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Computational Intelligence Techniques for Trading and Investment

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Adaptive Filtering
Adaptive Filtering Techniques
Adaptive Genetic Algorithm
AI
algorithms
ARMA Model
Artificial Fish Swarm Algorithm
artificial intelligence
Category=KCJ
Category=KFF
Category=UYQ
CME Group
computational
computational economics
computational intelligence
CRB
CRB Index
econometric models
economic forecasting
economic modeling
economic modelling
economic models
ensemble learning methods
eq_bestseller
eq_business-finance-law
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
evolutionary algorithms finance
evolutionary computation
experimental economics
Filter Based Feature Selection
financial analysis
financial modeling
financial modelling
financial models
financial time series analysis
Financial Time Series Forecasting
financial trading
forecasting
Fuzzy Regression Models
hybrid computational intelligence trading
Kalman Filter
LMS Algorithm
markov
Markov Blanket
MLP NNs
neural network modelling
Neural Network Strategy
neural networks
Out-of Sample Dataset
quantitative analysis
quantitative finance research
Random Forests
RLS
RLS Algorithm
Sharpe Ratio
support vector machines finance
Technical Trading Rules
time series data
trading strategies
Treynor Index
USD Exchange Rate
velupillai
Vice Versa
zambelli

Product details

  • ISBN 9780415636803
  • Weight: 580g
  • Dimensions: 156 x 234mm
  • Publication Date: 06 Mar 2014
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About

Christian Dunis is Emeritus Professor of Banking and Finance at Liverpool John Moores University, UK and Joint General Manager of global risk and new products at Horus Partners Wealth Management Group SA, Switzerland.

Spiros Likothanassis is Professor and Director at the Pattern Recognition Laboratory in the Department of Computer Engineering and Informatics at the University of Patras, Greece.

Andreas Karathanasopoulos is Senior Lecturer in Finance and Risk Management at the University of East London, UK

Georgios Sermpinis is Senior Lecturer in Economics at the University of Glasgow, UK.

Konstantinos Theofilatos is a Post-Doctoral Researcher in the Department of Computer Engineering and Informatics at the University of Patras, Greece.