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Neural Networks in Finance

Neural Networks in Finance
Author: Paul D. McNelis
Publisher: Academic Press
Total Pages: 262
Release: 2005-01-05
Genre: Business & Economics
ISBN: 0124859674

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This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website


Neural Networks in the Capital Markets

Neural Networks in the Capital Markets
Author: Apostolos-Paul Refenes
Publisher: Wiley
Total Pages: 392
Release: 1995-03-28
Genre: Business & Economics
ISBN: 9780471943648

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Based on original papers which represent new and significant research, developments and applications in finance and investment. The author takes a pragmatic view of neural networks, treating them as computationally equivalent to well-understood, non-parametric inference methods in decision science. The author also makes comparisons with established techniques where appropriate.


Neural Network Solutions for Trading in Financial Markets

Neural Network Solutions for Trading in Financial Markets
Author: Dirk Emma Baestaens
Publisher: Pitman Publishing
Total Pages: 274
Release: 1994
Genre: Business & Economics
ISBN:

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Offers an alternative technique in forecasting to the traditional techniques used in trading and dealing. The book explains the shortcomings of traditional techniques and shows how neural networks overcome many of the disadvantages of these traditional systems.


Neural Networks and the Financial Markets

Neural Networks and the Financial Markets
Author: Jimmy Shadbolt
Publisher: Springer Science & Business Media
Total Pages: 266
Release: 2012-12-06
Genre: Computers
ISBN: 1447101510

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This volume looks at financial prediction from a broad range of perspectives. It covers: - the economic arguments - the practicalities of the markets - how predictions are used - how predictions are made - how predictions are turned into something usable (asset locations) It combines a discussion of standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets. Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.


Neural Networks in Finance and Investing

Neural Networks in Finance and Investing
Author: Robert R. Trippi
Publisher: Irwin Professional Publishing
Total Pages: 872
Release: 1996
Genre: Artificial intelligence
ISBN:

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This completely updated version of the classic first edition offers a wealth of new material reflecting the latest developments in teh field. For investment professionals seeking to maximize this exciting new technology, this handbook is the definitive information source.


Neural Networks in Finance

Neural Networks in Finance
Author: Paul D. McNelis
Publisher: Elsevier
Total Pages: 261
Release: 2005-01-20
Genre: Computers
ISBN: 0080479650

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This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction. McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong. * Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance * Includes numerous examples and applications * Numerical illustrations use MATLAB code and the book is accompanied by a website


Neural Network Time Series

Neural Network Time Series
Author: E. Michael Azoff
Publisher:
Total Pages: 224
Release: 1994-09-27
Genre: Business & Economics
ISBN:

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Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software.


Artificial Neural Networks in Finance and Manufacturing

Artificial Neural Networks in Finance and Manufacturing
Author: Kamruzzaman, Joarder
Publisher: IGI Global
Total Pages: 299
Release: 2006-03-31
Genre: Computers
ISBN: 1591406722

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"This book presents a variety of practical applications of neural networks in two important domains of economic activity: finance and manufacturing"--Provided by publisher.


Artificial Intelligence in Financial Markets

Artificial Intelligence in Financial Markets
Author: Christian L. Dunis
Publisher: Springer
Total Pages: 349
Release: 2016-11-21
Genre: Business & Economics
ISBN: 1137488808

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As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field.


Neural Networks in Financial Engineering

Neural Networks in Financial Engineering
Author: Apostolos-Paul Refenes
Publisher: World Scientific Publishing Company Incorporated
Total Pages: 634
Release: 1996
Genre: Business & Economics
ISBN: 9789810228194

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Neural networks can be used for improving investment performance in the financial markets. The papers in this volume aim to give investment managers, institutional investors and analysts a comprehensive look at the most profitable applications of this tech