Rule extraction from technology IPOs in the US stock market

Mitsdorffer, R., Diederich, J. and Tan, C. (2002). Rule extraction from technology IPOs in the US stock market. In: L. Wang, J. C. Rajapakse, K. Fukushima, S. Lee and X. Yao, Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. ICONIP'02, Orchid Country Club, Singapore, (2328-2335). 18-22 November 2002.


Author Mitsdorffer, R.
Diederich, J.
Tan, C.
Title of paper Rule extraction from technology IPOs in the US stock market
Conference Paper Type Fully Published Paper
Conference name ICONIP'02
DOI 10.1109/ICONIP.2002.1201910
Conference location Orchid Country Club, Singapore
Conference dates 18-22 November 2002
Proceedings title Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
Journal name Iconip'02: Proceedings of the 9th International Conference On Neural Information Processing
Editor L. Wang
J. C. Rajapakse
K. Fukushima
S. Lee
X. Yao
Place published Nanynag Technological University, Singapore
Publisher Nanyang Technological University, Singapore
Publication date 2002
Volume number 5
Issue number IEEE Catalog Number: 02EX575
ISBN 981-04-7524-1
Start page 2328
End page 2335
Total pages 8
Collection year 2002
Language eng
Abstract/Summary Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.
Subjects E1
280205 Text Processing
700103 Information processing services
Q-Index Code E1

Document type: Conference Paper
Sub-type: Fully Published Paper
Collections: Excellence in Research Australia (ERA) - Collection
Centre for Online Health Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Google Scholar Search Google Scholar
Access Statistics: 30 Abstract Views  -  Detailed Statistics
Created: Fri, 24 Aug 2007, 01:49:30 EST