Clustering, Long Memory and Stocks’ Performance

Roy Cerqueti () and Raffaele Mattera ()
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Roy Cerqueti: Sapienza University of Rome
Raffaele Mattera: Sapienza University of Rome

Chapter Chapter 13 in Advances in Quantitative Methods for Economics and Business, 2025, pp 257-269 from Springer

Abstract: Abstract In this chapter, we investigate the existence of a relationship between long memory, considering Hurst exponents, and financial performances, taking the Sharpe ratio. To this aim, we collect a sample of more than one thousand stocks in the U.S. financial market. Moreover, we identify clusters of stocks characterized by different relationships using clusterwise mixture regression modelling. We find that a large Hurst exponent is associated with a low financial performance. However, we also show that this relationship obeys a clustered structure and that the relationship is not the same across the identified clusters.

Keywords: Long-range dependence; Hurst exponent; Econophysics; Sharpe ratio; Cluster analysis (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-84782-0_13

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DOI: 10.1007/978-3-031-84782-0_13

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