Optimal Convergence Trading for Cross-listed Stocks

33 Pages Posted: 8 Mar 2020

See all articles by Christian-Oliver Ewald

Christian-Oliver Ewald

University of Glasgow; Høgskole i Innlandet

Pengcheng Song

Peking University

Yao Wu

Peking University

Hai Zhang

Strathclyde Business School

Date Written: January 31, 2020

Abstract

With the intention of maximizing an investor's terminal utility, we construct a non-threshold ased trading model within which the optimal trading weights for daily rebalancing are derived analytically via stochastic optimal control. Having released the constraint that the cointegrating vector is equal to one, we propose a more practical trading strategy that applies to a much wider range of categories of cointegrated assets. We explore extensive out-of-the-sample experiments on the cross-listed stock portfolios, facilitating comparative studies among Chinese and European, UK and US stock markets. We further test the time-delay arbitrage of the strategy using the cross-listed stocks by employing two parallel trading mechanisms, respectively equity-based contracts for difference (CFD) and real shares trading. Our empirical results illustrate that the time-delay arbitrage of the cross-listed stocks strategy based on the analytical solution of weights yields relatively stable and better performance than that of the home market index. Our research is instructive for the practitioner's trading decision in cross-listed stocks and other kinds of convergence investment.

Keywords: Investment Analysis, Optimal Relative-Price Trading, Time-Delay Arbitrage, Cross-Listed Stocks, Stochastic Control

JEL Classification: G11, G15, G61

Suggested Citation

Ewald, Christian-Oliver and Song, Pengcheng and Wu, Yao and Zhang, Hai, Optimal Convergence Trading for Cross-listed Stocks (January 31, 2020). Available at SSRN: https://ssrn.com/abstract=3537909 or http://dx.doi.org/10.2139/ssrn.3537909

Christian-Oliver Ewald

University of Glasgow ( email )

Adam Smith Building
Glasgow, Scotland G12 8RT
United Kingdom

Høgskole i Innlandet ( email )

Lillehammer, 2624
Norway

Pengcheng Song (Contact Author)

Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Yao Wu

Peking University ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Hai Zhang

Strathclyde Business School ( email )

199 Cathedral Street
Glasgow G4 0QU
United Kingdom

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