Action-Based Quality Management
Action-Based Quality Management

Author: Marta Peris-Ortiz

Publisher: Springer

Published: 2014-06-13

Total Pages: 197

ISBN-13: 3319064533

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Featuring case studies from the industrial and tourism sectors, this book provides an interdisciplinary perspective on the effect of total quality management on business and innovation strategies. The principles of Total Quality Management (TQM) have been widely researched and analyzed as an essential tool for businesses to compete in a globalized economy. This book presents the latest research on the applications of TQM across different functions such as customer service, human resources management and cost control. It demonstrates how the utilization of TQM tools, such as the SERVQUAL model, Eco-Management and Audit Scheme (EMAS), High Involvement Practices (HIWP) and the EFQM excellence model, impacts a firm’s performance, enhances productivity and innovation and reduces cost, thereby allowing them to compete more effectively in the global market. Building on the extensive literature on the relationship between TQM and business performance, the authors argue that quality acts as a powerful competitive tool that companies should embrace in their corporate strategy. By promoting activities that result in greater efficiency, improved control and management of the organization (internal quality), firms can achieve significant improvement in customer satisfaction, employee satisfaction, social impact and business results (external quality) and exceed expectations in these areas.

Artificial Intelligence and Computational Intelligence
Artificial Intelligence and Computational Intelligence

Author: Jingsheng Lei

Publisher: Springer

Published: 2012-09-28

Total Pages: 804

ISBN-13: 3642334784

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This volume proceedings contains revised selected papers from the 4th International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. The total of 163 high-quality papers presented were carefully reviewed and selected from 724 submissions. The papers are organized into topical sections on applications of artificial intelligence, applications of computational intelligence, data mining and knowledge discovery, evolution strategy, expert and decision support systems, fuzzy computation, information security, intelligent control, intelligent image processing, intelligent information fusion, intelligent signal processing, machine learning, neural computation, neural networks, particle swarm optimization, and pattern recognition.

Cybernetic Trading Strategies
Cybernetic Trading Strategies

Author: Murray A. Ruggiero

Publisher: John Wiley & Sons

Published: 1997-07-01

Total Pages: 344

ISBN-13: 9780471149200

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Ein Überblick über die aktuellsten Technologien zum Aufbau einer Handelsstrategie: neuronale Netzwerke, genetische Algorithmen, Expertensysteme, Fuzzy logic und statistische Mustererkennung. Gezeigt wird, wie diese neuen Methoden in klassische Analysenverfahren integriert werden können. Auch Erläuterungen zur Prüfung und Bewertung existierender Systeme kommen nicht zu kurz.

The Essentials of Machine Learning in Finance and Accounting
The Essentials of Machine Learning in Finance and Accounting

Author: Mohammad Zoynul Abedin

Publisher: Routledge

Published: 2021-06-20

Total Pages: 259

ISBN-13: 1000394115

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• A useful guide to financial product modeling and to minimizing business risk and uncertainty • Looks at wide range of financial assets and markets and correlates them with enterprises’ profitability • Introduces advanced and novel machine learning techniques in finance such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and applies them to analyze finance data sets • Real world applicable examples to further understanding

Artificial Intelligence and Economic Theory: Skynet in the Market
Artificial Intelligence and Economic Theory: Skynet in the Market

Author: Tshilidzi Marwala

Publisher: Springer

Published: 2017-09-18

Total Pages: 206

ISBN-13: 3319661043

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This book theoretically and practically updates major economic ideas such as demand and supply, rational choice and expectations, bounded rationality, behavioral economics, information asymmetry, pricing, efficient market hypothesis, game theory, mechanism design, portfolio theory, causality and financial engineering in the age of significant advances in man-machine systems. The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence concepts such as the swarming of birds, the working of the brain and the pathfinding of the ants. Artificial Intelligence and Economic Theory: Skynet in the Market analyses the impact of artificial intelligence on economic theories, a subject that has not been studied. It also introduces new economic theories and these are rational counterfactuals and rational opportunity costs. These ideas are applied to diverse areas such as modelling of the stock market, credit scoring, HIV and interstate conflict. Artificial intelligence ideas used in this book include neural networks, particle swarm optimization, simulated annealing, fuzzy logic and genetic algorithms. It, furthermore, explores ideas in causality including Granger as well as the Pearl causality models.

Globalization, Gating, and Risk Finance
Globalization, Gating, and Risk Finance

Author: Unurjargal Nyambuu

Publisher: John Wiley & Sons

Published: 2018-01-16

Total Pages: 476

ISBN-13: 1119252652

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An in-depth guide to global and risk finance based on financial models and data-based issues that confront global financial managers. Globalization, Gating, and Risk Finance offers perspectives on global risk finance in a world with economies in transition. Developed from lectures and research projects investigating the consequences of globalization and strategic approaches to fundamental economics and finance, it provides an approach based on financial models and data; it includes many case-study problems. The book departs from the traditional macroeconomic and financial approaches to global and strategic risk finance, where economic power and geopolitical issues are intermingled to create complex and forward-looking financial systems. Chapter coverage includes: Globalization: Economies in Collision; Data, Measurements, and Global Finance; Global Finance: Utility, Financial Consumption, and Asset Pricing; Macroeconomics, Foreign Exchange, and Global Finance; Foreign Exchange Models and Prices; Asia: Financial Environment and Risks; Financial Currency Pricing, Swaps, Derivatives, and Complete Markets; Credit Risk and International Debt; Globalization and Trade: A Changing World; and Compliance and Financial Regulation. Provides a framework for global financial and inclusive models, some of which are not commonly covered in other books. Considers risk management, utility, and utility-based multi-agent financial theories. Presents a theoretical framework to assist with a variety of problems ranging from derivatives and FX pricing to bond default to trade and strategic regulation. Provides detailed explanations and mathematical proofs to aid the readers’ understanding. Globalization, Gating, and Risk Finance is appropriate as a text for graduate students of global finance, general finance, financial engineering, and international economics, and for practitioners.

Rational Machines and Artificial Intelligence
Rational Machines and Artificial Intelligence

Author: Tshilidzi Marwala

Publisher: Academic Press

Published: 2021-03-31

Total Pages: 272

ISBN-13: 0128209445

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Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. - Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? - Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions - Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets - Discusses the application of Moore's Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality

Genetic Algorithms in Search, Optimization, and Machine Learning
Genetic Algorithms in Search, Optimization, and Machine Learning

Author: David Edward Goldberg

Publisher: Addison-Wesley Professional

Published: 1989

Total Pages: 436

ISBN-13:

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A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.

Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation
Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation

Author: Samuelson Hong, Wei-Chiang

Publisher: IGI Global

Published: 2013-03-31

Total Pages: 357

ISBN-13: 1466636297

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Evolutionary computation has emerged as a major topic in the scientific community as many of its techniques have successfully been applied to solve problems in a wide variety of fields. Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation provides comprehensive research on emerging theories and its aspects on intelligent computation. Particularly focusing on breaking trends in evolutionary computing, algorithms, and programming, this publication serves to support professionals, government employees, policy and decision makers, as well as students in this scientific field.

Machine Learning in Finance
Machine Learning in Finance

Author: Matthew F. Dixon

Publisher: Springer Nature

Published: 2020-07-01

Total Pages: 565

ISBN-13: 3030410684

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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.