Improving Co-integration Trading Rule Profitability with Forecasts from an Artificial Neural Network
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 , is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071198Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 975
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