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Bootstrapping Fuzzy-GARCH Regressions on the Day of the Week Effect in Stock Returns: Applications in MATLAB

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  • Giovanis, Eleftherios

Abstract

This paper examines the well know day of the week effect on stock returns. Various approaches have been developed and applied in order to examine calendar effects in stock returns and to formulate appropriate financial and risk portfolios. We propose an alternative approach in the estimation of the day of the week effect. More specifically we apply fuzzy regressions with triangular membership function in four major stock market index returns. We expect that if the day of the week is valid, then the Monday returns should be negative or lower than the other days of the week and in addition Friday returns should be the highest. The main findings and results are mixed and based on the fuzzy regression we conclude that there isn’t the day of the week or the Monday effect. Specifically, we find a reverse Monday effect in S&P 500, a negative Friday effect in FTSE-100, a positive Tuesday effect in NIKKEI-225 and no effects in DAX index. The specific approach is appropriate as fuzzy logic regression is appropriate and able to capture the impressions and nonlinearities in finance and human behaviour, which are main characteristics in financial industry. Furthermore fuzzy regression avoids the classification of dummy variables to values of one and zero, as we do in the traditional statistical and econometric methodology

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 22326.

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Date of creation: 2009
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Handle: RePEc:pra:mprapa:22326

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Related research

Keywords: stock returns; day of the week effect; calendar effects/anomalies; GARCH regression; fuzzy logic; fuzzy rules; fuzzy regression; bootstrapping regression; MATLAB;

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  1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
  2. Kamara, Avraham, 1997. "New Evidence on the Monday Seasonal in Stock Returns," The Journal of Business, University of Chicago Press, vol. 70(1), pages 63-84, January.
  3. T. C. Mills & C. Siriopoulos & R. N. Markellos & D. Harizanis, 2000. "Seasonality in the Athens stock exchange," Applied Financial Economics, Taylor and Francis Journals, vol. 10(2), pages 137-142.
  4. Paul Alagidede, 2008. "Day of the week seasonality in African stock markets," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 4(2), pages 115-120.
  5. Sullivan, Ryan & Timmermann, Allan & White, Halbert, 2001. "Dangers of data mining: The case of calendar effects in stock returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 249-286, November.
  6. Zainudin Arsad & J. Andrew Coutts, 1997. "Security price anomalies in the London International Stock Exchange: a 60 year perspective," Applied Financial Economics, Taylor and Francis Journals, vol. 7(5), pages 455-464.
  7. Paul Draper & Krishna Paudyal, 2002. "Explaining Monday Returns," Journal of Financial Research, Southern Finance Association & Southwestern Finance Association, vol. 25(4), pages 507-520.
  8. Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-78, December.
  9. Wessel Marquering & Johan Nisser & Toni Valla, 2006. "Disappearing anomalies: a dynamic analysis of the persistence of anomalies," Applied Financial Economics, Taylor and Francis Journals, vol. 16(4), pages 291-302.
  10. Terence Mills & J. Andrew Coutts, 1995. "Calendar effects in the London Stock Exchange FT-SE indices," European Journal of Finance, Taylor and Francis Journals, vol. 1(1), pages 79-93.
  11. Agrawal, Anup & Tandon, Kishore, 1994. "Anomalies or illusions? Evidence from stock markets in eighteen countries," Journal of International Money and Finance, Elsevier, vol. 13(1), pages 83-106, February.
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