Monday Effect & Stock Return Seasonality

Electronic copy available at: http://ssrn.com/abstract=1103627Electronic copy available at: http://ssrn.com/abstract=1103627Electronic copy available at: http://ssrn.com/abstract=1103627Electronic copy available at: http://ssrn.com/abstract=1103627

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Monday Effect and Stock Return Seasonality:

Further Empirical Evidence

Dr. Rengasamy Elango*

Nabila Al Macki **

ABSTRACT

This study investigates whether the anomalous ‘weekend effect’ found in many developed and

developing markets around the world is also present in the rapidly emerging Indian equity market.

We use the real-time data of three of the major indices of the National Stock Exchange of India

(NSE) for 1999-2007 period. Standardizing the data, we apply a set of descriptive and inferential

statistics on the above three indices. Our analysis produced mixed results indicating that the

Monday returns are negative and low in the case of two out of three indices. The K-W test, which is

a non-parametric test applied to examine whether the ranks of mean returns for each day of the week

are equal, shows evidence of a statistically significant difference in the case of one sample index,

CNX S&P Nifty Junior. The implication is that the weekend effect is present in small stocks.

Dummy variable regression, which again examines the weekend effect shows that Monday returns

are negative in one of the bench-mark indices, the NSE S&P Nifty confirming that the Indian Market

is inefficient and could be exploited to maximize returns. Surprisingly, Wednesdays have yielded the

highest mean returns across indices. However, volatility is also higher in these stocks. These

findings offer interesting opportunities for individual investors and portfolio managers to place

bid/ask orders in order to maximize their returns. However, due caution needs to be exercised while

making the above decisions.

KEY WORDS: Monday Effect, Market Anomalies, Seasonality, K-W-Test, Dummy Variable

Regression, Mann-Whitney Test

*Dr. Rengasamy Elango is Faculty, Department of Business Management, Majan College (University College), Sultanate of Oman. He can be contacted at <[email protected]> **Nabila Al Macki is Head of Faculty, Department of Business Management, Majan College (University College), Sultanate of Oman. She can be contacted at <[email protected]>

Electronic copy available at: http://ssrn.com/abstract=1103627Electronic copy available at: http://ssrn.com/abstract=1103627Electronic copy available at: http://ssrn.com/abstract=1103627Electronic copy available at: http://ssrn.com/abstract=1103627

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I INTRODUCTION

The efficient market theory states that an informationally efficient market is one where the

market price is an unbiased estimate of the true value of the investment. It further states that the

current market price of a security fully reflects all available information and the current price is the

fair price as the security has traded in that price (Fama, 1969). In the words of Fama, “the

informational efficiency of financial markets requires that the market prices and rates of return at any

given time reflect all the information available to the participants” (Fama, 1965)

Academics and practitioners have documented many research works on the seasonality and

associated behavior of securities markets. Among others, the most widely mentioned seasonal effects

and market anomalies are January effect, Monday effect or Week-end effect, Holiday effect and

Small firm-effect, to mention a few. Among these, one of the widely discussed anomalies is the

negative average Monday stock return. Kenneth French (1980) in his paper titled, ‘Stock returns

and the week-end effect’ analyzed whether daily stock returns were generated by a trading or

calendar time hypothesis. He provided convincing evidence of unusually lower returns on Mondays

compared to the other trading days during the week. Afterwards, a few other studies have also

confirmed the negative returns on Mondays using different time periods, stocks and indices. This

anomaly not only exists in the more developed US markets but in other developing markets as well.

Research studies in developed markets such as USA, UK & Canada and emerging markets such as

Malaysia and Hongkong have also shown evidence of negative Monday returns. However, markets

such as Japan, France, Australia and Singapore show negative returns on Tuesdays. Following the

findings of Fama, researchers have made attempts to rationalize as to why the Mondays register

negative returns. Literature has documented two important theories on the above phenomenon. A

brief discussion of the same is presented below.

i Calendar Time Hypothesis.

This theory states that the return generating process is a continuous activity meaning that

Monday’s mean return would be different than the other days’ mean returns. The rationale is that

Monday’s mean return is estimated from the closing price on Friday until the closing price on

Monday which has a time span of three days. So, Monday’s mean return will be three times higher

than the mean returns of the other week days.

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ii. Trading Time Hypothesis

This hypothesis states that returns of stock are generated as a result of a transaction meaning

that the average returns of shares will be the same for all the week days as each day’s return

represents one day’s investment.

A few more arguments have also been advocated in an attempt to justify negative mean

returns on Mondays.

The rest of the paper is organized as follows. Section II reviews the literature pertaining to

the day-of-the-week effect and attempts to rationalize the Monday effect as quoted in the literature.

Section III presents an overview of the Indian bourses and the National Stock Exchange of India.

Section IV explains the data and methodology of the study. Section V examines the Monday effect

on the three sample indices drawn from the National Stock Exchange of India. Applying different

statistical parameters, it discusses the implications and the trading rule that an individual investor

and fund managers might apply in stock-picking. The paper is concluded in section VI.

II LITERATURE REVIEW

To put it in simple terms, the findings on Monday effect is that the mean returns for Monday

have been significantly lower than the returns for the other days of the week. Starting from Fama

(1965) this finding has been well-documented by the researchers. However, a close scrutiny of the

literature on Monday or Week-end-effect reveals that receiving lower returns does not happen only

on Mondays. Lower returns have been registered even on Tuesdays as well. Another interesting

dimension to the week-end effect is that a few studies have shown evidence of positive returns on

Mondays while quite a few studies do not support day-of-the week effect theory. This section is

organized into four sub-sections. A brief review of the previous studies on Monday effect or Week-

end-effect has been presented in the following paragraphs.

i. Monday Returns low and negative

Flannery and Protopadakis (1988) have found anomalous Monday return across different

types of securities which include common stocks as well. Studies conducted across advanced

markets such as USA, UK and Canada have concluded that Monday’s mean returns are negative and

Friday’s are positive. See, for example, (Cross, 1973; Gibbons & Hess, 1981; Keim & Stambaugh,

1984; Theobald and Price; 1984; Jaffe and Westerfield, 1985; Harris, 1986, Smirlock & Starts, 1986;

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Board and Suctliffe, 1988; Cohers & Cohers, 1995, Tang and Kwok, 1997) for six indices, Dow

Jones Industrial Average Index US), Financial Times Index (UK), Nikkei Average Index (Japan),

Hangseng Index (Hongkong), FA2 General Index (Germany) and All Oridnary Index (Australia).

Another interesting finding related to the Monday returns, according to Ko Wang, etal (1997), is that

Monday effect occurs primarily in the last two weeks of the month.

ii. Monday Returns Positive

The literature has documented quite a few studies showing evidence of positive returns on

Mondays. For example, Glenn (2003) supports that Mondays yield positive returns.

iii. Tuesday Returns Negative

Contrary to this, a few other studies have observed negative average returns on Tuesdays.

Studies by Condoyanni, O’Hanlon & Ward (1987), Solmik & Bousqet (1990) on the French stock

market, Athanassakos & Robinson (1994) in the Canadian Market, Jaffe & Westerfield (1985) in

Australian and Japanese markets, Kim (1988) in Korean and Japanese markets; Aggarwal & Rivoli

(1989) on Hongkong, Singapore, Malaysia and Philippines, Ho (1990) in the stock markets of

Australia, Hongkong, Japan, Korea, Singapore, Malaysia, Philiphines, Taiwan and Thailand have

observed negative returns on Tuesdays. Studies conducted by Wong, Hui and Chan (1992) Duboi &

Louvet (1996), Aggarwal and Tandon (1994), Aydoon (1994), Balaban (1995), Bildik (1997) and

Ozmen (1997) also support negative returns on Tuesdays.

iv. There is no day-of-the-week effect:

It is of considerable interest to note that a few studies have not shown any week-end-effect or

Monday effect. Studies conducted in Spanish markets by Santemases (1986), Pena (1995) and

Gardeazabal and Rogulaz (2002) revealed no day of the week effect.

Based on the above survey, it could be concluded that Mondays and Tuesdays are the usual

days on which day of the week effect is noticed. Even these results differ from market to market and

from period to period. For instance, while the evidence of Monday effect is consistent in advanced

markets, developing and underdeveloped markets have yielded mixed results. Adding to this

phenomenon is the fact that the less developed markets have a very few studies on the seasonal

anomalies. Yet another aspect to be noticed here is that the dissemination of information is not very

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free due to the lack of strict disclosure requirements and a few other factors such as investment

restrictions, stock-holding restrictions etc., found in the less developed markets. This phenomenon

too has contributed to the above results.

Why does the day-of-the-week effect occur?

Many explanations are found in the literature reasoning out the cause of ‘Monday effect’.

Studies conducted by Lakonishok and Maberly (1990) state that investors tend to increase their

‘trading activities, (particularly, sell orders) on Mondays. So, heavy sell orders might trigger bearish

trend in the market causing negative returns, it is stated. A study by Kamara (1995) shows that

individual trading is an important cause of the Monday effect. Sias and Starks (1995) document that

day of the week patterns in returns and volumes are more explained in securities in which

institutional investors participate very actively. Damodaran (1989) reports that firms in general

report bad news on Fridays. It is stated that reporting and delay in reporting of bad news might

cause the negative Monday effect.

III AN OVERVIEW OF NATIONAL STOCK EXCHANGE OF INDIA

i. Indian bourses

India, after USA hosts the largest number of listed companies in the world. Of late, global

investors are bee-lining to India as it has gained reputation as a preferred destination for investments.

This has gained further momentum after the liberalization of the Indian economy in the year 1991.

Indian stock exchanges adopt the most sophisticated technology in stock trading which includes

online trading as well. Indian stock markets are on the bullish run which could be attributed to the

steady growth of around 6% to 8% in Gross Domestic Product (GDP), the growth of Indian

companies in tune with MNC’s, the huge potential for growth in the fields of telecommunications,

manufacturing, media, education, tourism and the sectors backed by the continued economic

reforms.

BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) are the two most

important stock exchanges in India.

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ii. National Stock Exchange of India (NSE).

NSE is the second premier stock exchange in India in terms of popularity. During the year

2005-2006, NSE reported a turnover of Rs.15,69,556 crores ($40,245 cr) in the equities segment

(Source: www.nseindia.com dated 12.01.2008). NSE uses the most sophisticated electronic trading

systems, provide better mechanism for trade and post-trade executions. NSE uses the state-of-the-

art satellite technology to enable participation of investors from around 320 cities in India. It can also

handle 6 million trades per day. NSE also has the best surveillance mechanism in India meaning that

market manipulation by unscrupulous parties is minimal.

IV DATA AND METHODOLOGY

The present study considers the daily indices as reported by the NSE. The data comprise

daily closing prices of the National Stock Exchange (NSE) from 1.1.1999 to 31.12.2007 covering a

period of about nine years. As stated, the study makes an attempt to examine the presence of day-of-

the-effect, if any, in the India’s premier stock exchange. To achieve the same, selection of the

indices was based on certain logical considerations. These three indices, S&P CNX Nifty, S&P

CNX Defty, and CNX Nifty Junior, out of the two important categories of large and small indices,

form part of the large indices category. S&P CNX Nifty comprises the largest highly liquid fifty

blue-chip stocks in India representing twenty four sectors of the economy. It represents 58% of the

traded value of all the stocks in NSE. It also represents 60% of the market capitalization as on 31st

March, 2005. (Source: www.nseindia.com dated 12.01.2008) . It is also the benchmark index for

many other important purposes such as Index Funds and Stock Index- based derivatives

computations. The S&P Nifty Junior has in it, the next fifty large, liquid growth stocks in India.

These two indices make up the 100 most liquid stocks in India. The third index, S&P CNX Defty is

considered as the performance indicator for foreign institutional investors, off-shore funds etc., Defty

Index is S&P CNX Nifty computed in dollar terms enabling the foreign investors to calculate their

equity returns in dollar terms. So, Defty index would fluctuate if dollar witnesses a see-saw

movements due to various exposures. Also, out of the seven indices, only for these three indices,

data were available for a period of nine years. So, we are convinced that these three sample indices

mostly capture the behaviors of the equity market segment in India. The required data were

http://www.nseindia.com/


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downloaded from the NSE website (www.nseindia.com). The indices included for the study along

with the period covered are given in Table 1.

Table 1

Sample Indices and Period of Study

Sl.

No

Index Period No.of

Observations

1 S&P CNX NIFTY 1.1.1999 TO 31.12.2007

2244

2 S&P CNX DEFTY 1.1.1999 TO 31.12.2007

2242

3 CNX NIFTY JUNIOR

1.1.1999 TO 31.12.2007

2244

Indian stock exchanges, like the leading western markets open on Mondays and close on

Fridays. However, trading had taken place on a few Saturdays and Sundays as well but they were

not included for the purpose of the present study. In other words, except for the returns generated on

Monday, any returns that are preceded by a holiday were excluded for the purpose of the current

study. Also, we have not noticed any structural breaks in the data.

i. Computation of daily returns.

The daily returns on the NSE index were computed using the first differences of the

logarithmic price index. Excel and SPSS (Version 12) were used in data analysis. This approach of

logarithmic transformation of time series data was first suggested by Osborne (1959). The lognormal

returns follow the Normal distribution more closely than arithmetic returns. (See, Lauterbach and

Ungar (1995)).

tR = ( )[ ] 100*/ln 1−tt PP (1)

Where, Rt is the daily return from the index P is the price index, t and 1−t represent the current and

immediate preceding days.

ii. Computation of return seasonality

After the computation of daily returns normality test was applied on the returns. Kurtosis,

which forms part of the descriptive statistics gave an idea that the distributions were non-normal as

in all the indices, the value was above 3 indicating peakedness (leptokurtic) of the distribution

(Friedrich, 2003). Consequently, Shapiro-Wilk test for normality confirmed that the distributions

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were asymmetric in the case of all the three indices. With the result, K-W test, which is a non-

parametric test applied to examine whether or not the ranks of the rates of return in each day of the

week are equal is applied. The Kruskal-Wallis statistic is defined as:

(((( ))))[[[[ ]]]]

(((( )))) (((( ))))(((( ))))

�� ++++−−−−

++++++++

++++====

∑∑∑∑N

n

R.......

n

R

NNH

k

k (2)

Where,

∑ R1…∑ Rk are sums of the samples 1,2…k, n1, n2 …. nk are sizes of samples 1,2…k. N is the

combined number of observations for all samples. Equation 2 is distributed as a Chi-Square with (k-

1) four degrees of freedom. The null hypothesis in this case is that the distribution of stock returns

for all days of the week is equal. The Null Hypothesis, Ho will be rejected if the computed value of

the test statistic; H is more than the critical value at a chosen level of significance. In addition to the

above test, Friedman Anova, was also applied to the daily returns obtained from the sample indices.

This is also a non-parametric equivalent of One-Way Anova which identifies skewness or

seasonality in the distribution. According to Alford and Goffey (1996), Friedman test is not sensitive

to any possible inter-year heterogeneity in stock returns. So, this test has also been applied.

iii. Computation of Day-of-the-week effect

In order to investigate the day of the week effect, the following regression equation is used.

Rt = β1 D1(Mon) + β2 D2(Tue) + β3 D3(Wed) + β4 D4(Thu) + β5 D5(Fri) + εt (3)

Where,

Rt = Index return on day t;

D1(Mon) = dummy variable equal to 1 if t is a Monday and 0 otherwise,

D2(Tue) = dummy variable equal to 1 if t is a Tuesday and 0 otherwise,

D3(Wed) = dummy variable equal to 1 if t is a Wednesday and 0 otherwise,

D4(Thu) = dummy variable equal to 1 if t is a Thursday and 0 otherwise,

D5(Fri) = dummy variable equal to 1 if t is a Friday and 0 otherwise,

έi,t = error term

The intercept, β1 …….. β5, represent the average deviation of each day from the Monday return. Thus,

if the daily returns are equal, one expects the dummy variable coefficients to be statistically close to

zero. So, the coefficients of the regression are the mean returns obtained from Monday to Friday

applying ordinary least square (OLS). Ultimately, if NSE indices exhibit a traditional Monday

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effect, its estimated co-efficients would be either a) lesser than the returns of the other days of the

week, or b) negative, which may or may not be c) statistically significant. With the result, it would,

hopefully, be possible to identify a specific trading rule in order to gain abnormal returns,

particularly from the Indian markets. Again, if proved, it would be a suitable strategy to determine

the day on which shares could be bought or sold.

iv. Wilcoxon Mann-Whitney pair-wise test to capture Monday effect

Since the main focus of the study is to examine the Monday effect, Wilcoxon-Mann-Whitney

pair-wise test has also been applied. This test examines if the average Monday return is different and

statistically significant from returns generated from each of the remaining four days of the week,

based on ranking differences in pair-wise observations

V RESULTS AND DISCUSSION

i. Daily Return Pattern and Volatility

In Table 2, we present the results of the summary statistics of day-to-day mean stock returns

and standard deviation for all the four indices.

Table 2

Day-To-Day Mean Return(%) and S.D for the Stock Indices

Day

Value

S&P

CNXMF

S&P CNX

DEFTY

CNX

NIFTY

JR

Monday

Mean S.D Obs

-0.0041 1.776 451

0.00722 1.848 451

0.011967 2.151 451

Tuesday

Mean S.D Obs

0.0468 1.460 445

0.0397 1.4948

444

0.03910 1.8044

445

Wednesday

Mean S.D Obs

0.2482 1.4569

449

0.2406 1.4926

448

0.33484 1.76139

449

Thursday

Mean S.D Obs

0.0909 1.507 457

0.1048 1.5676

457

0.0647 1.7714

457

Friday

Mean S.D Obs

0.0415 1.6178

442

0.0461 1.654 05

442

-0.00915 1.9975

442

All Days

Mean S.D Obs

0.0848 1.5693 2244

0.0878 1.6179 2242

0.0885 1.905 2244

Obs. indicates the number of observations

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From the mean value of the stock returns, we can learn that the returns seem to be lower on

Mondays than other trading days of the week. The evidence is very clear in the case of S&P CNX

NIFTY (mean -0.0041) and S&P CNX DEFTY (0.00722) indices. These negative and lower mean

returns are consistent with the findings of French (1980), Gibbons and Hess (1981), Lakonishok and

Levi (1982), Smirlock and Starks (1986), Lakonishok and Smidt (1988), Ko Wang, Li and Erickson

(1997) Mehdian and Perry (2004) documented in the literature. This is a clear violation of the

Efficient Market Hypothesis (EMH) in its weak form which states that share price movement cannot

be predicted in advance to form a trading strategy. One possible explanation that could be attributed

to this phenomenon is that India is a developing economy and its markets are expected to be

inefficient. However, Monday effect is found in US markets too. Interestingly, CNX Nifty Junior

index has registered the highest negative return on Fridays. This is in contrast with the findings of

the previous studies documented on week-end-effect. Another interesting finding of the current

study is that Wednesdays have yielded the highest average returns across indices. So, based on the

above findings, we could confidently conclude that the best time to buy a scrip would be

Monday(Buy low) and selling might take place on a Wednesday (Sell high) as it yields the highest

mean return.

ii. Results for Equality of daily returns

As stated in the methodology, K-W test and Friedman rank sum test have also been applied

on the returns of the three indices. The K_W test uses the rankings of the mean returns series that

the daily mean returns from the indices are equal. The K-W test suggests the presence of

seasonalities in the case of CNX Nifty Junior index thus confirming the same with the results of the

descriptive statistics discussed earlier. So, Ho is rejected in the case of one index, i.e., CNX Nifty

Junior. All other indices do not indicate any seasonality effect in returns.

Table 3

Test Results for equality of Daily Returns

Index K-W Test Friedman Rank sums

S&P CNX NIFTY Chi- square 4.359 (0.360)

Chi-square 7.117 (0.130)

S&P CNX DEFTY Chi- square 4.151 (0.386)

Chi-square 4.150 (0.386)

CNX NIFTY JUNIOR

Chi-square 8.500 (0.075)*

Chi-square 5.878 (0.208)

P-Values are in parenthesis *indicates significance at 10% level

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Both K-W and Friedman tests are used to examine the skewness or seasonality in the returns

and they cannot be applied to capture the Monday effect, if any. Consequently, we have applied

Wilcoxon-Mann-Whitney pair-wise test. This test is applied to examine if the mean Monday return

is statistically different from each of the other four days. The results are presented in Table 4.

Table 4

Results of Wilcoxon Mann-Whitney Test

Rtn. Pair

Mean Rank

S&PCNX

NIFTY

S&P CNX

DEFTY

CNX

NIFTY JR

Monday Tuesday Prob.

451.17 445.79 (0.756)

452.47 443.45 (0.602)

461.20 435.63 (0.139)


437.52 463.54 (0.133)

439.32 460.75 (0.216)

441.62 459.42 (0.304)


450.71 458.24 (0.666)

450.31 458.64 (0.632)

463.55 445.57 (0.302)


447.40 446.59 (0.963)

448.87 445.10

(–0.218)

456.29 437.52 (0.277)

P values are in parenthesis

The results of the pair-wise comparison reveal that there is no significant difference between

the mean Monday return and returns gained on other days of the week.

iii. Results of Monday effect

Table 5 reports the results of the dummy variable regression to daily index returns. The co-

efficient β1 is the measure of mean Monday returns while the other coefficients, β2 to β5 represent the

differences between mean returns from Tuesday to Friday. When we look at the results index-wise,

the following findings emerge. The coefficient of Monday for S&P CNX Nifty is the lowest

(coefficient= -0.004) compared to all other days of the week. CNX Nifty index is one of the

benchmark indices in India and the present results goad us to conclude that there is Monday effect in

one of the largest stock exchanges, i.e NSE, in India. However, the p-value is not statistically

significant.

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Table 5

Monday Effect

Results of Dummy Variable Regression Analysis

Day

Parameter S&P CNX

NIFTY

S&P CNX DEFTY

CNX NIFTY JR

β1 Mon

(Intercept)

Coef Std.Err t-stat Prob

-0.004 0.074 -0.056 0.955

0.007 0.076 0.095 0.927

0.063 0.045 1.412 0.158

β 2 Tue


0.051 0.105 0.486 0.627

0.033 0.108 0 .301 0.764

-0.037 0.081 -0.463 0.643

β 3 Wed


0.252 0.105 2.414

0.016**

0.233 0.108 2.164

0.031**

0.258 0.081 3.203

0.001***

β 4 Thu


0.095 0.104 0.913 0.361

0.098 0.107 0.909 0.363

-0.012 0.080 -0.144 0.886

β 5 Fri


0.046 0.105 0.435 0.664

0.039 0.108 0.360 0.719

-0.086 0.081 -1.059 0.290

F - Stat 1.733 1.464 2.781

**Significant at 5% level ***Significant at 1% level

It could also be noticed that the coefficient on Friday has registered the next lower return

after Monday. This confirms that Monday and Friday returns are lower compared to all other days

of the week. These results are in tune with the results obtained from the descriptive statistics

discussed earlier. The next index S&P CNX Defty also reveals a lower Monday return. However,

CNX Nifty Junior presents a different outcome. This index has posted negative returns on Tuesday,

Thursday and Friday. Nifty Junior index comprises the next 50 most liquid stocks after S&P CNX

Nifty. These shares are not in great demand like the Nifty stocks. So, this could be a reason as to

why this index reveals results that are inconsistent with the findings documented in the literature.

Another very important and an interesting finding is that Wednesdays have yielded the highest return

across the three indices analyzed. The ‘t’ statistics in the case of Wednesday for all the three indices

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are statistically significant @ 5% level. It is less than 1% level of significance in the case of CNX

Nifty Junior.

So, based on the above analytical results, we can confidently arrive at the following trading

rule with regard to this market. “Monday is the best day to buy (buy low) and Wednesday is the best

day to sell (sell high)”. So, the results indicate the presence of Monday effect in one of the leading

indices of the Indian bourses.

VI SUMMARY AND CONCLUSION

The Monday effect or Week-end effect has been well-documented in different global

markets. This study provides an additional piece of evidence to this anomaly from one of the

leading stock markets in India, the National Stock Exchange. We consider the three most important

indices of this market during the period from 1.1.1999 to 31.12.2007. We analyze the daily return of

the above indices applying different statistical parameters. Our analysis has turned mixed results.

Our findings question the efficient market hypothesis which states that investors cannot make

abnormal profits using the historical prices. Specifically, our results indicate lower returns on

Mondays and Fridays. Surprisingly, Wednesdays have yielded the maximum returns across indices.

So, the specific trading rule that could be conceived of is that one could consider buying the scrips

on Mondays (buy low) and selling them on Wednesdays (sell high). However, this strategy needs to

be exercised with caution. We further suggest that investors could experiment the above strategy, to

start with, on small stocks and extend the same on blue-chips based on the risks and rewards. This

gains further momentum as Indian markets are more transparent and open to the global institutional

investors and fund managers seeking profitable trade opportunities.

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Monday Effect & Stock Return Seasonality

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