Name: Machine Learning for Asset Management and Pricing
ISBN: 9781611977899

Book information

Authors: Henry Schellhorn verified identity, Tianmin Kong verified identity
First published: 2024
Editions known: 2
Publishers: Society for Industrial and Applied Mathematics
Languages: eng
ISBN-13: 9781611977899, 9781611977905
Main topics: Financial Machine Learning
Related site topics: Machine Learning for Market Forecasting

Overview

Machine Learning for Asset Management and Pricing is a book by Henry Schellhorn and Tianmin Kong, first published in 2024. In this catalogue it is classified mainly under Financial Machine Learning.

Audience and depth. The available metadata did not allow a mathematical difficulty estimate, so none is shown; treat the technical level as unknown.

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 74% and difficulty confidence is 0%; 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	59/100 confidence 74% Score 59.3/100. Strongest: quant_relevance 0.89, metadata_completeness 0.85; weakest: practical_applicability 0.00. Evidence: title:'machine learning for asset'. Difficulty unknown (neutral technical_depth).
Mathematical difficulty	Unknown
Metadata quality	69%

Difficulty evidence (1 signal)

machine_learning matched in title: “machine learning”

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.

Improving S&P 500 Volatility Forecasting through Regime-Switching Methods Topic similarity inferred similarity 0.58
Predicting risk/reward ratio in financial markets for asset management using machine learning Topic similarity inferred similarity 0.57
Dynamic Investment Strategies Through Market Classification and Volatility: A Machine Learning Approach Topic similarity inferred similarity 0.57
Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets Topic similarity inferred similarity 0.56
Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins Topic similarity inferred similarity 0.56
Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies Topic similarity inferred similarity 0.56

Methods covered

Machine Learning (Non-Deep) inferred

Similar books

Machine Learning for Asset Managers inferred similarity 0.73
Machine Learning for Asset Management inferred similarity 0.68
Machine Learning in Finance inferred similarity 0.68
Machine Learning in Asset Pricing inferred similarity 0.65
Machine Learning in Finance inferred similarity 0.65

Author profiles

Henry Schellhorn verified identity
Tianmin Kong verified identity

External resources

Open Library
DOI
Search on Amazon (ISBN-13 9781611977905) (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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