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Abstract: The past decade has witnessed significant growth in developing intelligent tools for financial forecasting. Expert Systems were quickly shown to be inadequate for the tasks required in financial forecasting due to their static nature. As a result, interest started to move towards soft computing despite the fact that comprehensibility is often of paramount concern in financial forecasting. Merging the domains of fuzzy logic and rule induction paved the way for the emergence of successful... (Update)
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BibTeX entry: (Update)
@misc{ romahi-dynamic,
author = "Yazann Romahi and Qiang Shen",
title = "Dynamic Financial Forecasting with Automatically Induced Fuzzy Associations",
url = "citeseer.ist.psu.edu/284097.html" }
Citations (may not include all citations):921 Mining Association Rules Between Sets of Items in Large Data.. - Agrawal, Imierlinkski et al. - 1993
209 Mining Quantitative Association Rules in Large Relational Ta.. - Srikant, Agrawal - 1996
126 A Survey and Critique of Techniques for Extracting Rules fro.. (context) - Andrews, Diedrich et al. - 1995
11 How Effective are Neural Networks at Forecasting and Predict.. (context) - Adya, Collopy - 1998
6 An Effective Algorithm for Discovering Fuzzy Rules in Relati.. (context) - Au, Chan - 1998
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2 Option Pricing Using ANNs: The Case of S&P 500 Index Call Op.. (context) - Qi, Maddala - 1995
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2 Mining Generalised Association Rules (context) - Srikant, Agrawal - 1995
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Documents on the same site (http://www.romahi.com/yazann/uni/finance.html):
Reinforcement Learning in Computational Finance - Romahi (1999) (Correct)
Reinforcement Learning in Computational Finance - Romahi (1999) (Correct)
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