TY - JFULL AU - Paul Lajbcygier and Seng Lee PY - 2007/8/ TI - Improving Co-integration Trading Rule Profitability with Forecasts from an Artificial Neural Network T2 - International Journal of Economics and Management Engineering SP - 321 EP - 325 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/8604 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 7, 2007 N2 - 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. ER -