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Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market

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  • Su, EnDer
  • Fen, Yu-Gin
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    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.

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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 35474.

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    Date of creation: 12 Dec 2011
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    Handle: RePEc:pra:mprapa:35474

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    Related research

    Keywords: Knowledge-based Systems; Fuzzy Sets; Structural Equation Model (SEM); Genetic Algorithm (GA); Currency Volatility;

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    1. Christopher J. Neely, 2005. "An analysis of recent studies of the effect of foreign exchange intervention," Working Papers, Federal Reserve Bank of St. Louis 2005-030, Federal Reserve Bank of St. Louis.
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    7. Barry Eichengreen & Raul Razo-Garcia, 2006. "The international monetary system in the last and next 20 years," Economic Policy, CEPR;CES;MSH, CEPR;CES;MSH, vol. 21(47), pages 393-442, 07.
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