Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market
AbstractThe 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 35474.
Date of creation: 12 Dec 2011
Date of revision:
Knowledge-based Systems; Fuzzy Sets; Structural Equation Model (SEM); Genetic Algorithm (GA); Currency Volatility;
Find related papers by JEL classification:
- C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- F31 - International Economics - - International Finance - - - Foreign Exchange
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