Download or read book Neural Networks in Finance written by Paul D. McNelis and published by Academic Press. This book was released on 2005-01-05 with total page 262 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

Download or read book Neural Network Time Series written by E. Michael Azoff and published by . This book was released on 1994-09-27 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Neural Networks in Business Forecasting written by G. Peter Zhang and published by IGI Global. This book was released on 2004-01-01 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is one of the most important activities that form the basis for strategic, tactical, and operational decisions in all business organizations. Recently, neural networks have emerged as an important tool for business forecasting. There are considerable interests and applications in forecasting using neural networks. Neural Networks in Business Forecasting provides for researchers and practitioners some recent advances in applying neural networks to business forecasting. A number of case studies demonstrating the innovative or successful applications of neural networks to many areas of business as well as methods to improve neural network forecasting performance are presented.

Download or read book Artificial Neural Networks written by Ali Roghani and published by CreateSpace. This book was released on 2015-04-17 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."

Download or read book Financial Prediction Using Neural Networks written by Joseph S. Zirilli and published by . This book was released on 1997 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on approaches to performing trend analysis through the use of neural nets, this book comparess the results of experiments on various types of markets, and includes a review of current work in the area. It appeals to students in both neural computing and finance as well as to financial analysts and academic and professional researchers in the field of neural network applications.

Download or read book Foreign Exchange Rate Forecasting with Artificial Neural Networks written by Lean Yu and published by Springer Science & Business Media. This book was released on 2010-02-26 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges.

Download or read book Intelligent Systems and Financial Forecasting written by Jason Kingdon and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fundamental objective of Artificial Intelligence (AI) is the creation of in telligent computer programs. In more modest terms AI is simply con cerned with expanding the repertoire of computer applications into new domains and to new levels of efficiency. The motivation for this effort comes from many sources. At a practical level there is always a demand for achieving things in more efficient ways. Equally, there is the technical challenge of building programs that allow a machine to do something a machine has never done before. Both of these desires are contained within AI and both provide the inspirational force behind its development. In terms of satisfying both of these desires there can be no better example than machine learning. Machines that can learn have an in-built effi ciency. The same software can be applied in many applications and in many circumstances. The machine can adapt its behaviour so as to meet the demands of new, or changing, environments without the need for costly re-programming. In addition, a machine that can learn can be ap plied in new domains with the genuine potential for innovation. In this sense a machine that can learn can be applied in areas where little is known about possible causal relationships, and even in circumstances where causal relationships are judged not to exist. This last aspect is of major significance when considering machine learning as applied to fi nancial forecasting.

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 1996 with total page 872 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Forecasting Financial Markets Using Neural Networks written by Jason E. Kutsurelis and published by . This book was released on 1998 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research examines andanalyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of usingneural networks as a forecasting tool for the individual investor. This study builds upon the work done byEdward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.

Download or read book Artificial Neural Networks written by Ali Roghani and published by Createspace Independent Publishing Platform. This book was released on 2016-08-09 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."

Download or read book Forecasting Financial Markets Using Neural Networks written by Jason Kutsurelis and published by . This book was released on 1998-09-01 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research examines and analyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a traditional forecasting method, multiple linear regression analysis. Finally, the probability of the model's forecast being correct is calculated using conditional probabilities. While only briefly discussing neural network theory, this research determines the feasibility and practicality of using neural networks as a forecasting tool for the individual investor. This study builds upon the work done by Edward Gately in his book Neural Networks for Financial Forecasting. This research validates the work of Gately and describes the development of a neural network that achieved a 93.3 percent probability of predicting a market rise, and an 88.07 percent probability of predicting a market drop in the S&P500. It was concluded that neural networks do have the capability to forecast financial markets and, if properly trained, the individual investor could benefit from the use of this forecasting tool.

Download or read book Neural Networks for Financial Forecasting written by Edward Gately and published by Wiley. This book was released on 1995-10-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Succinctly explains how neural networks function, what they can accomplish as well as how to use, construct and apply them for maximum profit. Selecting what is to be predicted and choosing proper inputs, deciding on the best network architecture, training, and algorithms are among the topics discussed. Highlights examples of successful networks. Numerous graphs and spreadsheets are used to illustrate concepts. The appendix features lists of neural network suppliers, useful publications and more.

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

Download or read book Wavelet Neural Networks written by Antonios K. Alexandridis and published by John Wiley & Sons. This book was released on 2014-04-24 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification. The authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification also includes: • Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence • Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction • An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks • Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals Ideal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics.

Download or read book Nature Inspired Computing Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2016-07-26 with total page 1810 pages. Available in PDF, EPUB and Kindle. Book excerpt: As technology continues to become more sophisticated, mimicking natural processes and phenomena also becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for man-made computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Computing: Concepts, Methodologies, Tools, and Applications takes an interdisciplinary approach to the topic of natural computing, including emerging technologies being developed for the purpose of simulating natural phenomena, applications across industries, and the future outlook of biologically and nature-inspired technologies. Emphasizing critical research in a comprehensive multi-volume set, this publication is designed for use by IT professionals, researchers, and graduate students studying intelligent computing.

Download or read book Neural Networks for Financial Forecasting written by Ashur Wilson Paulus Odah and published by . This book was released on 2006 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.