A machine learning based pairs trading investment strategy
Author(s)
Bibliographic Information
A machine learning based pairs trading investment strategy
（Springer briefs in applied sciences and technology, . Computational intelligence）
Springer, c2021
- : pbk
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ISSN for subseries "SpringerBriefs in computational inrelligence": 26253704
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
Chapter 1. Introduction
Chapter 2. Pairs Trading - Background and Related Work
Chapter 3. Proposed Pairs Selection Framework
Chapter 4. Proposed Trading Model
Chapter 5. Implementation
Chapter 6. Results
Chapter 7. Conclusions and Future Work
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