Neural Networks In Finance And Investing
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Neural Networks In Finance And Investing
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Author : Robert R. Trippi
language : en
Publisher: Irwin Professional Publishing
Release Date : 1996
Neural Networks In Finance And Investing written by Robert R. Trippi and has been published by Irwin Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Business & Economics categories.
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 And Investing
DOWNLOAD
Author : Robert R. Trippi
language : en
Publisher: Irwin Professional Publishing
Release Date : 1996
Neural Networks In Finance And Investing written by Robert R. Trippi and has been published by Irwin Professional Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Business & Economics categories.
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 The Capital Markets
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Author : Apostolos-Paul Refenes
language : en
Publisher: Wiley
Release Date : 1995-03-28
Neural Networks In The Capital Markets written by Apostolos-Paul Refenes and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-03-28 with Business & Economics categories.
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.
Intelligent Computing
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Author : Kohei Arai
language : en
Publisher: Springer
Release Date : 2019-07-08
Intelligent Computing written by Kohei Arai and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-08 with Computers categories.
This book presents the proceedings of the Computing Conference 2019, providing a comprehensive collection of chapters focusing on core areas of computing and their real-world applications. Computing is an extremely broad discipline, encompassing a range of specialized fields, each focusing on particular areas of technology and types of application, and the conference offered pioneering researchers, scientists, industrial engineers, and students from around the globe a platform to share new ideas and development experiences. Providing state-of-the-art intelligent methods and techniques for solving real- world problems, the book inspires further research and technological advances in this important area.
Neural Networks In Finance
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Author : Paul D. McNelis
language : en
Publisher: Academic Press
Release Date : 2005-01-05
Neural Networks In Finance written by Paul D. McNelis and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-01-05 with Business & Economics categories.
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
Artificial Intelligence In Finance Investing
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Author : Robert R. Trippi
language : en
Publisher: McGraw Hill Professional
Release Date : 1996
Artificial Intelligence In Finance Investing written by Robert R. Trippi and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Business & Economics categories.
In Artificial Intelligence in Finance and Investing, authors Robert Trippi and Jae Lee explain this fascinating new technology in terms that portfolio managers, institutional investors, investment analysis, and information systems professionals can understand. Using real-life examples and a practical approach, this rare and readable volume discusses the entire field of artificial intelligence of relevance to investing, so that readers can realize the benefits and evaluate the features of existing or proposed systems, and ultimately construct their own systems. Topics include using Expert Systems for Asset Allocation, Timing Decisions, Pattern Recognition, and Risk Assessment; overview of Popular Knowledge-Based Systems; construction of Synergistic Rule Bases for Securities Selection; incorporating the Markowitz Portfolio Optimization Model into Knowledge-Based Systems; Bayesian Theory and Fuzzy Logic System Components; Machine Learning in Portfolio Selection and Investment Timing, including Pattern-Based Learning and Fenetic Algorithms; and Neural Network-Based Systems. To illustrate the concepts presented in the book, the authors conclude with a valuable practice session and analysis of a typical knowledge-based system for investment management, K-FOLIO. For those who want to stay on the cutting edge of the "application" revolution, Artificial Intelligence in Finance and Investing offers a pragmatic introduction to the use of knowledge-based systems in securities selection and portfolio management.
Machine Learning In Finance
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Author : Matthew F. Dixon
language : en
Publisher: Springer Nature
Release Date : 2020-07-01
Machine Learning In Finance written by Matthew F. Dixon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Business & Economics categories.
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.
Empirical Asset Pricing
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Author : Wayne Ferson
language : en
Publisher: MIT Press
Release Date : 2019-03-12
Empirical Asset Pricing written by Wayne Ferson and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-12 with Business & Economics categories.
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.
Network Models In Finance
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Author : Frank J. Fabozzi
language : en
Publisher: John Wiley & Sons
Release Date : 2025-02-05
Network Models In Finance written by Frank J. Fabozzi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-05 with Computers categories.
Expansive overview of theory and practical implementation of networks in investment management Guided by graph theory, Network Models in Finance: Expanding the Tools for Portfolio and Risk Management provides a comprehensive overview of networks in investment management, delivering strong knowledge of various types of networks, important characteristics, estimation, and their implementation in portfolio and risk management. With insights into the complexities of financial markets with respect to how individual entities interact within the financial system, this book enables readers to construct diversified portfolios by understanding the link between price/return movements of different asset classes and factors, perform better risk management through understanding systematic, systemic risk and counterparty risk, and monitor changes in the financial system that indicate a potential financial crisis. With a practitioner-oriented approach, this book includes coverage of: Practical examples of broad financial data to show the vast possibilities to visualize, describe, and investigate markets in a completely new way Interactions, Causal relationships and optimization within a network-based framework and direct applications of networks compared to traditional methods in finance Various types of algorithms enhanced by programming language codes that readers can implement and use for their own data Network Models in Finance: Expanding the Tools for Portfolio and Risk Management is an essential read for asset managers and investors seeking to make use of networks in research, trading, and portfolio management.
Applied Quantitative Methods For Trading And Investment
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Author : Christian L. Dunis
language : en
Publisher: John Wiley & Sons
Release Date : 2004-01-09
Applied Quantitative Methods For Trading And Investment written by Christian L. Dunis and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-01-09 with Business & Economics categories.
This book provides a manual on quantitative financial analysis. Focusing on advanced methods for modelling financial markets in the context of practical financial applications, it will cover data, software and techniques that will enable the reader to implement and interpret quantitative methodologies, specifically for trading and investment. Includes contributions from an international team of academics and quantitative asset managers from Morgan Stanley, Barclays Global Investors, ABN AMRO and Credit Suisse First Boston. Fills the gap for a book on applied quantitative investment & trading models Provides details of how to combine various models to manage and trade a portfolio