Titlebook: Decision Technologies for Computational Finance; Proceedings of the f Apostolos-Paul N. Refenes,Andrew N. Burgess,John E Conference proceed

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https://doi.org/10.1057/978-1-349-93358-7erfectly correlated, thus motivating the use of a population-based algorithm which jointly optimises a portfolio of decorrelated models. We describe an application of this methodology to trading statistical arbitrage between equity index futures and present empirical results, before concluding with

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Controlling Nonstationarity in Statistical Arbitrage Using a Portfolio of Cointegration Modelserfectly correlated, thus motivating the use of a population-based algorithm which jointly optimises a portfolio of decorrelated models. We describe an application of this methodology to trading statistical arbitrage between equity index futures and present empirical results, before concluding with

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Multi-Task Learning in a Neural Vector Error Correction Approach for Exchange Rate Forecastingerent, yet related, tasks simultaneously, underlying interdependencies between the various learning outputs can be exploited. The paper presents a neural Vector Error Correction approach with multiple output units as a Multi-Task Learning methodology of practical use in finance. By focusing on forec