A Machine Learning based Pairs Trading Investment Strategy (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Computational Intelligence
Release date:	July 2020
First published:	2021
Authors:	Simao Moraes Sarmento • Nuno Horta
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	104
Edition:	1st ed. 2021
ISBN-13:	978-3-03-047250-4
Categories:	Books > Business & Economics > Economics > Economic theory & philosophy Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-047250-7
Barcode:	9783030472504

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