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Critérios de formação de carteiras de ativos através de hierarchical clusters
[Criteria of portfolio formation of stocks through hierarchical clusters]

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  • Lucena, Pierre
  • Figueiredo, Antonio Carlos
  • Lachtermacher, Gerson

Abstract

This paper has as main objective to present and to test a tool of multivariate statistics in financial models. This methodology, known as clusters analysis, separates the observations in groups through its determined characteristic, in contrast of the traditional methodology, which is only the order through quantiles. This tool was applied in 213 shares negotiated in the São Paulo Stock Exchange (Bovespa), separating to the groups for size and book-to-market. Later, the new portfolios were applied in the Fama and French Model (1996), comparing the results in a portfolio formation for quintiles and for cluster analysis. Better results were found in the second methodology. The authors conclude that the cluster analysis can be more adequate, because tends to form more homogeneous groups, being useful its application for portfolio formation, and for financial theory.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 38105.

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Date of creation: 20 Feb 2008
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Publication status: Published in Revista de Administração Mackenzie 2.11(2009): pp. 123-141
Handle: RePEc:pra:mprapa:38105

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Related research

Keywords: Quantiles; Cluster analysis; Data Mining; Anomalies; Fama and French model;

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References

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  1. Newton C. A. da Costa Jr. & Myrian B. Eiras das Neves, 2000. "Variáveis Fundamentalistas e os Retornos das Ações," Revista Brasileira de Economia, Graduate School of Economics, Getulio Vargas Foundation (Brazil), vol. 54(1), pages 123-137, January.
  2. Stephen A. Ross, . "The Arbitrage Theory of Capital Asset Pricing," Rodney L. White Center for Financial Research Working Papers 2-73, Wharton School Rodney L. White Center for Financial Research.
  3. Grinblatt, Mark & Moskowitz, Tobias J., 2004. "Predicting stock price movements from past returns: the role of consistency and tax-loss selling," Journal of Financial Economics, Elsevier, vol. 71(3), pages 541-579, March.
  4. Ricardo Torres & Marco Antonio Cesar Bonomo & Cristiano Fernandes, 2002. "A Aleatoriedade do Passeio na Bovespa: Testando a Eficiência do Mercado Acionário Brasileiro," Revista Brasileira de Economia, Graduate School of Economics, Getulio Vargas Foundation (Brazil), vol. 56(2), pages 199-247, April.
  5. De Bondt, Werner F M & Thaler, Richard, 1985. " Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
  6. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-56, July.
  7. Lucena, Pierre & Fugueiredo, Antonio Carlos, 2004. "Pressupostos de Eficiência de Mercado: um estudo empírico na Bovespa
    [Assumptions of Market Efficiency: an empirical analysis at Bovespa/Brazil]    ," MPRA Paper 40884, University Library of Munich, Germany.
  8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
  9. Fama, Eugene F & French, Kenneth R, 1996. " Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
  10. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. " Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
  11. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
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