The primary goal of this book is to present to the scientific and management communities a selection of applications using recent Soft Computing (SC) and Computing with Words and Perceptions (CWP) models and techniques meant to solve some economics and financial problems that are of utmost importance. The book starts with a coverage of data mining tools and techniques that may be of use and significance for economic and financial analyses and applications. Notably, fuzzy and natural language based approaches and solutions for a more human consistent dealing with decision support, time series analysis, forecasting, clustering, etc. are discussed. The second part deals with various decision making models, particularly under probabilistic and fuzzy uncertainty, and their applications in solving a wide array of problems including portfolio optimization, option pricing, financial engineering, risk analysis etc. The selected examples could also serve as a starting point or as an opening out, in the SC and CWP techniques application to a wider range of problems in economics and finance.

This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM) methods. It introduces readers to a range of data science methods, and demonstrates their application in the fields of business, health, economics, finance and engineering. In addition, it provides suggestions based on the assessment results on each topic, which can help to enhance the efficiency of the financial system and the sustainability of economic development. Given its scope, the book will help readers broaden their perspective on the assessment and evaluation of financial issues using data science and MCDM approaches.

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.

"This book explores the use of data science applications such as web mining, text mining, and machine learning in business, health, economics, finance, and engineering"--

The chapters of the book are evolved from presentations made by selected participants at the 2005 BISC International Special Event, held at the University of California at Berkely. The papers include reports from the different front of soft computing in various industries and address the problems of different fields of research in fuzzy logic, fuzzy set and soft computing. The book provides a collection of forty-four articles in two volumes.

To deal with the abundant amount of information in the environment in order to achieve our goals, human beings adopt a strategy to accumulate some information and filter out other information to ultimately make decisions. Since the development of cognitive science in the 1960s, researchers have been interested in understanding how human beings process and accumulate information for decision-making. Researchers have conducted extensive behavioral studies and applied a wide range of modeling tools to study human behavior in simple-detection tasks and two-choice decision tasks (e.g., discrimination, classification). In general, researchers often assume that the manner in which information is processed for decision-making is invariant across individuals given a particular experimental context. Independent variables, including speed-accuracy instructions, stimulus properties (i.e., intensity), and characteristics of the participants (i.e., aging, cognitive ability) are assumed to affect the parameters in a model (i.e., speed of information accumulation, response bias) but not the way that participants process information (e.g., the order of information processing). Given these assumptions, much modeling has been accomplished based on the grouped data, rather than the individual data. However, a growing number of studies have demonstrated that there were individual differences in the perceptual decision process. In the same task context, different groups of the participants may process information in different manners. The capacity and architecture of the decision mechanism were found to vary across individuals, implying that humans’ decision strategies can vary depending on the context to maximize their performance. In this special issue, we focused on a particular subset of cognitive models, particularly accumulator models, multinomial processing trees and systems factorial technology (SFT) as applied to perceptual decision making. The motivation for the focus on perceptual decision-making is threefold. Empirical studies of perception have grown out of a history of making a large number of observations for each individual so as to achieve precise estimates of each individual’s performance. This type of data, rather than a small number of observations per individual, is most amenable to achieving precision in individual-level and group-level cognitive modeling. Second, the interaction between the acquisition of perceptual information and the decisions based on that information (to the extent that those processes are distinguishable) offers rich data for scientific exploration. Finally, there is an increasing interest in the practical application of individual variation in perceptual ability, whether to inform perceptual training and expertise, or to guide personnel decisions. Although these practical applications are beyond the scope of this issue, we hope that the research presented herein may serve as the foundation for future endeavors in that domain.