Towards a unified approach to industry recovery: Insights from intraday stock data and advanced community detection methods

Eamon Bracht, Robert Brunner and Jeff McMullin

Physica A: Statistical Mechanics and its Applications, 2025, vol. 669, issue C

Abstract: In this paper, we explore the impact of various time series parameters — such as sampling frequency, sample period, and time series length — on the ability to recover industry classifications within financial networks. By using high-frequency stock data from the S&P 500 from 2005 to 2012, we construct information connection networks using normalized mutual information (NMI) and employ the Planar Maximally Filtered Graph (PMFG) to filter noise. We apply both Leiden and spectral clustering algorithms to identify communities of stocks and compare them with the Global Industry Classification Standard (GICS) using the Adjusted Rand Index (ARI) to assess clustering accuracy. Our analysis reveals that the optimal recovery of industry structures occurs at a sampling frequency much faster than daily: with ARI values peaking at frequencies between 4 min and 48 min timescale and decreasing over longer frequencies. We observe that higher sampling frequencies introduce noise, leading to weaker clustering performance, likely due to the Epps effect. Additionally, the results indicate that ARI is sensitive to market conditions, with higher clustering accuracy during and after periods of market volatility, such as the 2008 financial crisis.

Keywords: Adjusted rand index; Community detection; Stock market; Correlation networks; Normalized mutual information; GICS (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437125001530
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:669:y:2025:i:c:s0378437125001530

DOI: 10.1016/j.physa.2025.130501

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().