@article{lead_lag_ML_Bennett_2022,
	author = {Bennett, Stefanos and Cucuringu, Mihai and Reinert, Gesine},
	date = {2022/12/01},
	date-added = {2023-09-03 13:24:56 +0100},
	date-modified = {2023-09-03 13:25:20 +0100},
	doi = {10.1007/s10994-022-06250-4},
	id = {Bennett2022},
	isbn = {1573-0565},
	journal = {Machine Learning},
	number = {12},
	pages = {4497--4538},
	rating = {4},
	title = {{Lead--lag detection and network clustering for multivariate time series with an application to the US equity market}},
	url = {https://doi.org/10.1007/s10994-022-06250-4},
	volume = {111},
	year = {2022},
	bdsk-url-1 = {https://doi.org/10.1007/s10994-022-06250-4},
abstract = {In multivariate time series systems, it has been observed that certain groups of variables partially lead the evolution of the system, while other variables follow this evolution with a time delay; the result is a lead--lag structure amongst the time series variables. In this paper, we propose a method for the detection of lead--lag clusters of time series in multivariate systems. We demonstrate that the web of pairwise lead--lag relationships between time series can be helpfully construed as a directed network, for which there exist suitable algorithms for the detection of pairs of lead--lag clusters with high pairwise imbalance. Within our framework, we consider a number of choices for the pairwise lead--lag metric and directed network clustering model components. Our framework is validated on both a synthetic generative model for multivariate lead--lag time series systems and daily real-world US equity prices data. We showcase that our method is able to detect statistically significant lead--lag clusters in the US equity market. We study the nature of these clusters in the context of the empirical finance literature on lead--lag relations, and demonstrate how these can be used for the construction of predictive financial signals.}
}