Prediction based – High Frequency Trading on Financial Time Series
english

Farhad Kia, János Levendovszky
2013 Proceedings of the 5th International Joint Conference on Computational Intelligence  
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
more » ... data series and achieved a considerable profit on the mid price. In the presence of the bid-ask spread, the gain is smaller but it still ranges in the interval 2-6 percent in 6 months without using any leverage. FFNNs can provide fast prediction which can give rise to high frequency trading on intraday data series.
doi:10.5220/0004555005020506 dblp:conf/ijcci/KiaL13 fatcat:5r7zzbvzxre6rdg2unyctgy7ra