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Clustering, Long Memory and Stocks’ Performance

In: Advances in Quantitative Methods for Economics and Business

Author

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  • Roy Cerqueti

    (Sapienza University of Rome
    University of Angers)

  • Raffaele Mattera

    (Sapienza University of Rome)

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.

Suggested Citation

  • Roy Cerqueti & Raffaele Mattera, 2025. "Clustering, Long Memory and Stocks’ Performance," Springer Books, in: Salvador Cruz Rambaud & Juan Evangelista Trinidad Segovia & Catalina B. García-García (ed.), Advances in Quantitative Methods for Economics and Business, chapter 0, pages 257-269, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-84782-0_13
    DOI: 10.1007/978-3-031-84782-0_13
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