Pattern Recognition Of Stochastic Processes In Market Data

This book introduces Stochastic Processes and its applications in Finance. It also combines Artificial Intelligence with Finance. The Hough Transformation is used to identify stochastic processes in dynamical systems. Mathematics of Wiener processes are treated in detail and their applications in different markets are shown. The Hough transform is used to locate market processes where transactions occur within the market.

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition, S+SSPR 2016, consisting of the International Workshop on Structural and Syntactic Pattern Recognition SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The 51 full papers presented were carefully reviewed and selected from 68 submissions. They are organized in the following topical sections: dimensionality reduction, manifold learning and embedding methods; dissimilarity representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis; spatio-temporal pattern recognition; structural matching; text and document analysis.

Academically thorough and up-to-date quantitative and qualitative market research methods text for business and social science students.

This book constitutes the refereed proceedings of the 10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014, held in St. Petersburg, Russia in July 2014. The 40 full papers presented were carefully reviewed and selected from 128 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.

Practical guide for deriving insight and commercial gain from data Monetising Data offers a practical guide for anyone working with commercial data but lacking deep knowledge of statistics or data mining. The authors — noted experts in the field — show how to generate extra benefit from data already collected and how to use it to solve business problems. In accessible terms, the book details ways to extract data to enhance business practices and offers information on important topics such as data handling and management, statistical methods, graphics and business issues. The text presents a wide range of illustrative case studies and examples to demonstrate how to adapt the ideas towards monetisation, no matter the size or type of organisation. The authors explain on a general level how data is cleaned and matched between data sets and how we learn from data analytics to address vital business issues. The book clearly shows how to analyse and organise data to identify people and follow and interact with them through the customer lifecycle. Monetising Data is an important resource: Focuses on different business scenarios and opportunities to turn data into value Gives an overview on how to store, manage and maintain data Presents mechanisms for using knowledge from data analytics to improve the business and increase profits Includes practical suggestions for identifying business issues from the data Written for everyone engaged in improving the performance of a company, including managers and students, Monetising Data is an essential guide for understanding and using data to enrich business practice.

As operations research (OR) applications continue to grow and flourish in a number of decision making fields, a reference that is comprehensive, concise, and easy to read is more than a nicety, it is a necessity. This book provides a single volume overview of OR applications in practice, making it the first resource a practitioner would reach for w

Aimed at providing an introduction to fundamental concepts and mathematical foundations essential for studying dynamics of financial markets, this volume focuses on stochastic processes and the manner in which they provide the basic framework for modeling the markets. Key Feautres: The book is mathematical in nature, but is not heavy on proofs Contains many examples Simulations and analysis of real data from different financial markets The overall objective is to make the presentation concrete and illustrate successes and limitations of models. In the process, readers are also made aware of a number of advances in the field.

The main purpose of this book is to resolve deficiencies and limitations that currently exist when using Technical Analysis (TA). Particularly, TA is being used either by academics as an “economic test” of the weak-form Efficient Market Hypothesis (EMH) or by practitioners as a main or supplementary tool for deriving trading signals. This book approaches TA in a systematic way utilizing all the available estimation theory and tests. This is achieved through the developing of novel rule-based pattern recognizers, and the implementation of statistical tests for assessing the importance of realized returns. More emphasis is given to technical patterns where subjectivity in their identification process is apparent. Our proposed methodology is based on the algorithmic and thus unbiased pattern recognition. The unified methodological framework presented in this book can serve as a benchmark for both future academic studies that test the null hypothesis of the weak-form EMH and for practitioners that want to embed TA within their trading/investment decision making processes. ​