Name: Neural networks in financial engineering
ISBN: 9789810224806

Book information

Authors: International Conference on Neural Networks in the Capital Markets (3rd 1995 London, England) verified identity
First published: 1996
Editions known: 3
Publishers: World Scientific
Languages: eng
ISBN-13: 9789810224806, 9789810228194
Main topics: Deep Learning for Finance, Quantitative Finance
Related site topics: Machine Learning for Market Forecasting, NLP & LLMs in Finance

Overview

Neural networks in financial engineering is a book by International Conference on Neural Networks in the Capital Markets (3rd 1995 London, England), first published in 1996. In this catalogue it is classified mainly under Deep Learning for Finance and Quantitative Finance.

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 65% 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	57/100 confidence 65% Score 56.7/100. Strongest: quant_relevance 0.84, metadata_completeness 0.85; weakest: practical_applicability 0.10. Evidence: title:'financial engineering'. Difficulty unknown (neutral technical_depth).
Mathematical difficulty	Unknown
Metadata quality	67%

Difficulty evidence (2 signals)

machine_learning matched in subjects: “neural networks”
machine_learning matched in title: “neural networks”

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.

MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction Topic similarity inferred similarity 0.49
BERT vs GPT for financial engineering Topic similarity inferred similarity 0.48
ChatGPT Informed Graph Neural Network for Stock Movement Prediction Topic similarity inferred similarity 0.48
Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction Topic similarity inferred similarity 0.46
Unified GARCH-Recurrent Neural Network in Financial Volatility Forecasting Topic similarity inferred similarity 0.45
Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges Topic similarity inferred similarity 0.38

External resources

Open Library
Search on Amazon (ISBN-13 9789810228194) (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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