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Application Of Neural Networks To An Emerging Financial Market
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Book Synopsis Application of Neural Networks to an Emerging Financial Market by : Mark T. Leung
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.
Book Synopsis Application of Neural Networks to an Emerging Financial Market by : An-Sing Chen
Download or read book Application of Neural Networks to an Emerging Financial Market written by An-Sing Chen and published by . This book was released on 2004 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decade, neural networks have drawn noticeable attention from many computer and operations researchers. While some previous studies have found encouraging results with using this artificial intelligence technique to predict the movements of established financial markets, it is interesting to verify the persistence of this performance in the emerging markets. These rapid 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. In this study, we attempt to model and predict the direction of return on the Taiwan Stock Exchange Index, one of the fastest growing financial exchanges in developing Asian countries. Our approach is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The Probabilistic Neural Network (PNN) is used to forecast the direction of index return after it is trained by historical data. The forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy and hold strategy, and the investment strategies guided by the forecasts estimated by the random walk model and the parametric Generalized Methods of Moments (GMM) with Kalman filter. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. The influences of the length of investment horizon and the commission rate are also considered.
Book Synopsis Neural Networks in Finance by : Paul D. McNelis
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
Book Synopsis Neural Networks in the Capital Markets by : Apostolos-Paul Refenes
Download or read book Neural Networks in the Capital Markets written by Apostolos-Paul Refenes and published by Wiley. This book was released on 1995-03-28 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Networks in Finance by : Paul D. McNelis
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
Book Synopsis Neural Networks in Finance and Investing by : Robert R. Trippi
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.
Book Synopsis Neural Networks and the Financial Markets by : Jimmy Shadbolt
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.
Book Synopsis Neural Network Time Series by : E. Michael Azoff
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.
Book Synopsis Neural Networks in Finance and Investing by : Robert R. Trippi
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.
Book Synopsis Neural Network Solutions for Trading in Financial Markets by : Dirk Emma Baestaens
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.
Book Synopsis Artificial Neural Networks in Finance and Manufacturing by : Kamruzzaman, Joarder
Download or read book Artificial Neural Networks in Finance and Manufacturing written by Kamruzzaman, Joarder and published by IGI Global. This book was released on 2006-03-31 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents a variety of practical applications of neural networks in two important domains of economic activity: finance and manufacturing"--Provided by publisher.
Book Synopsis Artificial Neural Networks by : Ali Roghani
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."
Book Synopsis Artificial Intelligence in Financial Markets by : Christian L. Dunis
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.
Book Synopsis Artificial Neural Networks by : Ali Roghani
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."
Book Synopsis Fractal Approaches for Modeling Financial Assets and Predicting Crises by : Nekrasova, Inna
Download or read book Fractal Approaches for Modeling Financial Assets and Predicting Crises written by Nekrasova, Inna and published by IGI Global. This book was released on 2018-02-09 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an ever-changing economy, market specialists strive to find new ways to evaluate the risks and potential reward of economic ventures. They start by assessing the importance of human reaction during the economic planning process and put together systems to measure financial markets and their longevity. Fractal Approaches for Modeling Financial Assets and Predicting Crises is a critical scholarly resource that examines the fractal structure and long-term memory of the financial markets in order to predict prices of financial assets and financial crises. Featuring coverage on a broad range of topics, such as computational process models, chaos theory, and game theory, this book is geared towards academicians, researchers, and students seeking current research on pricing and predicting financial crises.
Book Synopsis Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network by : Joish Bosco
Download or read book Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network written by Joish Bosco and published by GRIN Verlag. This book was released on 2018-09-18 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.
Book Synopsis Applications and Innovations in Intelligent Systems XIII by : Ann Macintosh
Download or read book Applications and Innovations in Intelligent Systems XIII written by Ann Macintosh and published by Springer Science & Business Media. This book was released on 2007-10-27 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: The papers in this volume are the refereed application papers presented at AI-2005, the Twenty-fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2005. The papers present new and innovative developments in the field, divided into sections on Synthesis and Prediction, Scheduling and Search, Diagnosis and Monitoring, Classification and Design, and Analysis and Evaluation. This is the thirteenth volume in the Applications and Innovations series. The series serves as a key reference on the use of AI Technology to enable organisations to solve complex problems and gain significant business benefits. The Technical Stream papers are published as a companion volume under the title Research and Development in Intelligent Systems XXII.
