ACADEMY OF ECONOMIC STUDIES DOCTORAL SCHOOL OF FINANCE AND BANKING

ACADEMY OF ECONOMIC STUDIESDOCTORAL SCHOOL OF FINANCE AND BANKING

DISSERTATION PAPER

The day of the week effect on stock market return and volatility:

International evidence

Student: Sorin Stoica Supervisor: Professor Moisa Altar

BUCHAREST, JULY 2008

Contents

1. Introduction

2. Literature review

3. Data and Model description

4. Empirical results

5. Conclusions

6. Bibliography

1. Introduction

A market is efficient if prices fully and instantaneously reflect all available information and no profit opportunities are left unexploited. In an efficient situation, new information is unpredictable, so stock market returns cannot be predicted and there is therefore no trading pattern, which an investor can follow in order to make unexpected profits.

*(The efficient-market hypothesis was developed by Professor Eugene Fama at the University of Chicago Graduate School of Business as an academic concept of study through his published Ph.D. thesis in the early 1960s at the same school)

The day of the week effect refers to the existence of a pattern of stock returns during the week, a seasonal «anomaly», which contradicts the «Efficient Market Hypothesis» *

2. Literature review

Cross (1973) and French (1980) were the first to observe a specific seasonality in stock returns during the week, that was named «Day of the Week Effect». According to this phenomenon, the average stock market return on the last trading day of the week (Friday) is positive and is the highest across all days of the week and the return on the first trading day of the week (Monday) is negative and is the lowest across the same period.

French (1987) examine the relationship between stock prices and volatility and report that unexpected stock market returns are negatively related to the unexpected changes in volatility. Campbell and Hentschel (1992) report similar results and argue that an increase in stock market volatility raises the required rate of return on common stocks and hence lowers stock prices. Glosten (1993) and Nelson (1991), on the other hand, report that positive unanticipated returns reduce conditional volatility whereas negative unanticipated returns increase conditional volatility.

Chen (2001) examine the day of the week effect in the stock markets of China for the recent years. The conclusion is consistent with the efficient market;

Kiymaz and Berument (2003) investigate the day of the week effect on the volatility and return of major stock markets (German, Japan, US, Canadaand United Kingdom) for the time period from 1998 to 2002. Their findings are consistent with the day of the week effect both for returns and volatility.

Patev (2003) examine the presence of the day-of-the-week effect anomaly in the Central European stock markets during the period 1997 to 2002. Their results indicated that the Czech and Romanian markets have significant negative Monday returns while the Slovenian market has significant positive Wednesday returns and has non-significant negative returns on Fridays. The Polish and Slovak markets have no day-of-the week effect anomaly.

Cabello and Ortiz (2004) investigate the day of the week and month of the yeareffect for Latin America stock markets. The paper supports the existence of calendar anomalies. They find the lowest and negative returns on Mondays and high returns on Fridays.

Hui (2005) examines the day of the week effect at Asian-Pacific markets during the period of Asian financial crisis and also tests the presence of weekend effect in developed stock markets of US and Japan. The paper supports no evidence of the day of the week effect in capital markets for the recent years, in both Asian Pacific and US capital markets.

3. Data and Model description3.1 Data

Period ROMANIABET 10

FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

Downtrend 1/10/2001-28/3/2003

1/10/2001-28/3/2003

1/10/2001-28/3/2003

1/10/2001-28/3/2003

1/10/2001-28/3/2003

1/10/2001-28/3/2003

(390) (390) (390) (390) (390) (390)

Uptrend 31/3/2003-20/6/2008

31/3/2003-20/6/2008

31/3/2003-20/6/2008

31/3/2003-20/6/2008

31/3/2003-20/6/2008

31/3/2003-20/6/2008

(1365) (1365) (1365) (1365) (1365) (1365)

The data set used in this study consists of six European Index values obtained from Bloomberg.For econometric reasons, for working days that the stock markets did not open and of course the indices did not change, the value of the previous day has been used.

The returns used in each of the time series are computed as follows:

day workingprevious in theindex theof valuethe:

index theof valuethe:

returnday the:

ln

1

1

t

t

t

t

tt

P

P

R

P

PR

Notes: Numbers in parentheses depict observations used in each period

0

5000

10000

15000

20000

25000

30000

35000

01:10 02:01 02:04 02:07 02:10 03:01

BETCACDAX

FTSEMADRIDMIBTEL

DOWNTREND PERIOD

0

10000

20000

30000

40000

50000

2003 2004 2005 2006 2007

BETCACDAX

FTSEMADRIDMIBTEL

UPTREND PERIOD

0

10000

20000

30000

40000

50000

2002 2003 2004 2005 2006 2007

BETCACDAX

FTSEMADRIDMIBTEL

WHOLE PERIOD

3.3 Model description

tR represents returns on a selected index

tttt FandHTM ,, are the dummy variables for Monday, Tuesday, Thursday, and Friday at time t

is a measure of the risk premium


12

t the GARCH term

12

t the ARCH term

The first GARCH-M (1, 1) model investigate the day of the week effect in stock return and it consists of the following two equations:

12

112

102

110

ttt

ttttFtHtTtMt RFHTMcR





12

t the GARCH term

12

t the ARCH term

In both models the Wednesday dummy variable is excluded to avoid the dummy variable trap

The mean equation allows for an autoregression of order 1 in the mean of returns since most of the returns data exhibit a small but significant first order autocorrelation

0 the mean





12

t the GARCH term

12

t the ARCH term

The second GARCH-M (1, 1) model investigate the day of the week effect in both stock return and volatility and it consists of the following two equations:




12

t the GARCH term

12

t the ARCH term

The quasi-maximum likelihood estimation (QMLE) method introduced by Bollerslev and Wooldridge (1992) is used to estimate parameters

The mean equation allows for an autoregression of order 1 in the mean of returns since most of the returns data exhibit a small but significant first order autocorrelation

12

112

102

110

tttFtHtTtMt

ttttFtHtTtMt

FHTM

RFHTMcR

0 the mean 0 the mean

Notes: p values are reported in brackets; ** denotes significance at the 1% level of significance

• The return series are nonsymmetric and leptokurtic compared to the normal distribution

• According to Augmented Dickey - Fuller test all return series are stationary

Whole period (Returns)

ROMANIABET 10

FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

Mean 0.001142 6.77E-05 0.000250 -4.66E-05 0.000311 3.15E-05

Median 0.000115 0.000102 0.000576 0.000000 0.000481 0.000309

Maximum 0.145016 0.070023 0.075527 0.068219 0.067222 0.064038

Minimum -0.119056 -0.070774 -0.074335 -0.059332 -0.078393 -0.053131

Std. Dev. 0.016653 0.013626 0.015120 0.011948 0.012338 0.011510

Skewness 0.144307 -0.008740 -0.036558 -0.102100 -0.038203 -0.082341

Kurtosis 10.33795 6.783804 6.536587 6.345886 6.627896 5.730291

Jarque-Bera 3941.297 1046.369 914.4765 821.2117 962.3224 546.7811

Observations 1754 1754 1754 1754 1754 1754

ADF (returns) -39.11451**[0]

-43.93095**[0]

-44.38366**[0]

-27.51158**[0]

-44.40952**[0]

-44.00585**[0]

4. Empirical results

4.1 Testing the series

Down trend(Returns)

ROMANIABET 10

FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

Mean 0.001547 -0.000982 -0.001337 -0.000911 -0.000454 -0.000608

Median 0.000000 -0.001053 -0.001204 -0.001179 0.000000 -0.000648

Maximum 0.145016 0.070023 0.075527 0.068219 0.056942 0.064038

Minimum -0.044481 -0.060448 -0.063360 -0.059332 -0.052006 -0.050102

Std. Dev. 0.016518 0.021481 0.024335 0.017495 0.018810 0.017979

Skewness 1.901538 0.250631 0.196448 0.144311 0.300625 0.217018

Kurtosis 17.59871 3.769944 3.393660 4.263171 3.185388 3.180879

Jarque-Bera 3688.783 13.68108 5.013823 27.21225 6.416413 3.583733

Observations 389 389 389 389 389 389

ADF Test -19.18971**[0]

-19.84382**[0]

-20.90961**[0]

-21.38023**[0]

-20.41756**[0]

-20.14965**[0]







Up trend(Returns)

ROMANIABET 10

FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

Mean 0.001027 0.000367 0.000703 0.000200 0.000529 0.000214

Median 0.000182 0.000449 0.000924 0.000253 0.000765 0.000786

Maximum 0.089371 0.058335 0.057610 0.044623 0.067222 0.038619

Minimum -0.119056 -0.070774 -0.074335 -0.056277 -0.078393 -0.053131

Std. Dev. 0.016695 0.010342 0.011155 0.009805 0.009735 0.008841

Skewness -0.338816 -0.376683 -0.239517 -0.306060 -0.532929 -0.558735

Kurtosis 8.304512 6.575000 6.731557 5.784750 9.571692 5.869344

Jarque-Bera 1626.456 759.1779 805.0084 462.3666 2520.881 539.2817

Observations 1365 1365 1365 1365 1365 1365

ADF (returns) -34.09442**[0]

-40.82585**[0]

-39.29837**[0]

-42.64936**[0]

-40.17416**[0]

-40.08223**[0]

Whole period

Mean MO TU WE TH FR F-stat Prob

BET 10 0.005918 0.005890 0.005843 0.005785 0.005734 0.001340 1.0000

CAC 40 0.000432 0.000421 0.000394 0.000257 0.000227 0.004023 1.0000

DAX 30 0.001320 0.001305 0.001190 0.001116 0.001093 0.003717 1.0000

FTSE 100 -0.000137 -0.000128 -0.000228 -0.000308 -0.000385 0.007723 0.9999

MADRID 0.001653 0.001620 0.001588 0.001492 0.001568 0.001945 1.0000

MIBTEL 0.000248 0.000228 0.000150 5.13E-05 7.60E-05 0.004465 1.0000

Whole period

Std. Dev. MO TU WE TH FR Levene Prob

BET 10 0.041494 0.039676 0.038668 0.035554 0.037045 1.063576 0.3730

CAC 40 0.030611 0.029600 0.030619 0.025393 0.025475 1.640912 0.1614

DAX 30 0.034507 0.033402 0.033373 0.029319 0.030010 1.478357 0.2062

FTSE 100 0.026093 0.023406 0.024967 0.021326 0.021358 1.860181 0.1149

MADRID 0.028230 0.025554 0.027220 0.023941 0.023496 0.974811 0.4201

MIBTEL 0.026436 0.025032 0.025754 0.022853 0.022933 0.725194 0.5747

The descriptive statistics for each day of the week

The F-Stat refers to the F-Statistic of the Equality of means test.If p-value < 0.050, then the hypothesis of equal means is rejected

The L-Value refers to the Levene Value of the Equality of variance test. If p-value < 0.050, then the hypothesis of equal variances is rejected

Down Trend


BET 10 0.007724 0.007595 0.007658 0.007856 0.007857 0.000832 1.0000

CAC 40 -0.004991 -0.004796 -0.004768 -0.005616 -0.005471 0.005675 0.9999

DAX 30 -0.006612 -0.006367 -0.007044 -0.007342 -0.007490 0.000461 1.0000

FTSE 100 -0.004284 -0.004305 -0.004432 -0.005108 -0.005291 0.014669 0.9996

MADRID -0.002345 -0.002299 -0.002143 -0.002791 -0.002249 0.003187 1.0000

MIBTEL -0.003127 -0.003062 -0.003210 -0.003838 -0.003443 0.005131 0.9999

Down trend

Std. Dev. MO TU WE TH FR Levene Prob

BET 10 0.034672 0.036623 0.038090 0.033666 0.035559 0.105995 0.9804

CAC 40 0.047271 0.049090 0.051749 0.039945 0.038637 0.730156 0.5718

DAX 30 0.053515 0.053612 0.053847 0.045270 0.045831 0.934238 0.4431

FTSE 100 0.036625 0.036621 0.039259 0.030501 0.029112 0.615906 0.6514

MADRID 0.041747 0.038969 0.042352 0.036434 0.033663 1.010731 0.4017

MIBTEL 0.041197 0.040986 0.041212 0.034844 0.034272 0.668828 0.6140



Up trend


BET 10 0.005409 0.005409 0.005332 0.005201 0.005135 0.002722 1.0000

CAC 40 0.001961 0.001892 0.001850 0.001914 0.001834 0.001591 1.0000

DAX 30 0.003557 0.003469 0.003512 0.003501 0.003514 0.006821 0.9999

FTSE 100 0.001032 0.001050 0.000958 0.001046 0.000999 0.001116 1.0000

MADRID 0.002780 0.002725 0.002641 0.002700 0.002645 0.002172 1.0000

MIBTEL 0.001201 0.001156 0.001098 0.001148 0.001068 0.002043 1.0000

Up trend

Mean MO TU WE TH FR Levene Prob

BET 10 0.043267 0.040546 0.038883 0.036107 0.037495 1.114507 0.3480

CAC 40 0.023817 0.021000 0.021095 0.019209 0.020094 1.912600 0.1059

DAX 30 0.026548 0.024640 0.024378 0.022458 0.023280 0.930642 0.4461

FTSE 100 0.022193 0.017942 0.019046 0.017755 0.018433 2.442196 0.0450

MADRID 0.023016 0.020199 0.021081 0.018918 0.019658 0.532459 0.7119

MIBTEL 0.020454 0.018185 0.019297 0.018037 0.018497 0.532061 0.7122



Whole Period

Return ecuation

ROMANIABET 10

FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

Constant -0,0003 0,000706 0,000878 0,000173 0,001168 0,001476*

(0.001708) (0.000869) (0.000926) (0.000765) (0.000821) (0.000725)

Monday -0,00031 -0,000594 -0,000242 0,000356 -0,00114 -0,001503*

(0.001108) (0.000724) (0.000823) (0.000633) (0.000729) (0.000676)

Tuesday 0,001601 -0,00078 -0,000862 0,000148 -0,00106 -0,001336*

(0.001138) (0.000708) (0.000767) (0.000668) (0.000694) (0.000613)

Thursday 3,06E-05 1,58E-04 -3,39E-05 7,46E-04 -1,83E-04 -8,49E-04

(0.001089) (0.00073) (0.000787) (0.000669) (0.000677) (0.000649)

Friday 6,61E-05 1,05E-04 -2,41E-04 9,46E-04 -2,99E-04 -6,52E-04

(0.001057) (0.000709) (0.000767) (0.000639) (0.000724) (0.000625)

Return(t-1) 5,53E-02 -6,60E-02** -4,93E-02* -8,99E-02** -3,58E-02 -5,85E-02**

(0.034143) (0.023936) (0.024818) (0.024974) (0.025588) (0.02407)

Risk 0,092859 0,015239 0,028418 -0,020148 0,023416 -0,016238

(0.113662) (0.076608) (0.071814) (0.073613) (0.074867) (0.074664)

4.2 The Results of the regressions

Notes: Standard errors are reported in parentheses; ** denotes significance at the 1% level of significance

The day of the week effects in returns for whole period

VolatilityROMANIA

BET 10FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

Mean 3,01E-05** 1,93E-06** 2,07E-06* 1,46E-06** 2,14E-06** 1,15E-06*

(0.0000111) (0.000000745) (0.000000964) (0.000000524) (0.000000831) (0.000000508)

ARCH 0,177732** 0,090911** 0,089462** 0,089707** 0,097743** 0,073798**

(0.057288) (0.019176) (0.021683) (0.014662) (0.024015) (0.016404)

GARCH 0,721278** 0,897015** 0,899845** 0,899368** 0,887776** 0,916383**

(0.068387) (0.018995) (0.022146) (0.014838) (0.023285) (0.017282)

Whole Period

Ljung–Box Q

statisticsROMANIA

BET 10FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

5 2,5467 8,3985 8,2389 6,1078 3,7531 5,7284

[0.769] [0.136] [0.144] [0.296] [0.585] [0.334]

10 7,2557 12,559 11,504 6,9764 6,6237 9,4085

[0.509] [0.128] [0.175] [0.539] [0.578] [0.309]

15 24,409 17,954 17,336 12,63 19,498 12,492

[0.058] [0.265] [0.299] [0.631] [0.192] [0.642]

20 31,865 24,681 22,088 20,86 23,096 17,725

[0.045] [0.214] [0.336] [0.405] [0.284] [0.606]

25 37,99 25,995 24,727 28,881 25,632 18,442

[0.046] [0.408] [0.478] [0.269] [0.427] [0.823]

Whole Period

ARCH-LM test

ROMANIABET 10

FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

5 0,382733 1,727216 1,129006 0,69928 4,075016 0,933572

[0.860877] [0.125118] [0.342759] [0.624008] [0.00111] [0.458033]

10 0,381393 0,927862 0,649215 0,850661 2,106105 0,714608

[0.955162] [0.5062] [0.772107] [0.579599] [0.021217] [0.711419]

15 0,319659 1,106179 0,835782 1,612083 1,900103 1,063579

[0.993643] [0.344869] [0.637904] [0.063357] [0.019338] [0.386045]

20 0,280678 0,937869 0,794883 1,348491 1,953253 0,978515

[0.999309] [0.537914] [0.722357] [0.138103] [0.007002] [0.48565]

25 0,764475 1,090896 0,796243 1,160234 1,746724 1,119203

[0.790675] [0.344074] [0.750802] [0.265791] [0.012622] [0.310641]

The conditional variances are always positive and are not explosive in our samples. According to the Ljung–Box Q statistics we can not reject the null hypothesis that the residuals are not autocorrelated. The ARCH-LM test does not indicate the presence of a significant ARCH effect in any of the sampled markets except MADRID.

Whole period

Return ecuation

ROMANIABET 10

FRANCECAC 40

GERMANY

DAX 30UK

FTSE 100SPAIN

MADRIDITALY

MIBTEL

Constant -0,00078 0,000767 0,000794 0,000224 0,001116 0,001341

(0.001663) (0.000862) (0.000962) (0.000761) (0.00084) (0.000714)

Monday -0,00034 -0,000613 -0,00025 0,00036 -0,00119 -0,00151*

(0.001088) (0.000721) (0.000843) (0.000632) (0.000745) (0.000683)

Tuesday 0,00139 -0,000804 -0,0009 0,000141 -0,00109 -0,00131*

(0.001079) (0.000709) (0.000786) (0.000665) (0.000712) (0.000617)

Thursday 2,38E-05 2,15E-04 -3,07E-05 7,93E-04 -2,94E-04 -8,24E-04

(0.00109) (0.00073) (0.000795) (0.000667) (0.000681) (0.000647)

Friday 2,48E-04 1,85E-04 -1,75E-04 9,84E-04 -2,73E-04 -6,65E-04

(0.001025) (0.000708) (0.000776) (0.000638) (0.000721) (0.000623)

Return(t-1) 6,06E-02 -6,63E-02** -5,00E-02*-8,89E-

02** -3,62E-02 -6,06E-02**

(0.034595) (0.023934) (0.024656) (0.024993) (0.025303) (0.023909)

Risk 0,122603 0,009604 0,034378 -0,025356 0,031732 -0,00411

(0.116125) (0.075484) (0.073363) (0.072933) (0.074814) (0.076502)

The day of the week effects in returns and volatilities for whole period

* Statistically significant at the 5% level.** Statistically significant at the 1% level.

Whole Period

VolatilityROMANIA

BET 10FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

Mean -9,87E-06 4,92E-06 1,32E-05 -2,95E-07 1,13E-05 1,42E-06

(0.0000562) (0.0000109) (0.0000141) (0.00000802) (0.000011) (0.0000076)

ARCH 0,210745** 0,093075** 0,087229** 0,089355** 0,094191** 0,072936**

(0.059474) (0.019896) (0.021338) (0.014525) (0.023343) (0.01512)

GARCH 0,662341** 0,896094** 0,901471** 0,900508** 0,892052** 0,915054**

(0.076557) (0.01932) (0.021912) (0.014675) (0.022591) (0.016479)

Monday 7,72E-05 -1,42E-05 -1,48E-05 -4,13E-06 -1,35E-05 1,25E-05

(0.000058) (0.0000147) (0.0000198) (0.0000111) (0.0000157) (0.0000125)

Tuesday 1,00E-04 -9,59E-08 -1,69E-05 8,89E-06 -1,42E-05 -1,51E-05

(0.0000969) (0.0000215) (0.0000284) (0.0000153) (0.0000217) (0.0000153)

Thursday 3,89E-05 9,30E-07 -1,73E-05 3,18E-06 -2,06E-05 5,68E-06

(0.0000778) (0.0000159) (0.0000201) (0.0000127) (0.0000159) (0.0000123)

Friday 2,48E-05 -2,07E-06 -6,38E-06 5,01E-07 2,03E-06 -3,61E-06

(0.0000577) (0.0000153) (0.0000182) (0.0000114) (0.0000158) (0.0000124)

The conditional variances are always positive and are not explosive in our samples.

Whole Period

Q statROMANIA

BET 10FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

5 3,0236 8,5064 8,7964 6,0607 4,0458 5,5976

[0.696] [0.13] [0.117] [0.3] [0.543] [0.347]

10 8,2793 12,759 12,084 7,024 6,8198 9,1098

[0.407] [0.12] [0.148] [0.534] [0.556] [0.333]

15 25,043 18,238 18,178 12,551 20,135 12,019

[0.049] [0.25] [0.253] [0.637] [0.167] [0.678]

20 31,952 25,255 22,881 20,821 23,846 17,078

[0.044] [0.192] [0.295] [0.408] [0.249] [0.648]

25 38,21 26,578 25,594 28,619 26,714 17,848

[0.044] [0.377] [0.429] [0.28] [0.37] [0.849]

The Ljung–Box Q statistics for the normalized residuals at 5-, 10-, 15-, 20-, and 25-day lags

None of these coefficients are statistically significant. Therefore, we cannot reject the null hypothesis that the residuals are not autocorrelated.

Whole Period

ARCH-LM test

ROMANIABET 10

FRANCECAC 40

GERMANYDAX 30

UK FTSE 100

SPAINMADRID

ITALYMIBTEL

5 0,420936 1,680777 1,201455 0,692022 4,505728 0,980701

[0.834402] [0.135968] [0.306014] [0.629501] [0.000439] [0.428102]

10 0,601501 0,88257 0,691385 0,911325 2,343972 0,759711

[0.813712] [0.548923] [0.73335] [0.521666] [0.009571] [0.668039]

15 0,505872 1,074607 0,877044 1,590531 2,057816 1,222953

[0.938819] [0.375135] [0.590322] [0.068863] [0.009606] [0.246415]

20 0,474225 0,920121 0,822288 1,341716 2,094818 1,113194

[0.976242] [0.561059] [0.688144] [0.141982] [0.003109] [0.327823]

25 0,799504 1,062295 0,804766 1,148274 1,863541 1,209985

[0.746558] [0.379769] [0.739655] [0.278409] [0.005968] [0.217401]

Engle’s ARCH-LM for whole period

Engle’s ARCH-LM test does not indicate the presence of a significant ARCH effect in any of the sampled markets except MADRID. This finding indicates that the standardized residual terms have constant variances and do not exhibit autocorrelation except MADRID.

The results for downtrend period

The day of the week effects in returns for downtrend period

There is no coefficient of dummy’s variables statistically significant. Thus, we don’t find the evidence for the existence of the classical day of the week effect.

The estimated coefficients for BET 10, MADRID and MIBTEL are lowest on Mondays but they are statistically insignificant. The coefficient of the conditional standard deviation of the return equation (risk) is positive for BET10 (0,347303), CAC 40 (0,115031), DAX 30 (0,058048), FTSE 100 (0,178102), MADRID (0,224529) and MIBTEL (0,218751). However, the estimated coefficients are not statistically significant.

The conditional variances are always positive and are not explosive in our samples. According to the Ljung–Box Q statistics we cannot reject the null hypothesis that the residuals are not autocorrelated. The ARCH-LM test does not indicate the presence of a significant ARCH effect in any of the sampled markets. This finding indicates that the standardized residual terms have constant variances and do not exhibit autocorrelation.

The day of the week effects in returns and volatilities for downtrend period

The estimated coefficients for dummy’s variables in volatility equation are not statistically significant except the ones from Monday and Tuesday for BET10, the one from Tuesday for DAX 40 and the one from Friday for FTSE 100 who are statistically significant.

Not only we don’t find strong evidence for the existence of the classical day of the week effect, but there is no any obvious pattern in coefficient’s significances.

The coefficients of the conditional standard deviation of the return equation (risk) are positive for all markets. However, the estimated coefficients are not statistically significant except BET10. The conditional variances are always positive and are not explosive in our samples According to the Ljung–Box Q statistics we cannot reject the null hypothesis that the residuals are not autocorrelated. The ARCH-LM test does not indicate the presence of a significant ARCH effect in any of the sampled markets

The results for uptrend period

The day of the week effects in returns for uptrend period

The estimated coefficient of the Mondays’ dummy variable for MIBTEL (-0,001503) is negative and statistically significant at the 1% level, suggesting that Mondays’ returns are smaller than those of Wednesdays. Also the estimated coefficient of the Tuesdays’ dummy variables for MIBTEL (-0,001316) is negative and statistically significant at the 1% level, suggesting that Tuesdays’ returns are smaller than those of Wednesdays. All the rest of dummy’s coefficients are not statistically significant.

The coefficient of the conditional standard deviation of the return equation (risk) is positive for CAC 40 (0,09187), DAX 30 (0,158252), MADRID (0,108172), MIBTEL (0,012795) and it is negative for BET10 (-0,08354), FTSE 100 (-0,005897), However, the estimated coefficients are not statistically significant.

There is no classical version of the day of the week effect and no substantial day effect for the developed stock markets.

The conditional variances are always positive and are not explosive in our samples. According to the Ljung–Box Q statistics we cannot reject the null hypothesis that the residuals are not autocorrelated. ARCH-LM test does not indicate the presence of a significant ARCH effect in any of the sampled markets except MADRID.

The estimated coefficients for dummy’s variables in volatility equation are not statistically significant. Thus, there is no evidence of a day of the week in volatility.

The coefficients of the conditional standard deviation of the return equation (risk) are positive for all markets except BET10 (-0,071027) and FTSE100 (-0,008442) who are negative. However, the estimated coefficients are not statistically significant.

The estimated coefficient of the Mondays’ dummy variable in the return equation for MIBTEL (-0,00143) is negative and statistically significant at the 1% level, suggesting that Mondays’ returns are smaller than those of Wednesdays. Also the estimated coefficient of the Tuesdays’ dummy variables in the return equation for MIBTEL (-0,00129) is negative and statistically significant at the 1% level, suggesting that Tuesdays’ returns are smaller than those of Wednesdays.

The conditional variances are always positive and are not explosive in our samples. According to the Ljung–Box Q statistics we cannot reject the null hypothesis that the residuals are not autocorrelated. ARCH-LM test does not indicate the presence of a significant ARCH effect in any of the sampled markets except MADRID.

The day of the week effects in returns and volatilities for uptrend period

5. The Conclusions

Finally, the conclusion of this study is that the phenomenon of the «Day of the Week Effect» seems to be weaker than it was in previous decades as a result of investor’s behavior. Investors are more mature, well educated, with more professional attitude, characteristics that help stock markets to become more efficient.

The phenomenon of the «Day of the Week Effect» seems to disappears from the developed stock markets and not to have a specific pattern in general.

Nowadays, the stock markets are more liquid than ever and seem to be more efficient that the previous decades because of the easiest capital transmission, the technological changes and the changes in the stock market microstructure. So, it is logical for investors to react more mature, something that induces less inefficient results.

1. Joshi, N.K., F.K.C. Bahadur (2006). “The Nepalese Stock Market: Efficiency and Calendar Anomalies”. Economic Review 17.2. Chiaku Chukwuogor-Ndu (2006).” Stock Market Returns Analysis, Day-of-the-Week Effect, Volatility of Returns: Evidence from European Financial Markets 1997-2004.” ISSN 1450-2887 International Research Journal of Finance and Economics3. Berument, H., & Kiymaz, H. (2001). The day of the week effect on stock market volatility. Journal of Economics and Finance, 25, 181–193.4. Halil Kiymaza, Hakan Berument “The day of the week effect on stock market volatility and volume: International evidence” Review of Financial Economics 12 (2003) 363–3805. Lyroudi, K., D. Subeniotis, G. Komisopoulos (2002). “Market Anomalies in the A.S.E.: The Day of the Week Effect”. European Financial Management Association, London Meetings.

6. Bibliography

6. Lakonishok, J., S. Smidt, (1988), “Are seasonal anomalies real? A ninety-year perspective”, Review of Financial Studies, 1, 403-25.7. Morton B. Brown and Alan B. Forsythe (1974), “Robust Tests for the Equality of Variances” Journal of the American Statistical Association, Vol. 69, No. 346, (Jun., 1974), pp. 364 -3678. Keim, B.D., R. F. Stambaugh, (1984), “A further investigation of the weekend effect in stock returns”, Journal of Finance, 39, 819-840.9. Dimitris Balios, Sophia Stavraki (2007), “Stock Market Trends, Day of the Week Effect and Investor’s Behavior after the September’s 2001 Attacks”, European Journal of Economics, Finance and Administrative Sciences ISSN 1450-2887 Issue 8 (2007)10. Green, William H. (2000), Econometric Analysis, New York University

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