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Neural Networks For Economic And Financial Modelling
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Book Synopsis Neural Networks for Economic and Financial Modelling by : Andrea Beltratti
Download or read book Neural Networks for Economic and Financial Modelling written by Andrea Beltratti and published by . This book was released on 1996 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of economics and finance is one of the few areas where the need for neural network applications is increasing. This book investigates the use of neural networks in developing real-world applications to help economists and financial strategists predict the movement of the markets.
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 Artificial Higher Order Neural Networks for Economics and Business by : Zhang, Ming
Download or read book Artificial Higher Order Neural Networks for Economics and Business written by Zhang, Ming and published by IGI Global. This book was released on 2008-07-31 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is the first book to provide opportunities for millions working in economics, accounting, finance and other business areas education on HONNs, the ease of their usage, and directions on how to obtain more accurate application results. It provides significant, informative advancements in the subject and introduces the HONN group models and adaptive HONNs"--Provided by publisher.
Book Synopsis Wavelet Neural Networks by : Antonios K. Alexandridis
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.
Book Synopsis Biologically Inspired Algorithms for Financial Modelling by : Anthony Brabazon
Download or read book Biologically Inspired Algorithms for Financial Modelling written by Anthony Brabazon and published by Springer Science & Business Media. This book was released on 2006-03-28 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling. In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies – neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures. The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.
Book Synopsis Nonlinear Time Series Analysis of Economic and Financial Data by : Philip Rothman
Download or read book Nonlinear Time Series Analysis of Economic and Financial Data written by Philip Rothman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Time Series Analysis of Economic and Financial Data provides an examination of the flourishing interest that has developed in this area over the past decade. The constant theme throughout this work is that standard linear time series tools leave unexamined and unexploited economically significant features in frequently used data sets. The book comprises original contributions written by specialists in the field, and offers a combination of both applied and methodological papers. It will be useful to both seasoned veterans of nonlinear time series analysis and those searching for an informative panoramic look at front-line developments in the area.
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 Financial Modelling by : Maria Bonilla
Download or read book Financial Modelling written by Maria Bonilla and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a selection of the papers presented at the 24th Meeting of the Euro Working Group on Financial Modelling held in Valencia, Spain, on April 8-10, 1.999. The Meeting took place in the Bancaja Cultural Center, a nice palace of the XIX century, located in the center of the city. Traditionally, members of the Euro Working Group on Financial Mod elling meet twice a year, hosted by different active groups in successions. The year 1999 was very special for us because the University of Valencia celebrates its fifth century. The Meeting was very well attended and of high quality. More than 90 participants, coming from 20 different countries debated 46 communications in regular sessions. The opening lecture was given by Prof. H. White, from the University of California, San Diego. The topics discussed were classified in nine sessions: Financial Theory, Financial Time Series, Risk Analysis, Portfolio Analysis, Financial Institu tions, Microstructures Market and Corporate Finance, Methods in Finance, Models in Finance and Derivatives. The papers collected in this volume provide a representative but not com plete sample of the fields where the members of the working group develop their scientific activity. The papers are a sample of this activity, and consist of theoretical papers as well as empirical ones.
Book Synopsis Computational Techniques for Modelling Learning in Economics by : Thomas Brenner
Download or read book Computational Techniques for Modelling Learning in Economics written by Thomas Brenner and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Techniques for Modelling Learning in Economics offers a critical overview of the computational techniques that are frequently used for modelling learning in economics. It is a collection of papers, each of which focuses on a different way of modelling learning, including the techniques of evolutionary algorithms, genetic programming, neural networks, classifier systems, local interaction models, least squares learning, Bayesian learning, boundedly rational models and cognitive learning models. Each paper describes the technique it uses, gives an example of its applications, and discusses the advantages and disadvantages of the technique. Hence, the book offers some guidance in the field of modelling learning in computation economics. In addition, the material contains state-of-the-art applications of the learning models in economic contexts such as the learning of preference, the study of bidding behaviour, the development of expectations, the analysis of economic growth, the learning in the repeated prisoner's dilemma, and the changes of cognitive models during economic transition. The work even includes innovative ways of modelling learning that are not common in the literature, for example the study of the decomposition of task or the modelling of cognitive learning.
Book Synopsis Machine Learning in Finance by : Matthew F. Dixon
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.
Book Synopsis Principles of Neural Model Identification, Selection and Adequacy by : Achilleas Zapranis
Download or read book Principles of Neural Model Identification, Selection and Adequacy written by Achilleas Zapranis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling. However a major weakness is the lack of established procedures for testing mis-specified models and the statistical significance of the various parameters which have been estimated. This is particularly important in the majority of financial applications where the data generating processes are dominantly stochastic and only partially deterministic. Based on the latest, most significant developments in estimation theory, model selection and the theory of mis-specified models, this volume develops neural networks into an advanced financial econometrics tool for non-parametric modelling. It provides the theoretical framework required, and displays the efficient use of neural networks for modelling complex financial phenomena. Unlike most other books in this area, this one treats neural networks as statistical devices for non-linear, non-parametric regression analysis.
Book Synopsis Financial Modeling Using Neural Networks by : Costas Vlassis
Download or read book Financial Modeling Using Neural Networks written by Costas Vlassis and published by . This book was released on 1994 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Econometrics of Financial Markets by : John Y. Campbell
Download or read book The Econometrics of Financial Markets written by John Y. Campbell and published by Princeton University Press. This book was released on 2012-06-28 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is intended for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including: the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory. Each chapter develops statistical techniques within the context of a particular financial application. This exciting new text contains a unique and accessible combination of theory and practice, bringing state-of-the-art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have read into their own applications.
Book Synopsis Novel Financial Applications of Machine Learning and Deep Learning by : Mohammad Zoynul Abedin
Download or read book Novel Financial Applications of Machine Learning and Deep Learning written by Mohammad Zoynul Abedin and published by Springer Nature. This book was released on 2023-03-01 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Book Synopsis Empirical Asset Pricing by : Wayne Ferson
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
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 Network Models in Economics and Finance by : Valery A. Kalyagin
Download or read book Network Models in Economics and Finance written by Valery A. Kalyagin and published by Springer. This book was released on 2014-10-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.
