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Training a multilayer neural network for the Euro-dollar (EUR/ USD) exchange rate
Entrenamiento de una red neuronal multicapa para la tasa de cambio euro - dólar (EUR/USD)
DOI:
https://doi.org/10.15446/ing.investig.v27n3.14851Keywords:
artificial neural network, chemotaxis, FOREX, trading strategy (en)chemotaxis, estrategias de negociación, Forex, redes neuronales artificiales, redes multicapa, JEL, F310, C450 (es)
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A mathematical tool or model for predicting how an economic variable like the exchange rate (relative price between two currencies) will respond is a very important need for investors and policy-makers. Most current techniques are based on statistics, particularly linear time series theory. Artificial neural networks (ANNs) are mathematical models which try to emulate biological neural networks’ parallelism and nonlinearity; these models have been successfully applied in Economics and Engineering since the 1980s. ANNs appear to be an alternative for modelling the behavior of financial variables which resemble (as first approximation) a random walk. This paper reports the results of using ANNs for Euro/USD exchange rate trading and the usefulness of the algorithm tor chemotaxis leading to training networks thereby maximising an objective function re predicting a trader’s profits. JEL: F310, C450.
Tanto para los inversionistas como para las autoridades económicas es necesario que se desarrolle una herramienta matemática que logre dar cuenta de la dirección de una variable como el tipo de cambio (el precio relativo entre dos monedas). Muchos de los mecanismos usados actualmente están basados en el uso de técnicas estadísticas, en particular series de tiempo lineales. Las redes neuronales artificiales (RNA) son modelos matemáticos que pretenden emular el funcionamiento del cerebro humano, su aplicación en economía e ingeniería surge a finales de los años ochenta con buenos resultados. Las RNA se presentan como una alternativa para simular el comportamiento de variables financieras que, por lo general, tienden a parecerse a un paseo aleatorio. En este trabajo se muestran los resultados del entrenamiento de una red neuronal para negociación de la tasa de cambio EUR/USD y las bondades del algoritmo de entrenamiento chemotaxis, que permite entrenar redes que maximicen una función objetivo que relacione aciertos en la predicción con las ganancias de un trader.
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1. Jimmy Rafael Landaburu Mendoza, Luz María Quinde Arreaga, Nuvia Aurora Zambrano Barros, Adolfo Hernán Elizondo Saltos. (2023). Aplicación de series de tiempo en valores de activos financieros. Religación, 8(38), p.e2301117. https://doi.org/10.46652/rgn.v8i38.1117.
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Copyright (c) 2007 Jaime Alberto Villamil Torres, Jesús Alberto Delgado Rivera
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