Please use this identifier to cite or link to this item: https://doi.org/10.1007/BF00436283
Title: A stock selection strategy using fuzzy neural networks
Authors: Wong, F.S. 
Wang, P.Z. 
Teh, H.H. 
Keywords: artificial intelligence
financial analysis
fuzzy logic
neural network
risk analysis
Stock market analysis
Issue Date: May-1991
Citation: Wong, F.S.,Wang, P.Z.,Teh, H.H. (1991-05). A stock selection strategy using fuzzy neural networks. Computer Science in Economics and Management 4 (2) : 77-89. ScholarBank@NUS Repository. https://doi.org/10.1007/BF00436283
Abstract: This paper describes, from a general system-design perspective, an artificial neural network (ANN) approach to a stock selection strategy. The paper suggests a concept of neural gates which are similar to the processing elements in ANN, but generalized into handling various types of information such as fuzzy logic, probabilistic and Boolean information together. Forecasting of stock market returns, assessing of country risk and rating of stocks based on fuzzy rules, probabilistic and Boolean data can be done using the proposed neural gates. Fuzzy logic is known to be useful for decision-making where there is a great deal of uncertainty as well as vague phenomena, but lacks the learning capability; on the other hand, neural networks are useful in constructing an adaptive system which can learn from historical data, but are not able to process ambiguous rules and probabilistic data sets. This paper describes how these problems can be solved using the proposed neural gates. © 1991 Kluwer Academic Publishers.
Source Title: Computer Science in Economics and Management
URI: http://scholarbank.nus.edu.sg/handle/10635/111130
ISSN: 09212736
DOI: 10.1007/BF00436283
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