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A Machine Learning based Pairs Trading Investment StrategyResults

A Machine Learning based Pairs Trading Investment Strategy: Results [This chapter compiles the main results obtained in this work. It starts by illustrating the dataset preprocessing outcome and highlights some relevant details when comparing different selection techniques. It proceeds to describe the results achieved by the application of the proposed pairs selection framework (in Chap. 3) and allows us to infer the effectiveness of applying an Unsupervised Learning based technique in this context. The results concerning the new proposed forecasting-based trading model (in Chap. 4) are analyzed next. Some insightful conclusions are inferred regarding the suitability of a forecasting based trading model in this setup.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Machine Learning based Pairs Trading Investment StrategyResults

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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-47250-4
Pages
75 –101
DOI
10.1007/978-3-030-47251-1_6
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter compiles the main results obtained in this work. It starts by illustrating the dataset preprocessing outcome and highlights some relevant details when comparing different selection techniques. It proceeds to describe the results achieved by the application of the proposed pairs selection framework (in Chap. 3) and allows us to infer the effectiveness of applying an Unsupervised Learning based technique in this context. The results concerning the new proposed forecasting-based trading model (in Chap. 4) are analyzed next. Some insightful conclusions are inferred regarding the suitability of a forecasting based trading model in this setup.]

Published: Jul 14, 2020

Keywords: Machine learning; PCA; OPTICS; ARMA; LSTM; Encoder-Decoder

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