Name: Neural networks and the financial markets
ISBN: 9781447101512

Book information

Authors: Jimmy Shadbolt verified identity, John G. Taylor verified identity
First published: 2002
Editions known: 2
Publishers: Springer London
Languages: eng
ISBN-13: 9781447101512, 9781852335311
Main topics: Deep Learning for Finance
Related site topics: Machine Learning for Market Forecasting, NLP & LLMs in Finance

Overview

Neural networks and the financial markets is a book by Jimmy Shadbolt and John G. Taylor, first published in 2002. In this catalogue it is classified mainly under Deep Learning for 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 61% 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	44/100 confidence 61% Score 43.9/100. Strongest: quant_relevance 0.42, metadata_completeness 0.85; weakest: practical_applicability 0.10. Evidence: title:'financial', title:'markets'. Difficulty unknown (neutral technical_depth).
Mathematical difficulty	Unknown
Metadata quality	66%

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.

ChatGPT Informed Graph Neural Network for Stock Movement Prediction Topic similarity inferred similarity 0.60
Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction Topic similarity inferred similarity 0.53
Spiking Neural Network for Cross-Market Portfolio Optimization in Financial Markets: A Neuromorphic Computing Approach Topic similarity inferred similarity 0.53
Unified GARCH-Recurrent Neural Network in Financial Volatility Forecasting Topic similarity inferred similarity 0.53
GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets Topic similarity inferred similarity 0.52
Dynamic graph neural networks for enhanced volatility prediction in financial markets Topic similarity inferred similarity 0.51

Methods covered

Deep Learning inferred

Similar books

Neural Networks in Finance inferred similarity 0.76
Neural Networks in Finance and Investing inferred similarity 0.68
Neural networks for financial forecasting inferred similarity 0.65
Neural networks in financial engineering inferred similarity 0.62
Wavelet neural networks inferred similarity 0.60

Author profiles

Jimmy Shadbolt verified identity
John G. Taylor verified identity

External resources

Open Library
DOI
Search on Amazon (ISBN-13 9781852335311) (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.

Interactive relationship graph (requires JavaScript). The relationships are also listed as text below it once loaded.