Author : Rogemar S. Mamon
Publisher : Springer Science & Business Media
Page : 203 pages
File Size : 15,76 MB
Release : 2007-04-26
Category : Business & Economics
ISBN : 0387711635
A number of methodologies have been employed to provide decision making solutions globalized markets. Hidden Markov Models in Finance offers the first systematic application of these methods to specialized financial problems: option pricing, credit risk modeling, volatility estimation and more. The book provides tools for sorting through turbulence, volatility, emotion, chaotic events – the random "noise" of financial markets – to analyze core components.
Author : Sylvia Frühwirth-Schnatter
Publisher : Springer Science & Business Media
Page : 506 pages
File Size : 16,66 MB
Release : 2006-11-24
Category : Mathematics
ISBN : 0387357688
The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.
Author : Bellie Sivakumar
Publisher : World Scientific
Page : 542 pages
File Size : 40,59 MB
Release : 2010
Category : Science
ISBN : 9814307971
This book comprehensively accounts the advances in data-based approaches for hydrologic modeling and forecasting. Eight major and most popular approaches are selected, with a chapter for each stochastic methods, parameter estimation techniques, scaling and fractal methods, remote sensing, artificial neural networks, evolutionary computing, wavelets, and nonlinear dynamics and chaos methods. These approaches are chosen to address a wide range of hydrologic system characteristics, processes, and the associated problems. Each of these eight approaches includes a comprehensive review of the fundamental concepts, their applications in hydrology, and a discussion on potential future directions.
Author : Álvaro Cartea
Publisher : Cambridge University Press
Page : 360 pages
File Size : 29,91 MB
Release : 2015-08-06
Category : Mathematics
ISBN : 1316453650
The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms, then this is the book for you.
Author : Nizar Bouguila
Publisher : Springer Nature
Page : 303 pages
File Size : 16,94 MB
Release : 2022-05-19
Category : Technology & Engineering
ISBN : 3030991423
This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.
Author : Nikolaos Freris
Publisher : European Alliance for Innovation
Page : 2592 pages
File Size : 43,83 MB
Release : 2023-02-15
Category : Business & Economics
ISBN : 1631903853
It is our great pleasure to have you at the 2022 4th International Conference on Economic Management and Model Engineering (ICEMME 2022), which was held in Nanjing, China from November 18th to 20th (virtual event). It is an international forum for academic communications between experts and scholars in the fields of economic management and model engineering. The main objective of ICEMME 2022 is to provide a platform to deliberate latest developments and future directions in the fields of economic management and model engineering. The conference provided opportunities for the delegates to exchange research ideas and scientific information, and established business or research relations for all participants to find global partners for future collaboration.
Author : Igor Tulchinsky
Publisher : John Wiley & Sons
Page : 265 pages
File Size : 35,73 MB
Release : 2019-10-01
Category : Business & Economics
ISBN : 111957126X
Discover the ins and outs of designing predictive trading models Drawing on the expertise of WorldQuant’s global network, this new edition of Finding Alphas: A Quantitative Approach to Building Trading Strategies contains significant changes and updates to the original material, with new and updated data and examples. Nine chapters have been added about alphas – models used to make predictions regarding the prices of financial instruments. The new chapters cover topics including alpha correlation, controlling biases, exchange-traded funds, event-driven investing, index alphas, intraday data in alpha research, intraday trading, machine learning, and the triple axis plan for identifying alphas. • Provides more references to the academic literature • Includes new, high-quality material • Organizes content in a practical and easy-to-follow manner • Adds new alpha examples with formulas and explanations If you’re looking for the latest information on building trading strategies from a quantitative approach, this book has you covered.
Author : Christian Dunis
Publisher : Routledge
Page : 239 pages
File Size : 15,62 MB
Release : 2014-03-26
Category : Business & Economics
ISBN : 1136195114
Computational intelligence, a sub-branch of artificial intelligence, is a field which draws on the natural world and adaptive mechanisms in order to study behaviour in changing complex environments. This book provides an interdisciplinary view of current technological advances and challenges concerning the application of computational intelligence techniques to financial time-series forecasting, trading and investment. The book is divided into five parts. The first part introduces the most important computational intelligence and financial trading concepts, while also presenting the most important methodologies from these different domains. The second part is devoted to the application of traditional computational intelligence techniques to the fields of financial forecasting and trading, and the third part explores the applications of artificial neural networks in these domains. The fourth part delves into novel evolutionary-based hybrid methodologies for trading and portfolio management, while the fifth part presents the applications of advanced computational intelligence modelling techniques in financial forecasting and trading. This volume will be useful for graduate and postgraduate students of finance, computational finance, financial engineering and computer science. Practitioners, traders and financial analysts will also benefit from this book.
Author : Kevin P. Murphy
Publisher : MIT Press
Page : 1102 pages
File Size : 14,11 MB
Release : 2012-08-24
Category : Computers
ISBN : 0262018020
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Author : Jun Chen
Publisher : CRC Press
Page : 165 pages
File Size : 17,5 MB
Release : 2020-09-14
Category : Business & Economics
ISBN : 1000220168
Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.
