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Improving Co-integration Trading Rule Profitability with Forecasts from an Artificial Neural Network

Authors: Paul Lajbcygier, Seng Lee

Abstract:

Co-integration models the long-term, equilibrium relationship of two or more related financial variables. Even if cointegration is found, in the short run, there may be deviations from the long run equilibrium relationship. The aim of this work is to forecast these deviations using neural networks and create a trading strategy based on them. A case study is used: co-integration residuals from Australian Bank Bill futures are forecast and traded using various exogenous input variables combined with neural networks. The choice of the optimal exogenous input variables chosen for each neural network, undertaken in previous work [1], is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy.

Keywords: Artificial neural networks, co-integration, forecasting, trading rule.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071198

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References:


[1] P.Lajbcygier and S.Lee Input variable selection for neural networks using cross-validation and bootstrap, working paper.
[2] Campbell, J. Y., A. w. Lo, et al. (1997). "The econometrics of financial markets.": 512.
[3] Ripley, B (1996) Pattern Recognition and Neural Networks Cambridge University Press.