Prediction based - High Frequency Trading on Financial Time Series
نویسندگان
چکیده
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 FOREX 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.
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