% and difficulty confidence is 66%; the catalogue lists no ratings or awards.

Research assessment

Rule-based and model-derived from metadata (title, subject headings, edition data). These are not reviews or ratings; see the evidence below.

Quant-finance relevance	63/100 confidence 87% Score 63.2/100. Strongest: quant_relevance 0.91, technical_depth 0.51; weakest: practical_applicability 0.00. Evidence: title:'machine learning for asset'.
Mathematical difficulty	Intermediate (6/10) confidence 66%
Prerequisites	calculus, linear algebra, machine learning, optimization, probability theory, statistics
Metadata quality	90%

Difficulty evidence (2 signals)

machine_learning matched in title: “machine learning”
optimization matched in openalex_topics: “optimization”

Research connections

Related papers on this site

Similarity of research area (shared vocabulary, topics and methods). Not citations: the paper does not necessarily cite this book.

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models Topic similarity inferred similarity 0.54
Dynamic Investment Strategies Through Market Classification and Volatility: A Machine Learning Approach Topic similarity inferred similarity 0.53
Some variation of COBRA in sequential learning setup Topic similarity inferred similarity 0.52
The Role of Deep Learning in Financial Asset Management: A Systematic Review Topic similarity inferred similarity 0.52
Using Machine Learning to Forecast Market Direction with Efficient Frontier Coefficients Topic similarity inferred similarity 0.52
Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms Topic similarity inferred similarity 0.52

External resources

Open Library
DOI
Search on Amazon (ISBN-13 9781786305442) (search link; may be an affiliate link)

Relationship graph

Solid lines are verified facts (authorship from persistent identifiers, editions); dashed lines are inferred by rules or similarity. Open in the full explorer.

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