Constructing Structural Equation Model Rule-Based Fuzzy System with Genetic Algorithm

EnDer Su (), Thomas W. Knowles and Yu-Gin Fen
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Thomas W. Knowles: Stuart School of Business, Illinois Institute of Technology, Chicago, IL, USA
Yu-Gin Fen: College of Finance and Banking, National Kaohsiung First University of Science and Technology, Kaohsiung City, Taiwan

International Journal of Strategic Decision Sciences (IJSDS), 2016, vol. 7, issue 2, 69-88

Abstract: The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state.

Date: 2016
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