Improving Moving Average Trading Rules with Boosting and Statistical Learning Methods, страница 14

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Authors’ biographies:

Fernando Fernández-Rodríguez has an MSc in Mathematics (Universidad de Zaragoza, Spain) and PhD in Economics (Universidad de Las Palmas de Gran Canaria, Spain). His current research interest includes nonlinear dynamics and fi nance. He has published articles in international journals in his fi eld including System Dynamic Review, Bulletin of the Polish Academy of Mathematical Sciences, European Economics Review, Journal of Business, Journal of Applied Econometrics, International Journal of Forecasting, Economics Letters, Studies in Nonlinear Dynamics and Econometrics and Japan and the World Economy. His articles have also appeared in several proceedings of national and international conferences. He is currently Full Professor at the Universidad de Las Palmas de Gran Canaria.

Julián Andrada-Félix has an MSc in Mathematics (Universidad de La Laguna, Spain) and a PhD in Economics (Universidad de Las Palmas de Gran Canaria, Spain). His current research interests include nonlinear dynamics and fi nance. He has published articles in international journals in his fi eld including Journal of Applied Econometrics, International Journal of Forecasting, Journal of Empirical Finance, Studies in Nonlinear Dynamics and Econometrics, Applied Financial Economics and Applied Economics Letters. His articles have also appeared in several proceedings of national and international conferences. He is currently Profesor Titular at the Universidad de Las Palmas de Gran Canaria.

Authors’ addresses:

Fernando Fernández-Rodríguez and Julián Andrada-Félix, Facultad de Ciencias Económicas y Empresariales, 35017 Las Palmas de Gran Canaria, Spain.



[1] The data are taken from http://research.stlouisfed.org/fred/data/wkly/dtb3.

[2] All these ex ante rules are obtained using the boosting, committee and Bayesian algorithms with a training period of 100 days.