Prediction based – high frequency trading on financial time series

By J. Levendovszky, F. Kia. 2012

5 citations recorded by the source corpus.

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 themid 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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Juho Kanniainen
2. Moncef Gabbouj
3. Dat Thanh Tran
4. Alexandros Iosifidis
5. Martin Magris

In Electrical and Electronic Engineering

1. Juho Kanniainen
2. Alexandros Iosifidis
3. Martin Magris
4. Moncef Gabbouj

In Management Science and Operations Research

1. Adamantios Ntakaris
2. Dat Thanh Tran
3. Juho Kanniainen
4. Alexandros Iosifidis
5. Martin Magris
6. Moncef Gabbouj
7. M. Ugur Gudelek
8. Omer Berat Sezer

Citations in the corpus

• Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “Popular machine learning methods in HFT include regression analysis [1], [2], [3], [4], [5], multilayer feed forward network [6], [7], [8], convolutional neural network [9] , recurrent neural network [10], [11], [12].”
• Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017, methodology): “Lev-endovszky and Kia [30] propose a multi-layer feedforward neural network 1 The method is the generalisation of a linear regression model when dependent variable is discrete.”