Recent Advances in Computational Finance


Nikolaos Thomaidis (Editor)
Department of Financial Engineering & Management, University of the Aegean, Chios, GREECE

Gordon H. Dash (Editor)
Professor of Finance and Decision Sciences, University of Rhode Island, College of Business Administration, Kingston, Rhode Island, USA

Series: Business Issues, Competition and Entrepreneurship, Financial Institutions and Services
BISAC: BUS027000

As it stands today, the spectrum of methods, tools, and applications that populate the area of computational finance is literally vast. Distinctively, it is this vast domain that differentiates today’s financial decision makers from their counterparts of just a decade ago. Couched within this landscape are a set of increasingly complex resource utilization decisions; decisions that are, today, impacted by a surprising growth in technology that now spans a more globally diverse production and engineering environment. Collectively, firm financial managers, portfolio managers, and enterprise risk managers continue to exhort the computational finance community to formulate effective tools that more descriptively reconcile difficult problems in new product development, risk mitigation, and overall enterprise management.

The computational finance community has responded to this call by offering refinements to classic computational methods while also introducing new ones. From continuous optimization to natural and evolutionary computing to time-series econometrics, this edition covers contemporary developments in computational finance. The book examines how interdisciplinary contributions from applied mathematics, statistics, and engineering can be adapted to a problem-solving approach in finance with an emphasis on vexing, but identifiable, real-world problems. (Imprint: Nova)



Table of Contents


Chapter 1. Short-term Market Forecasting for Intraday Trading with Neuro-Evolutionary Modeling
(Antonia Azzini, Mauro Dragoni and Andrea G. B. Tettamanzi, Università degli Studi di Milano, Italy and others)

Chapter 2. Detecting Fraudulent Financial Statements through Nature Inspired Techniques
(Yorgos Goletsis, Christos Katsis and Evangelos Koumanakos, University of Ioannina, Greece, and others)

Chapter 3. High-frequency Trading With Type-2 Fuzzy Logic Time Series Forecasting and Hilbert Transforms
(Abdalla Kablan and Wing Lon Ng, University of Malta, Malta, and others)

Chapter 4. Production of Efficient Wealth Maximization Using Neuroeconomic Behavioral Drivers and Continuous Automated Trading
(Nina Kajiji and John Forman, Department of Computer Science and Statistics, University of Rhode Island, USA, and others)

Chapter 5. Applications of Stochastic Hybrid Systems in Portfolio Optimization
(Erdem Kilic, Azar Karimov and Gerhard-Wilhelm Weber, Middle East Technical University, Turkey)

Chapter 6. Genetic Programming: Current Trends and Applications in Computational Finance
(Gabriel Kronberger, Michael Affenzeller and Stefan Fink, University of Applied Science Upper Austria, Austria, and others)

Chapter 7. Mean-variance Portfolio Optimization with Cardinality and Class Constraints Using Multiobjective Evolutionary Algorithms
(Georgios Mamanis and Konstantinos P. Anagnostopoulos, Democritus University of Thrace, Greece)

Chapter 8. A Review of Multi-criteria Portfolio Optimization by Mathematical Programming
(Bartosz Sawik, AGH University of Science & Technology, Poland)

Chapter 9. Predicting Stock Price Movements from Concept Map Information
(Ankit Soni, Nees Jan Van Eck and Uzay Kaymak, Indian Institute of Technology Kanpur, India)

Chapter 10. Computational Practice: Multivariate Parametric or Nonparametric Modelling of European Bond Volatility Spillover?
(Nina Kajiji and Gordon H. Dash, Jr., University of Rhode Island, USA)


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