Neural Network Solutions For Trading In Financial Markets

Author : Dirk Emma Baestaens
Publisher : Pitman Publishing
ISBN 13 :
Total Pages : 274 pages
Book Rating : 4.16/5 ( download)

DOWNLOAD NOW!

Download or read book Neural Network Solutions for Trading in Financial Markets written by Dirk Emma Baestaens and published by Pitman Publishing. This book was released on 1994 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Matthew F. Dixon
Publisher : Springer Nature
ISBN 13 : 3030410684
Total Pages : 565 pages
Book Rating : 4.81/5 ( download)

DOWNLOAD NOW!

Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Author : Jimmy Shadbolt
Publisher : Springer Science & Business Media
ISBN 13 : 1447101510
Total Pages : 266 pages
Book Rating : 4.12/5 ( download)

DOWNLOAD NOW!

Download or read book Neural Networks and the Financial Markets written by Jimmy Shadbolt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Christian L. Dunis
Publisher : Springer
ISBN 13 : 1137488808
Total Pages : 349 pages
Book Rating : 4.00/5 ( download)

DOWNLOAD NOW!

Download or read book Artificial Intelligence in Financial Markets written by Christian L. Dunis and published by Springer. This book was released on 2016-11-21 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Paul D. McNelis
Publisher : Elsevier
ISBN 13 : 0080479650
Total Pages : 261 pages
Book Rating : 4.51/5 ( download)

DOWNLOAD NOW!

Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Elsevier. This book was released on 2005-01-20 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : Mark T. Leung
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.39/5 ( download)

DOWNLOAD NOW!

Download or read book Application of Neural Networks to an Emerging Financial Market written by Mark T. Leung and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there exists some studies which deal with the issues of forecasting stock market index and development of trading strategies, most of the empirical findings are associated with the developed financial markets (e.g., U.S., U.K., and Japan). Currently, many international investment bankers and brokerage firms have major stakes in overseas markets. Given the economic success of Taiwan in the last two decades, the financial markets in this Asian country have attracted considerable global investments. Our study models and predicts the TSE Index using neural networks. Their performance is compared with that of parametric forecasting approaches, namely the Generalized Methods of Moments (GMM) and random walk. These rapidly growing financial markets are usually characterized by high volatility, relatively smaller capitalization, and less price efficiency, features which may hinder the effectiveness of those forecasting models developed for established markets. The good performance of the PNN suggests that the neural network models are useful in predicting the direction of index returns. Furthermore, PNN has demonstrated a stronger predictive power than both the GMM-Kalman filter and the random walk forecasting models. This superiority is partially attributed to PNN's ability to identify outliers and erroneous data. Compared to the other two parametric techniques examined in this study, PNN does not require any assumption of the underlying probability density functions of the class populations. The trading experiment shows that the PNN-guided trading strategies obtain higher profits than the other investment strategies utilizing the market direction generated by the parametric forecasting methods. In addition, the PNN-guided trading with multiple triggering thresholds is generally better than the one with single triggering thresholds. The multiple threshold version is able to consider the degree of certainty of a particular PNN classification and thereby reduce potential loss in the market.

Author : Robert Arnold Klein
Publisher : Irwin Professional Publishing
ISBN 13 :
Total Pages : 392 pages
Book Rating : 4.25/5 ( download)

DOWNLOAD NOW!

Download or read book Virtual Trading written by Robert Arnold Klein and published by Irwin Professional Publishing. This book was released on 1995 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: In plain language, Virtual Trading, shows you how to proceed from data collection to system development to actual trading. For traders who want to stay on the cutting edge of market technology, Virtual Trading is a must read. Featuring contributions from the leading experts in the field, Virtual Trading provides in-depth information on every important aspect of artificial intelligence in trading. Highlights include: Synergistic market analysis using neural networks by Lou Mendelsohn; Developing a market-timing system using genetic algorithms by Casimir Klimasauskas; Neural networkds and stock market valuation by John Keal; Applying chaos theory to a neural network by Joseph Shepard; Developing a trading system that uses Al by Mark Jurik; Neural network techniques for time series analysis by Peter Davies.

Author : Guido J. Deboeck
Publisher : John Wiley & Sons
ISBN 13 : 9780471311003
Total Pages : 426 pages
Book Rating : 4.06/5 ( download)

DOWNLOAD NOW!

Download or read book Trading on the Edge written by Guido J. Deboeck and published by John Wiley & Sons. This book was released on 1994-04-18 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experts from the world's major financial institutions contributed to this work and have already used the newest technologies. Gives proven strategies for using neural networks, algorithms, fuzzy logic and nonlinear data analysis techniques to enhance profitability. The latest analytical breakthroughs, the impact on modern finance theory and practice, including the best ways for profitably applying them to any trading and portfolio management system, are all covered.

Author : Christian Dunis
Publisher : Routledge
ISBN 13 : 1136195106
Total Pages : 236 pages
Book Rating : 4.05/5 ( download)

DOWNLOAD NOW!

Download or read book Computational Intelligence Techniques for Trading and Investment written by Christian Dunis and published by Routledge. This book was released on 2014-03-26 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational intelligence, a sub-branch of artificial intelligence, is a field which draws on the natural world and adaptive mechanisms in order to study behaviour in changing complex environments. This book provides an interdisciplinary view of current technological advances and challenges concerning the application of computational intelligence techniques to financial time-series forecasting, trading and investment. The book is divided into five parts. The first part introduces the most important computational intelligence and financial trading concepts, while also presenting the most important methodologies from these different domains. The second part is devoted to the application of traditional computational intelligence techniques to the fields of financial forecasting and trading, and the third part explores the applications of artificial neural networks in these domains. The fourth part delves into novel evolutionary-based hybrid methodologies for trading and portfolio management, while the fifth part presents the applications of advanced computational intelligence modelling techniques in financial forecasting and trading. This volume will be useful for graduate and postgraduate students of finance, computational finance, financial engineering and computer science. Practitioners, traders and financial analysts will also benefit from this book.

Author : Robert R. Trippi
Publisher : Irwin Professional Publishing
ISBN 13 : 9781557384522
Total Pages : 513 pages
Book Rating : 4.25/5 ( download)

DOWNLOAD NOW!

Download or read book Neural Networks in Finance and Investing written by Robert R. Trippi and published by Irwin Professional Publishing. This book was released on 1993 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many believe that neural networks will eventually out-perform even the best traders and investors, yet this extraordinary technology remained largely inaccessible to practitioners--prior to this landmark text. Nowhere else will you find such a thorough and relevant examination of the applications and potential of this cutting-edge technology. This book not only contains many examples of neural networks for prediction and risk assessment, but provides promising systems for forecasting and explaining price movements of stocks and securities. Sections include neural network overview; analysis of financial condition; business failure prediction; debt risk assessment; security market applications; and neural network approaches to financial forecasting.