Neural Networks In Finance And Investing PDF Download
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Author | : Robert R. Trippi |
Publisher | : Irwin Professional Publishing |
Total Pages | : 872 |
Release | : 1996 |
Genre | : Artificial intelligence |
ISBN | : |
Download Neural Networks in Finance and Investing Book in PDF, ePub and Kindle
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.
Author | : Apostolos-Paul Refenes |
Publisher | : Wiley |
Total Pages | : 392 |
Release | : 1995-03-28 |
Genre | : Business & Economics |
ISBN | : 9780471943648 |
Download Neural Networks in the Capital Markets Book in PDF, ePub and Kindle
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.
Author | : Robert R. Trippi |
Publisher | : McGraw Hill Professional |
Total Pages | : 280 |
Release | : 1996 |
Genre | : Business & Economics |
ISBN | : 9781557388681 |
Download Artificial Intelligence in Finance & Investing Book in PDF, ePub and Kindle
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.
Author | : Apostolos-Paul Refenes |
Publisher | : World Scientific Publishing Company Incorporated |
Total Pages | : 634 |
Release | : 1996 |
Genre | : Business & Economics |
ISBN | : 9789810228194 |
Download Neural Networks in Financial Engineering Book in PDF, ePub and Kindle
Neural networks can be used for improving investment performance in the financial markets. The papers in this volume aim to give investment managers, institutional investors and analysts a comprehensive look at the most profitable applications of this tech
Author | : Matthew F. Dixon |
Publisher | : Springer Nature |
Total Pages | : 565 |
Release | : 2020-07-01 |
Genre | : Business & Economics |
ISBN | : 3030410684 |
Download Machine Learning in Finance Book in PDF, ePub and Kindle
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 | : Söhnke M. Bartram |
Publisher | : CFA Institute Research Foundation |
Total Pages | : 95 |
Release | : 2020-08-28 |
Genre | : Business & Economics |
ISBN | : 195292703X |
Download Artificial Intelligence in Asset Management Book in PDF, ePub and Kindle
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Author | : Paul D. McNelis |
Publisher | : Academic Press |
Total Pages | : 262 |
Release | : 2005-01-05 |
Genre | : Business & Economics |
ISBN | : 0124859674 |
Download Neural Networks in Finance Book in PDF, ePub and Kindle
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 | : Paul D. McNelis |
Publisher | : Elsevier |
Total Pages | : 261 |
Release | : 2005-01-20 |
Genre | : Computers |
ISBN | : 0080479650 |
Download Neural Networks in Finance Book in PDF, ePub and Kindle
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 | : Marcos Lopez de Prado |
Publisher | : John Wiley & Sons |
Total Pages | : 400 |
Release | : 2018-01-23 |
Genre | : Business & Economics |
ISBN | : 1119482119 |
Download Advances in Financial Machine Learning Book in PDF, ePub and Kindle
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Author | : Wayne Ferson |
Publisher | : MIT Press |
Total Pages | : 497 |
Release | : 2019-03-12 |
Genre | : Business & Economics |
ISBN | : 0262039370 |
Download Empirical Asset Pricing Book in PDF, ePub and Kindle
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.