Abstract
In this study we try to briefly revise the day of the week effect (DOW) and to examine why there are conflicting empirical results through the time. Moreover, we try to add a new-alternative view to the specific area of study, adding a further possible explanation in calendar anomalies field of study. Specifically, we try to examine if investors’ weekday behavior changes depend on the financial trend. For example, let suppose that there are evidence that Mondays are positive returns days, but there are signs for an upcoming financial crisis. Could this general believed practical rule be strong enough in order to be sustainable even during financial crisis period or does it change? In order to analyze this issue providing empirical support, we examine the US stock market and the S&P index for the time period 2000–2013. The results confirm our assumption that the financial trend influences the weekly stock returns’ pattern, which may be an alternative explanation for the conflicting empirical findings that have been documented in the literature up today.
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Notes
- 1.
Usually turn of the month days are defined as the last day of a month and the first three trading days of the following month.
- 2.
This study examines latter data questions Sias and Starks (1995) findings.
- 3.
The term day of the week is generally used because quite often in the literature is reported a week pattern which is not the weekend effect (e.g. positive return day Tuesday and negative Wednesday).
- 4.
- 5.
Ülkü and Prodan (2013) suggest that when today’s closing price (Pt) crosses the n-days moving average MA(n) from below we buy, but when the Pt crosses the MA(n) from above we sell. n is the time period for which we calculate the moving average. This period could be short-term (n < 10), medium term (10 < n < 150) or long term (n > 150).
- 6.
Local maximum is a mathematical term for the peak.
- 7.
Friedrich Nietzsche’s said “The future influences the present just as much as the past”. Following this quote we thought that investors forecasts influence their current behavior and this was the starting point which finally helped us resolve the financial trend definition issue.
- 8.
The 4th sub-period’s max value, according to the data which we had available, is the 18/9/2013 (1725.52), but we use as end of period date 16/10/2013 (1721.54) in order to make some more observations and because the difference was not significant enough to change our conclusions (we run the model taking the 18/9/2013 as period-end date). All these data are available upon request.
- 9.
This partly confirms our prior characterization for the total period as no-trend period.
- 10.
The leptokurtosis of these three distributions is a sign that linear models may not be adequate to explain the specific time series’ behavior (Vasileiou 2015).
- 11.
We run the OLS models and we find significant ARCH effect problems. Moreover, if we adopt the OLS (assuming that the ARCH effect does not exist) the results are different than the final. These data are available upon request. The appropriate model selection is crucial, because (i) through time some studies which report the weekend effect fade, are called in question due to the violations of the OLS assumptions in the returns (Connolly 1989; Alford and Guffey 1996) and (ii) may be the reason for the conflicting findings.
- 12.
- 13.
These data are available upon request.
- 14.
The Akaike and Schwartz criterions present better results if we use the t-statistics instead of the Generalized Error Distribution (GED) proposed by Nelson (1991).
- 15.
Generally, similar to related studies when the two criteria suggest different models, lag orders the SIC is preferred to the AIC because: (i) it corrects the over-fitting nature of the AIC, and (ii) it is asymptotically consistent (Koehler and Murphree 1988). However, in most of the cases both criteria suggest similar results.
- 16.
More statistical information are available upon request.
References
Abraham A, Ikenberry D (1994) The individual investor and the weekend effect. J Financ Quant Anal 29:263–278
Agrawal A, Tandon K (1994) Anomalies or illusions? Evidence from the stock markets in eighteen countries. J Int Money Financ 13(1):83–106
Alford A, Guffey D (1996) A re-examination of international seasonalities. J Financ Econ 5:1–17
Branch B, Jung J, Yang T (2001) The monday merger effect. Int Rev Financ Anal 10(1):1–18
Brooks C (2008) Introductory econometrics for finance. Cambridge University Press, Cambridge
Brusa J, Liu P, Schulman C (2005) Weekend effect, ‘reverse’ weekend effect, and investor trading activities. J Bus Financ Acc 32:1495–1517
Chan SH, Leung WK, Wang K (2004) The impact of institutional investors on the Monday seasonal. J Bus 77:967–986
Chen H, Singal V (2003) Role of speculative short sales in price formation: the case of the weekend effect. J Financ 58:685–706
Connolly R (1989) An examination of the robustness of the weekend effect. J Financ Quant Anal 24(2):133–169
Cross F (1973) The behavior of stock prices on Fridays and Mondays. Financ Anal J 29:67–69
Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Financ 25(2):383–417
French K (1980) Stock returns and the weekend effect. J Financ Econ 8:55–69
Glosten LR, Jagannathan R, Runkle DE (1993) On the relation between the expected value and the volatility of the nominal excess return on stocks. J Financ 48:1779–1801
Gu AX (2004) The reversing weekend effect: evidence from the U.S. equity markets. Rev Quant Financ Acc 22:5–14
Jaffe J, Westerfield R (1985) The week-end effect in common stock returns: the international evidence. J Financ 40:433–454
Keim D, Stambaugh R (1984) A further investigation of the weekend effect in stock returns. J Financ 39:819–840
Koehler A, Murphree E (1988) A comparison of the Akaike and Schwarz criteria for selecting model order. J R Stat Soc 37(2):187–195
Lakonishok J, Maberly E (1990) The weekend effect: trading patterns of individual and institutional investors. J Financ 45:231–243
Mills TC, Siriopoulos C, Markellos RN, Harizanis D (2000) Seasonality in the Athens stock exchange. Appl Financ Econ 10(2):137–142
Nelson DB (1991) Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59:347–370
Schwert GW (2003) Anomalies and market efficiency. In: Constantinides G et al (eds) Handbook of the economics of finance, Chap 17. Elsevier, Amsterdam
Sias R, Starks L (1995) The day-of-the-week anomaly: the role of institutional investors. Financ Anal J 12:58–67
Syed AB, Sadorsky P (2006) Day-of-the-week effects in emerging stock markets. Appl Econ Lett 13:621–628
Ülkü N, Prodan E (2013) Drivers of technical trend-following rules’ profitability in world stock markets. Int Rev Financ Anal 30:214–229. doi:10.1016/j.irfa.2013.08.005
Vasileiou E (2015) Long live day of the week patterns and the financial trends’ role. Evidence from the Greek stock market during the Euro Era (2002–12). Invest Manag Financ Innovat 12(3):19–32
Vasileiou E, Samitas A (2014) Does the financial crisis influence the month and the trading month effects? Evidence from the Athens stock exchange. Stud Econ Financ 32(2):181–203
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Vasileiou, E. (2017). Calendar Anomalies in Stock Markets During Financial Crisis: The S&P 500 Case. In: Hacioğlu, Ü., Dinçer, H. (eds) Global Financial Crisis and Its Ramifications on Capital Markets. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-47021-4_34
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