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Prediction based – High Frequency Trading on Financial Time Series
english
2013
Proceedings of the 5th International Joint Conference on Computational Intelligence
english
In this paper we investigate prediction based trading on financial time series assuming general AR(J) models. A suitable nonlinear estimator for predicting the future values will be provided by a properly trained FeedForward Neural Network (FFNN) which can capture the characteristics of the conditional expected value. In this way, one can implement a simple trading strategy based on the predicted future value of the asset price and comparing it to the current value. The method is tested on
doi:10.5220/0004555005020506
dblp:conf/ijcci/KiaL13
fatcat:5r7zzbvzxre6rdg2unyctgy7ra