Abstract
Pairs trading is an instance of statistical arbitrage that relies on heavy quantitative data analysis to profit by capitalising low-risk trading opportunities provided by anomalies of related assets. A key element in pairs trading is the rule by which open and close trading triggers are defined. This paper investigates the use of time series regression to define the rule which has previously been identified with fixed threshold-based approaches. Empirical results indicate that our approach may yield significantly increased excess returns compared to ones obtained by previous approaches on large capitalisation stocks in the Korean equities market.
| Original language | English |
|---|---|
| Pages (from-to) | 755-768 |
| Number of pages | 14 |
| Journal | Journal of Experimental and Theoretical Artificial Intelligence |
| Volume | 29 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2017 Jul 4 |
Bibliographical note
Publisher Copyright:© 2016 Informa UK Limited, trading as Taylor & Francis Group.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
Keywords
- ARIMA
- DTW
- Pairs trading
- SVR
- cointegration
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Artificial Intelligence