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

Giovanis, Eleftherios (2009): Bootstrapping Fuzzy-GARCH Regressions on the Day of the Week Effect in Stock Returns: Applications in MATLAB.

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

• Item Type: MPRA Paper
• Language: English
• Keywords: stock returns, day of the week effect, calendar effects/anomalies, GARCH regression, fuzzy logic, fuzzy rules, fuzzy regression, bootstrapping regression, MATLAB
• Subjects: G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill

References

Aggarwal, R. and Rivoli, P. (1989). Seasonal and Day-of-the-Week Effects in Four Emerging Stock Markets, The Financial Review, Vol. 24 No. 7, pp. 541-550

Aggarwal, A. and Tandon, K. (1994). Anomalies or illusions? Evidence from stock markets in eighteen countries, Journal of International Money and Finance, Vol.13, pp. 083-106

Alagidede, P. (2008). Day of the week seasonality in African stock markets, Applied Financial Economics Letters, Vol. 4, pp. 115-120

Arsad, Z. and Coutts, G.A. (1997). Security price anomalies in the London International Stock Exchange: a 60 year perspective¸ Applied Financial Economics, Vol. 7, pp. 455-464

Draper, P. and Paudyal, K. (2002). Explaining Monday returns, The Journal of Financial Research, Vol. 25 No.4, pp. 507–520

Engle, R. F. and Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility, Journal of Finance, Vol. 48, pp. 1749-78

Giovanis, E. (2009). Calendar Effects in Fifty-five Stock Market Indices, Global Journal of Finance and Management, Vol. 1 No.2, pp. 75-98

Glosten, L.R., Jagannathan, R. and Runkle, D.E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Returns on Stocks, Journal of Finance, Vol. 48 No.5, pp. 1779-1801

Kamara, A. (1997). New Evidence on the Monday Seasonal in Stock Returns, Journal of Business, Vol. 70 No.1, pp. 63-84.

Lakonishok, J. and Smidt, S. (1988). Are Seasonal Anomalies Real? A Ninety-Year Perspective, The Review of Financial Studies, Vol. 1 No.4, pp. 403-425

Marquering, J., Nisser, J. and Valla, T. (2006). Disappearing anomalies: a dynamic analysis of the persistence of anomalies, Applied Financial Economics, Vol. 16, pp. 291–302

Mehdian, S. and Perry, M. (2001). The Reversal of the Monday Effect: New Evidence from US Equity Markets, Journal of Business Finance and Accounting, Vol. 28 No. 7/8, pp. 1043-1065

Mills, T.C. and Coutts, A.J. (1995). Calendar effects in the London Stock Exchange FT-SE indices, The European Journal of Finance, Vol. 1, pp. 79-93

Mills, T.C., Siriopoulos, C., Markellos, R.N. and Harizanis, D. (2000). Seasonality in the Athens stock exchange, Applied Financial Economics, Vol. 10, pp. 137-142

Nelson, D. B., (1991). Conditional Heteroskedasticity In Asset Returns: A New Approach, Econometrica, Vol. 59, pp. 347-370

Onyuma, S.O. (2009). Day-of-the-Week and Month-of-the-Year Effect on the Kenyan Stock Market Returns, Eastern Africa Social Science Research Review, Vol. 25 No.2, pp. 53-74

Papadopoulos, Β.Κ. and Sirpi, Μ.Α. (1999). Similarities in Fuzzy Regression Models, Journal of Optimization Theory and Applications, Vol. 2 No.2, pp. 373-383

Pettengill, G.N. (2003). A survey of the Monday effect literature, Quarterly Journal of Business and Economics, Vol. 42 No. 3/4, pp. 3-28

Sullivan, R., Timmermann, A. and White, H. (2001). Dangers of data mining: The case of calendar effects in stock returns, Journal of Econometrics, Vol. 105, pp. 249–286