Applying Independent Component Analysis and Predictive Systems for Algorithmic Trading

Attila Ceffer (), Janos Levendovszky () and Norbert Fogarasi ()
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Attila Ceffer: Budapest University of Technology and Economics
Janos Levendovszky: Budapest University of Technology and Economics
Norbert Fogarasi: Budapest University of Technology and Economics

Computational Economics, 2019, vol. 54, issue 1, No 13, 303 pages

Abstract: Abstract In this paper, a Nonlinear AutoRegressive network with eXogenous inputs and a support vector machine are proposed for algorithmic trading by predicting the future value of financial time series. These architectures are capable of modeling and predicting vector autoregressive VAR(p) time series. In order to avoid overfitting, the input is pre-processed by independent component analysis to filter out the most noise like component. In this way, the accuracy of the prediction and the trading performance is increased. The proposed algorithms have a small number of free parameters which makes fast learning and trading possible. The method is not only tested on single asset price series, but also on predicting the value of mean reverting portfolios obtained by maximizing the predictability parameter of VAR(1) processes. The tests were first performed on artificially generated data and then on real data selected from exchange traded fund time series including bid–ask spread. In both cases the proposed method could achieve positive returns.

Keywords: Algorithmic trading; Financial time series; Neural network; Support vector machine; Independent component analysis; Mean reverting portfolio (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-017-9719-z

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