Abstract
The search for models which can accurately forecast the market trend has developed over the past decades. Technical indicators and oscillators are the most usually employed inputs in the prediction models. These inputs basically rely on prices and the evolution of the index itself, which may cause some problems like multicolinearity and autocorrelation, in the case of linear models, or overoptimization and noise, in the case of neural networks. This paper proposes filtering the inputs to be employed in the models. To this end, their impact on the forecast will be analysed. A support vector machine will be used to this end, in order to characterize both inputs (indicators and oscillators) and output (market trend). Doing this, it can be assessed whether the relationship between the different inputs and the market trend offers relevant information regarding the contribution of the inputs in the prediction process and whether this contribution remains constant over time. Those inputs will be selected, which obtain more stable forecasts in order to obtain more consistent predictions.
Highlights
It can be assessed whether the relationship between the di erent inputs and the market trend o ers relevant information regarding the contribution of the inputs in the prediction process and whether this contribution remains constant over time
Predicting the direction of stock market index movement using an optimized artificial neural network model
Summary
La búsqueda de modelos para la predicción de la tendencia de los índices bursátiles se ha desarrollado en las últimas décadas. Los indicadores y osciladores técnicos son los inputs más utilizados en todos los modelos. Se va a analizar el impacto que tienen éstos en el proceso de predicción de la tendencia de un índice bursátil. El modelo utilizado es la support vector machine que permite encontrar las características tanto de los inputs (indicadores y osciladores) como del output (la tendencia del índice). Este mapeo de la relación de los indicadores y la tendencia ofrece información relevante sobre si dicha contribución a su predicción es estable en el tiempo. Se seleccionarán aquellos inputs cuyas características estabilicen las predicciones en los modelos.
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