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A machine learning based pairs tradi...

Moraes Sarmento, Simao.

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A machine learning based pairs trading investment strategy

Record Type:	Electronic resources : Monograph/item
Title/Author:	A machine learning based pairs trading investment strategy/ by Simao Moraes Sarmento, Nuno Horta.
Author:	Moraes Sarmento, Simao.
other author:	Horta, Nuno.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	ix, 104 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Pairs Trading - Background and Related Work -- Proposed Pairs Selection Framework -- Proposed Trading Model -- Implementation -- Results -- Conclusions and Future Work.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-47251-1
ISBN:	9783030472511

A machine learning based pairs trading investment strategy
Moraes Sarmento, Simao.

A machine learning based pairs trading investment strategy[electronic resource] /by Simao Moraes Sarmento, Nuno Horta. - Cham :Springer International Publishing :2021. - ix, 104 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology, Computational intelligence. - SpringerBriefs in applied sciences and technology.Computational intelligence..

Introduction -- Pairs Trading - Background and Related Work -- Proposed Pairs Selection Framework -- Proposed Trading Model -- Implementation -- Results -- Conclusions and Future Work.

This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.

ISBN: 9783030472511

Standard No.: 10.1007/978-3-030-47251-1doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .M67 2021

Dewey Class. No.: 006.31

A machine learning based pairs trading investment strategy
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245 1 2 $a A machine learning based pairs trading investment strategy $h [electronic resource] / $c by Simao Moraes Sarmento, Nuno Horta.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a ix, 104 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, Computational intelligence
505 0 $a Introduction -- Pairs Trading - Background and Related Work -- Proposed Pairs Selection Framework -- Proposed Trading Model -- Implementation -- Results -- Conclusions and Future Work.
520 $a This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.
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650 2 4 $a Economic Theory/Quantitative Economics/Mathematical Methods. $3 2162305
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950 $a Intelligent Technologies and Robotics (SpringerNature-42732)