Download or read book Decision Technologies for Computational Finance written by Apostolos-Paul N. Refenes and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains selected papers that were presented at the International Conference COMPUTATIONAL FINANCE 1997 held at London Business School on December 15-17 1997. Formerly known as Neural Networks in the Capital Markets (NNCM), this series of meetings has emerged as a truly multi-disciplinary international conference and provided an international focus for innovative research on the application of a multiplicity of advanced decision technologies to many areas of financial engineering. It has drawn upon theoretical advances in financial economics and robust methodological developments in the statistical, econometric and computer sciences. To reflect its multi-disciplinary nature, the NNCM conference has adopted the new title COMPUTATIONAL FINANCE. The papers in this volume are organised in six parts. Market Dynamics and Risk, Trading and Arbitrage strategies, Volatility and Options, Term-Structure and Factor models, Corporate Distress Models and Advances on Methodology. This years' acceptance rate (38%) reflects both the increasing interest in the conference and the Programme Committee's efforts to improve the quality of the meeting year-on-year. I would like to thank the members of the programme committee for their efforts in refereeing the papers. I also would like to thank the members of the computational finance group at London Business School and particularly Neil Burgess, Peter Bolland, Yves Bentz, and Nevil Towers for organising the meeting.
Download or read book Decision Technologies For Financial Engineering Proceedings Of The Fourth International Conference On Neural Networks In The Capital Markets Nncm 96 written by Yaser Abu-mostafa and published by World Scientific. This book was released on 1998-01-02 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume selects the best contributions from the Fourth International Conference on Neural Networks in the Capital Markets (NNCM). The conference brought together academics from several disciplines with strategists and decision makers from the financial industries.The various chapters present and compare new techniques from many areas including data mining, information systems, machine learning, and statistical artificial intelligence. The volume focuses on evaluating their usefulness for problems in computational finance and financial engineering.Applications — risk management; asset allocation; dynamic trading and hedging; forecasting; trading cost control. Markets — equity; foreign exchange; bond; commodity; derivatives; Approaches — data mining; statistical AI; machine learning; Monte Carlo simulation; bootstrapping; genetic algorithms; nonparametric methods; fuzzy logic.The chapters emphasizes in-depth and comparative evaluation with established approaches.
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.
Download or read book Encyclopedia of Information Science and Technology written by Mehdi Khosrow-Pour and published by IGI Global Snippet. This book was released on 2009 with total page 4292 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This set of books represents a detailed compendium of authoritative, research-based entries that define the contemporary state of knowledge on technology"--Provided by publisher.
Download or read book Modern Computational Finance written by Antoine Savine and published by John Wiley & Sons. This book was released on 2018-11-20 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware. AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance. Danske Bank's wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank's systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software. This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates. The book comes with professional source code in C++, including an efficient, up to date implementation of AAD and a generic parallel simulation library. Modern C++, high performance parallel programming and interfacing C++ with Excel are also covered. The book builds the code step-by-step, while the code illustrates the concepts and notions developed in the book.
Download or read book Computational Finance 1999 written by Yaser S. Abu-Mostafa and published by MIT Press. This book was released on 2000 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an exciting new cross-disciplinary research area, draws extensively on the tools and techniques of computer science, statistics, information systems, and financial economics. This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. These methods are applied to a wide range of problems in finance, including risk management, asset allocation, style analysis, dynamic trading and hedging, forecasting, and option pricing. The book is based on the sixth annual international conference Computational Finance 1999, held at New York University's Stern School of Business.
Download or read book Intelligent Decision Technologies written by Junzo Watada and published by Springer Science & Business Media. This book was released on 2011-11-19 with total page 903 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Decision Technologies (IDT) seeks an interchange of research on intelligent systems and intelligent technologies which enhance or improve decision making in industry, government and academia. The focus is interdisciplinary in nature, and includes research on all aspects of intelligent decision technologies, from fundamental development to the applied system. This volume represents leading research from the Third KES International Symposium on Intelligent Decision Technologies (KES IDT’11), hosted and organized by the University of Piraeus, Greece, in conjunction with KES International. The symposium was concerned with theory, design, development, implementation, testing and evaluation of intelligent decision systems. Topics include decision making theory, intelligent agents, fuzzy logic, multi-agent systems, Bayesian networks, optimization, artificial neural networks, genetic algorithms, expert systems, decision support systems, geographic information systems, case-based reasoning, time series, knowledge management systems, rough sets, spatial decision analysis, and multi-criteria decision analysis. These technologies have the potential to revolutionize decision making in many areas of management, healthcare, international business, finance, accounting, marketing, military applications, ecommerce, network management, crisis response, building design, information retrieval, and disaster recovery for a better future. The symposium was concerned with theory, design, development, implementation, testing and evaluation of intelligent decision systems. Topics include decision making theory, intelligent agents, fuzzy logic, multi-agent systems, Bayesian networks, optimization, artificial neural networks, genetic algorithms, expert systems, decision support systems, geographic information systems, case-based reasoning, time series, knowledge management systems, rough sets, spatial decision analysis, and multi-criteria decision analysis. These technologies have the potential to revolutionize decision making in many areas of management, healthcare, international business, finance, accounting, marketing, military applications, ecommerce, network management, crisis response, building design, information retrieval, and disaster recovery for a better future.
Download or read book Decision Technologies for Computational Finance written by Apostolos-Paul N. Refenes and published by . This book was released on 2014-09-01 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intelligent Decision Technologies 2017 written by Ireneusz Czarnowski and published by Springer. This book was released on 2017-05-24 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume presents a collection of peer-reviewed articles from the 9th KES International Conference on Intelligent Decision Technologies (KES-IDT-17), held in Vilamoura, Algarve, Portugal on 21–23 June 2017. The conference addressed critical areas of computer science, as well as promoting knowledge transfer and the generation of new ideas in the field of intelligent decision making, project management and data analysis. The range of topics addressed includes methods of classification, prediction, data analysis, decision support, modeling, social media and many more in such diverse areas as finance, linguistics, management and transportation.
Download or read book Encyclopedia of Information Science and Technology First Edition written by Khosrow-Pour, D.B.A., Mehdi and published by IGI Global. This book was released on 2005-01-31 with total page 3807 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive coverage of critical issues related to information science and technology.
Download or read book New Advances in Intelligent Decision Technologies written by Gloria Phillips-Wren and published by Springer Science & Business Media. This book was released on 2009-04-28 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: IDT (Intelligent Decision Technologies) seeks an interchange of research on intelligent systems and intelligent technologies which enhance or improve decision making in industry, government and academia. The focus is interdisciplinary in nature, and includes research on all aspects of intelligent decision technologies, from fundamental development to the applied system. It constitutes a great honor and pleasure for us to publish the works and new research results of scholars from the First KES International Symposium on Intelligent Decision Technologies (KES IDT’09), hosted and organized by University of Hyogo in conjunction with KES International (Himeji, Japan, April, 2009). The symposium was concerned with theory, design, development, implementation, testing and evaluation of intelligent decision systems. Its topics included intelligent agents, fuzzy logic, multi-agent systems, artificial neural networks, genetic algorithms, expert systems, intelligent decision making support systems, information retrieval systems, geographic information systems, and knowledge management systems. These technologies have the potential to support decision making in many areas of management, international business, finance, accounting, marketing, healthcare, military applications, production, networks, traffic management, crisis response, and human interfaces.
Download or read book Genetic Algorithms and Genetic Programming in Computational Finance written by Shu-Heng Chen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: After a decade of development, genetic algorithms and genetic programming have become a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering volume devoted entirely to a systematic and comprehensive review of this subject. Chapters cover various areas of computational finance, including financial forecasting, trading strategies development, cash flow management, option pricing, portfolio management, volatility modeling, arbitraging, and agent-based simulations of artificial stock markets. Two tutorial chapters are also included to help readers quickly grasp the essence of these tools. Finally, a menu-driven software program, Simple GP, accompanies the volume, which will enable readers without a strong programming background to gain hands-on experience in dealing with much of the technical material introduced in this work.
Download or read book Machine Learning and Data Sciences for Financial Markets written by Agostino Capponi and published by Cambridge University Press. This book was released on 2023-04-30 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Download or read book Applied Quantitative Methods for Trading and Investment written by Christian L. Dunis and published by John Wiley & Sons. This book was released on 2004-01-09 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Quantitative Analysis In Financial Markets Collected Papers Of The New York University Mathematical Finance Seminar Vol Ii written by Marco Avellaneda and published by World Scientific. This book was released on 2001-01-10 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains lectures delivered at the celebrated Seminar in Mathematical Finance at the Courant Institute. The lecturers and presenters of papers are prominent researchers and practitioners in the field of quantitative financial modeling. Most are faculty members at leading universities or Wall Street practitioners.The lectures deal with the emerging science of pricing and hedging derivative securities and, more generally, managing financial risk. Specific articles concern topics such as option theory, dynamic hedging, interest-rate modeling, portfolio theory, price forecasting using statistical methods, etc.
Download or read book Data Mining in Finance written by Boris Kovalerchuk and published by Springer Science & Business Media. This book was released on 2005-12-11 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics.
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.
