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The Resource Applications of artificial intelligence in finance and economics, edited by Jane M. Binner, Graham Kendall, and Shu-Heng Chen, (electronic book)

Applications of artificial intelligence in finance and economics, edited by Jane M. Binner, Graham Kendall, and Shu-Heng Chen, (electronic book)

Label
Applications of artificial intelligence in finance and economics
Title
Applications of artificial intelligence in finance and economics
Statement of responsibility
edited by Jane M. Binner, Graham Kendall, and Shu-Heng Chen
Contributor
Subject
Genre
Language
eng
Summary
Artificial intelligence is a consortium of data-driven methodologies which includes artificial neural networks, genetic algorithms, fuzzy logic, probabilistic belief networks and machine learning as its components. We have witnessed a phenomenal impact of this data-driven consortium of methodologies in many areas of studies, the economic and financial fields being of no exception. In particular, this volume of collected works will give examples of its impact on the field of economics and finance. This volume is the result of the selection of high-quality papers presented at a special session entitled 'Applications of Artificial Intelligence in Economics and Finance' at the '2003 International Conference on Artificial Intelligence' (IC-AI '03) held at the Monte Carlo Resort, Las Vegas, Nevada, USA, June 23-26 2003. The special session, organised by Jane Binner, Graham Kendall and Shu-Heng Chen, was presented in order to draw attention to the tremendous diversity and richness of the applications of artificial intelligence to problems in Economics and Finance. This volume should appeal to economists interested in adopting an interdisciplinary approach to the study of economic problems, computer scientists who are looking for potential applications of artificial intelligence and practitioners who are looking for new perspectives on how to build models for everyday operations. There are still many important Artificial Intelligence disciplines yet to be covered. Among them are the methodologies of independent component analysis, reinforcement learning, inductive logical programming, classifier systems and Bayesian networks, not to mention many ongoing and highly fascinating hybrid systems. A way to make up for their omission is to visit this subject again later. We certainly hope that we can do so in the near future with another volume of Applications of Artificial Intelligence in Economics and Finance
Member of
Cataloging source
UtOrBLW
Dewey number
330.015195
Index
no index present
LC call number
HB139
LC item number
.A67 2004
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorDate
  • 1961-
  • 1961-
  • 1959-
  • 2003
http://library.link/vocab/relatedWorkOrContributorName
  • Binner, Jane M.
  • Kendall, Graham
  • Chen, Shu-Heng
  • International Conference on Artificial Intelligence
Series statement
Advances in econometrics
Series volume
19
http://library.link/vocab/subjectName
  • Econometrics
  • Economics
  • Finance
Label
Applications of artificial intelligence in finance and economics, edited by Jane M. Binner, Graham Kendall, and Shu-Heng Chen, (electronic book)
Instantiates
Publication
Contents
Statistical analysis of genetic algorithms in discovering technical trading strategies / Chueh-Yung Tsao, Shu-Heng Chen -- Co-evolving neural networks with evolutionary strategies : a new application to divisia money / Jane M. Binner, Graham Kendall, Alicia Gazely -- Forecasting the EMU inflation rate : linear econometric vs. non-linear computational models using genetic neural fuzzy systems / Stefan Kooths, Timo Mitze, Eric Ringhut -- Finding or not finding rules in time series / Jessica Lin, Eamonn Keogh -- A comparison of var and neural networks with genetic algorithm in forecasting price of oil / Sam Mirmirani, Hsi Cheng Li -- Searching for divisia/inflation relationships with the aggregate feedforward neural network / Vincent A. Schmidt, Jane M. Binner -- Predicting housing value : genetic algorithm attribute selection and dependence modelling utilising the gamma test / Ian D. Wilson, Antonia J. Jones, David H. Jenkins, J.A. Ware -- A genetic programming approach to model international short-term capital flow / Tina Yu, Shu-Heng Chen, Tzu-Wen Kuo -- Tools for non-linear time series forecasting in economics : an empirical comparison of regime switching vector autoregressive models and recurrent neural networks / Jane M. Binner, Thomas Elger, Birger Nilsson, Jonathan A. Tepper -- Using non-parametric search algorithms to forecast daily excess stock returns / Nathan Lael Joseph, David S. Bre, Efstathios Kalyvas
Control code
bslw06311239
Dimensions
unknown
Extent
1 online resource (xiii, 275 p.).
Form of item
online
Isbn
9781849503037
Specific material designation
remote
Label
Applications of artificial intelligence in finance and economics, edited by Jane M. Binner, Graham Kendall, and Shu-Heng Chen, (electronic book)
Publication
Contents
Statistical analysis of genetic algorithms in discovering technical trading strategies / Chueh-Yung Tsao, Shu-Heng Chen -- Co-evolving neural networks with evolutionary strategies : a new application to divisia money / Jane M. Binner, Graham Kendall, Alicia Gazely -- Forecasting the EMU inflation rate : linear econometric vs. non-linear computational models using genetic neural fuzzy systems / Stefan Kooths, Timo Mitze, Eric Ringhut -- Finding or not finding rules in time series / Jessica Lin, Eamonn Keogh -- A comparison of var and neural networks with genetic algorithm in forecasting price of oil / Sam Mirmirani, Hsi Cheng Li -- Searching for divisia/inflation relationships with the aggregate feedforward neural network / Vincent A. Schmidt, Jane M. Binner -- Predicting housing value : genetic algorithm attribute selection and dependence modelling utilising the gamma test / Ian D. Wilson, Antonia J. Jones, David H. Jenkins, J.A. Ware -- A genetic programming approach to model international short-term capital flow / Tina Yu, Shu-Heng Chen, Tzu-Wen Kuo -- Tools for non-linear time series forecasting in economics : an empirical comparison of regime switching vector autoregressive models and recurrent neural networks / Jane M. Binner, Thomas Elger, Birger Nilsson, Jonathan A. Tepper -- Using non-parametric search algorithms to forecast daily excess stock returns / Nathan Lael Joseph, David S. Bre, Efstathios Kalyvas
Control code
bslw06311239
Dimensions
unknown
Extent
1 online resource (xiii, 275 p.).
Form of item
online
Isbn
9781849503037
Specific material designation
remote

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