%0 Journal Article
	%A Paul Lajbcygier and  Seng Lee
	%D 2007
	%J International Journal of Economics and Management Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 7, 2007
	%T Improving Co-integration Trading Rule Profitability with Forecasts from an Artificial Neural Network
	%U https://publications.waset.org/pdf/8604
	%V 7
	%X 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.

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