A Machine Learning based Pairs Trading Investment Strategy

(0) Erste Bewertung abgeben
20%
CHF 57.60 Sie sparen CHF 14.40
Print on demand - Exemplar wird für Sie besorgt.
Kartonierter Einband
Kein Rückgaberecht!

Beschreibung

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.



Discusses unsupervised learning applied in pairs trading

Presents exclusive trading models

Simulates the performance of a pairs trading strategy using commodity-linked ETFs with 5-min frequency price series



Autorentext

Simão Sarmento received his B.Sc. and M.Sc. degrees in Electrical and Computer Engineering from the Instituto Superior Técnico (IST), University of Lisbon, Portugal, in 2017 and 2019, respectively. In 2018, he completed the first year of his master's degree at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, where he focused on machine learning. He also worked at the Research and Development Department of Feedzai from July to September 2018, investigating the application of deep learning to fight financial fraud.

Nuno Horta (S'89-M'97-SM'11) received his Licenciado, M.Sc., Ph.D. and Postdoctorate degrees in Electrical and Computer Engineer from the Instituto Superior Técnico (IST), University of Lisbon, Portugal, in 1989, 1992, 1997 and 2014, respectively. In March 1998, he joined the IST Electrical and Computer Engineering Department, where he is currently an Associate Professor. Since 1998, he has also been Head of the Integrated Circuits Group at the Instituto de Telecomunicações. He has authored or co-authored more than 150 publications including books, book chapters, international journal papers and conference papers. He has also participated as a researcher or coordinator in several national and European R&D projects. He was General Chair of AACD 2014, PRIME 2016 and SMACD 2016 and was a member of the organizing and technical program committees of several other conferences, e.g., IEEE ISCAS, IEEE LASCAS, DATE, NGCAS, etc. He is an Associated Editor of Integration, The VLSI Journal, and serves as a reviewer for several prestigious publications, including IEEE TCAD, IEEE TEC, IEEE TCAS, ESWA, and ASC. His research interests include analog and mixed-signal IC design, analog IC design automation, soft computing and data science.



Inhalt

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

Mehr anzeigen

Produktinformationen

Titel
A Machine Learning based Pairs Trading Investment Strategy
Autor
EAN
9783030472504
ISBN
978-3-030-47250-4
Format
Kartonierter Einband
Herausgeber
Springer, Berlin
Genre
Technik
Anzahl Seiten
104
Gewicht
196g
Größe
H234mm x B235mm x T156mm
Jahr
2020
Untertitel
Englisch
Auflage
1st ed. 2021
Mehr anzeigen
Andere Kunden kauften auch