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Modelos predictivos de lógica y lógica borrosa en índices bursátiles de América del Norte

Author

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  • Parisi F., Antonino

    (Universidad de Chile)

  • Parisi F., Franco

    (Universidad de Chile)

Abstract

This article continues with the research related to predict stock index, such as, genetic algorithms and neuronal networks. Parametrics or non parametrics, lineals and non lineals models, try to recognize patterns and relations that express themselves in a mathematical language, through the estimation of coefficients and their statistical significance. However, most of the agents in the stock market use a language that incorporates qualitative aspects to refer to, for example, the price of an asset, the yield of the investment, etc. In this context, the quantitative models have problems to absorb this information, which suggests the need to develop and analyze new techniques, in corporating this type of references. The methodology of fuzzy logic gives answer to this question because it’s based on the idea that the variables should be handled not as a number but as characteristics that they represent. We used historic series of daily prices of the North American stock index DJI and Nasdaq (USA), IPC (México) y TSE (Canada), corresponding to the period October 8, 1996 and January 7, 2005. We designed a model of logic and fuzzy logic, to forecast the stock indexes sign variations. The logic models and the fuzzy logic models reached a forecast capability statistically significant. In addition, both models achieved a significant and positive abnormal return when they were used as a trading strategy, even after transaction costs.// Este artículo continúa con la línea de investigación relativa a modelos predictivos de índices bursátiles: algoritmos genéticos y redes neuronales. Los modelos anteriores, paramétricos o no paramétricos, lineales y no lineales, buscan reconocer pautas de comportamiento y relaciones que se expresan en un lenguaje matemático, por medio de la estimación de coeficientes y su significación estadística. Sin embargo, la mayoría de los agentes que participan en el mercado bursátil utiliza un lenguaje que incorpora aspectos cualitativos para referirse, por ejemplo, al precio de un activo, a la rentabilidad de la inversión, etc. En este contexto, los modelos cuantitativos tienen dificultades para absorber esta información, lo que plantea la necesidad de desarrollar y analizar el uso de nuevas técnicas que permitan incorporar este tipo de referencias. La metodología de lógica borrosa, basada en la idea de que las variables deben ser manejadas no como un número sino más bien por las características que ellas presentan, responde a esta inquietud. Se utilizaron series históricas de cotizaciones diarias de los índices bursátiles norteamericanos DJI y Nasdaq (Estados Unidos), IPC (México) y TSE (Canadá), correspondientes al periodo comprendido entre el 8 de octubre de 1996 y el 7 de enero de 2005. Se construyó un modelo de lógica y otro de lógica difusa, para efectos de proyectar el signo de las variaciones de los índices bursátiles ya señalados. Los modelos de lógica y de lógica borrosa tuvieron una capacidad predictiva estadísticamente significativa. Además, ambos modelos lograron un rendimiento extranormal significativo y positivo al ser utilizados en una estrategia de comercio (trading), aun después de considerar los costos de transacción.

Suggested Citation

  • Parisi F., Antonino & Parisi F., Franco, 2006. "Modelos predictivos de lógica y lógica borrosa en índices bursátiles de América del Norte," El Trimestre Económico, Fondo de Cultura Económica, vol. 0(290), pages 265-288, abril-jun.
  • Handle: RePEc:elt:journl:v:73:y:2006:i:290:p:265-288
    DOI: http://dx.doi.org/10.20430/ete.v73i290.545
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    File URL: http://www.eltrimestreeconomico.com.mx/index.php/te/article/view/545/721
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    More about this item

    Keywords

    lógica borrosa; funciones de pertenencia; conjuntos de pertenencia; reglas de comercio (trading); desfuzificación; porcentaje de predicción de signo; prueba de acierto direccional;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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