{
  "id": "2201.08283",
  "version": "v1",
  "published": "2022-01-20T16:39:57.000Z",
  "updated": "2022-01-20T16:39:57.000Z",
  "title": "Lead-lag detection and network clustering for multivariate time series with an application to the US equity market",
  "authors": [
    "Stefanos Bennett",
    "Mihai Cucuringu",
    "Gesine Reinert"
  ],
  "comment": "29 pages, 28 figures; preliminary version appeared at KDD 2021 - 7th SIGKKDD Workshop on Mining and Learning from Time Series (MiLeTS)",
  "categories": [
    "stat.ML",
    "cs.LG",
    "q-fin.ST",
    "stat.ME"
  ],
  "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 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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-01-20T16:39:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "multivariate time series",
      "equity market",
      "network clustering",
      "lead-lag detection",
      "statistically significant lead-lag clusters"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 29,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}