Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies

47 Pages Posted: 8 Nov 2023 Last revised: 26 Jan 2024

See all articles by Álvaro Cartea

Álvaro Cartea

University of Oxford; University of Oxford - Oxford-Man Institute of Quantitative Finance

Mihai Cucuringu

University of Oxford - Department of Statistics; The Alan Turing Institute

Qi Jin

University of Oxford - Oxford-Man Institute of Quantitative Finance; University of Oxford - Department of Statistics

Date Written: October 11, 2023

Abstract

We propose a method to detect linear and nonlinear lead-lag relationships in stock returns. Our approach uses pairwise Lévy-area and cross-correlation of returns to rank the assets from leaders to followers. We use the rankings to construct a portfolio that longs or shorts the followers based on the previous returns of the leaders, and the stocks are ranked every time the portfolio is rebalanced. The portfolio also takes an offsetting position on the SPY ETF so that the initial value of the portfolio is zero. Our data spans from 1963 to 2022, and we use an average of over 500 stocks to construct portfolios for each trading day. The annualized returns of our lead-lag portfolios are over 20%, and the returns outperform all lead-lag benchmarks in the literature. There is little overlap between the leaders and the followers we find and those that are reported in previous studies based on market capitalization, volume traded, and intra-industry relationships. Our findings support the slow information diffusion hypothesis; i.e., portfolios rebalanced once a day consistently outperform the bidiurnal, weekly, bi-weekly, tri-weekly, and monthly rebalanced portfolios.

Keywords: Return prediction, Lead-lag relationships, Ranking, Lévy-area, Clustering

JEL Classification: G11, G12, G14, G17

Suggested Citation

Cartea, Álvaro and Cucuringu, Mihai and Jin, Qi, Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies (October 11, 2023). Available at SSRN: https://ssrn.com/abstract=4599565 or http://dx.doi.org/10.2139/ssrn.4599565

Álvaro Cartea

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Mihai Cucuringu

University of Oxford - Department of Statistics ( email )

24-29 St Giles
Oxford
United Kingdom

HOME PAGE: http://https://www.stats.ox.ac.uk/~cucuring/

The Alan Turing Institute ( email )

British Library, 96 Euston Road
96 Euston Road
London, NW12DB
United Kingdom

Qi Jin (Contact Author)

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

University of Oxford - Department of Statistics ( email )

24-29 St Giles
Oxford
United Kingdom

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