Journal of Modern Accounting and Auditing, ISSN 1548-6583
October 2011, Vol. 7, No. 10, 1116-1121
Daily Patterns in Stock Returns: Evidence From the
New Zealand Stock Market
Li Bin, Liu Benjamin
Griffith University, Australia
In this paper, we study day-of-the-week effects in stock returns across different industry sectors in the New Zealand
market. Unlike other studies on this market, we examine weekday seasonality using daily stock return data of four
market indexes and 16 industry sectors for the period from October 1, 1997 to April 16, 2009. We do not find
significant Monday anomalies of the market index, large capitalization stock index and all industry sectors except
for the property sector. Our finding is inconsistent with the literature on the New Zealand stock market. However,
we find that the mid and small capitalization stocks have significant negative returns on Mondays than on other
weekdays, which is consistent with the previous studies in some other markets.
Keywords: New Zealand stock market, market efficiency, market anomaly, day-of-the-week effect
Introduction
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Seasonal or calendar anomalies in equity markets have attracted a widespread attention and considerable interests among practitioners and academics alike. Over the last hundred years, a large number of literature from both the practitioner and academic fields have documented day-of-the-week effects or day seasonality on returns of various assets, such as stocks, debt securities, futures, foreign currencies and even commodities. The earliest research can be traced back to the late 1920s (Pettengill, 2003). There are more than two hundred published papers on this topic from different perspectives by 2010 proving the existence of day-of-the-week return behavior.
Calendar anomalies, relying on the assumption that a certain pattern of stock markets is formed on the basis of the past stock price, can be used to predict future stock price. If the pattern is fixed, informed investors
can utilize this pattern to earn a risk-free profit by trading the stocks. The study of seasonality implies that investors could employ the findings on anomalies to predict the future behavior of prices (Fama, 1965).
Certainly, seasonal anomalies are in contradiction to any form of efficient market hypothesis (EMH), pa
rticularly the weak-form efficiency. In an efficient market, stock prices should follow a random pattern and no one can consistently earn excess returns by trading the stocks.
However, during the last four decades, there is mounting evidence on seasonality of stock markets around the world. For example, Cross (1973), French (1980), Keim and Stambaugh (1984), and Cho, Linton, and Whang (2007) all reported significantly negative average returns in the US stock markets on Mondays. Similar
anomalies are found in international equity markets as well, such as Jaffe and Westerfield (1985) and Tong
Li Bin, Ph.D., lecturer, Department of Accounting, Finance and Economics, Griffith Business School, Griffith University.
Liu Benjamin, Ph.D., lecturer, Department of Accounting, Finance and Economics, Griffith Business School, Griffith University.
DAILY PATTERNS IN STOCK RETURNS 1117
(2000). In contrast to the above findings, some recent literature, however, reported the declining or rev
ersing weekend effects and a different shift of the weekend effect on large-capitalization securities and small capitalization securities (e.g., Connolly, 1989; Chang, Pinegar, & Ravichandran, 1993; Kamara, 1997; Doyle & Chen, 2007; Worthington, 2010).
Although New Zealand is a small open economy as a member of Organization for Economic Co-operation and Development [OECD] and has a well established stock market, research on seasonality is very limited. To the best of our knowledge, we find only two studies on this topic: Keef and McGuinness (2001) and Hasan and Raj (2001). Keef and McGuinness (2001) examined whether the settlement practice impacts day-of-the-week return behavior as New Zealand Stock Exchange experienced six amendments to its settlement during the period 1989-1996. They, using NZSE Gross Index for this period, found that there is a strong Monday effect across all six settlement periods.
To further examine this issue, unlike the existing studies, we investigate day-of-the-week return behavior of four market indexes and 16 industry sector indexes of the New Zealand Exchange (NZX) over the period from October 1, 1997 to April 16, 2009. Since stock returns of industry sectors may have different return patterns from those of major indexes as found in Marrett and Worthington (2008) and Liu and Li (2010) in the Australian stock market, we are motivated to conduct a further investigation into day-of-the-week effects in the New Zealand stock market.
The rest of the paper is structured as follows: Section 2 offers a description of the data and summary statistics; section 3 describes empirical approaches and discusses empirical findings; and section 4 concludes this paper.
Data
The data employed in this study are daily closing prices for market indexes and industry indexes of the NZX over the period from October 1, 1997 to April 16, 2009. The prices are adjusted by dividend distribution, new equity issuance and share buyback. The data are sourced from DataStream. We study both market indexes and industry indexes. We consider four kinds of market indexes: NZX All Companies Index, NZX Top 10 Index, NZX Mid Cap Index, and NZX Small Cap Index. There are 16 industry sectors in our study: Agriculture & Fishing, Building, Consumer, Energy, Financial and Other Services, Food, Forestry, Goods, Intermediary & Durable, Mining, Primary, Property, Ports, Services, Transport, and Textiles and Apparel 1. The detailed description of DataStream codes and their corresponding names can be found in Table 1.
The daily market return at day t is calculated as:
,,,1ln(/)i t i t i t R P P −= (1)
where ,i t P is the price of stock i at day t .
Table 1 presents summary statistics of the daily returns. The sample means, standard deviations, medians, minimums, maximums, skewness, kurtosis, Jacaque-Bera statistics, and the first-order autocorrelation coefficients are reported. The median returns for most indexes are close to zero. Consistent with Marret and Worthington (2008), we find that the return distributions for most indexes are non-normal. All Jarque-Bera statistics for normality test are significant at the 1% level, suggesting the rejection of the null hypothesis.
1 There are another three industry sectors in DataStream: Investment, Leisure & Tourism, and Media & Communication. However, they are not included in the study as their stock price data discontinue on July 20, 2001 in the database.
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DAILY PATTERNS IN STOCK RETURNS
1118Furthermore, the kurtosis for all return series except for the building sector is significantly larger than 3, suggesting fat-tail distributions. Finally, the first-order autocorrelation coefficients for all the indexes companies are less than 0.1.
Table 1
Summary Statistics
Data stream code Name Mean (× 100)Std. Dev.(× 100) Median (× 100)Min (× 100)Max (× 100)Skewness Kurtosis Jarque- Bera
ρ (1)Market index
NZSEALL NZX all companies index 0.000 0.749 0.007 -5.07 5.33 -0.50 4.61 2,7930.04NZ10CAP NZX top 10 index -0.017 1.112 0.000 -15.45 10.63 -0.73 16.81 35,718-0.02NZMCAPC NZX mid cap index -0.002 0.732 0.007 -8.64 6.21 -1.06 15.04 28,9370.05NZSMCIC NZX small cap index 0.005 0.582 0.019 -8.22 6.10 -1.99 27.92 99,7910.07Industry index
NZSEAGR Agriculture & fishing -0.015 1.118 0.000 -11.43 8.69 -0.63 9.10 10,5830.07NZSEBLD Building 0.037 1.482 0.000 -8.54 8.29 -0.05 2.76 9580.02NZSECON Consumer 0.012 1.041 0.000 -17.17 8.79 -1.66 30.75 120,0060.03NZSEENG Energy 0.030 0.891 0.000 -5.97 6.27 -0.05 5.02 3,1590.05NZSEFIN Financial & other services 0.020 0.995 0.019 -12.67 9.88 -0.91 18.19 41,9110.06NZSEFOO Food 0.041 1.559 0.000 -15.77 13.33 0.06 13.07 21,420-0.06NZSEFOR Forestr
y -0.034 2.182 0.000 -22.32 22.32 0.46 17.47 38,408-0.05NZSEGOO Goods 0.014 1.057 0.000 -11.08 4.93 -0.68 7.42 7,1390.07NZSEINT Intermediary & durable 0.013 1.214 0.000 -14.00 6.08 -0.58 8.58 9,4050.08NZSEMIN Mining 0.028 2.250 0.000 -13.80 11.14 -0.11 3.17 1,268-0.04NZSEPRM Primary -0.009 1.484 0.000 -19.71 12.52 -0.29 16.28 33,2910.01NZSEPRP Property -0.012 0.672 0.000 -5.78 5.23 -0.15 6.75 5,728-0.02NZSEPRT Ports 0.009 1.228 0.000 -11.82 11.90 0.20 12.96 21,1000.01NZSESRV Services -0.014 1.021 0.000 -11.53 8.69 -0.64 10.34 13,6240.01NZSETRN Transport -0.038 1.716 0.000 -17.76 10.06 -0.98 13.41 23,0580.06NZSETXT Textiles & apparel -0.028 1.666 0.000 -18.29 15.60 -0.98 16.42 34,3180.08Notes. All Jarque-Bera statistics for normality are significant at the 1% level. The samples are daily and start from October 1, 1997 and end on April 16, 2009.
Empirical Approaches and Results
We use t-tests to test the day-of-the-week hypothesis. Following Liu and Li (2010), and Marret and Worthington (2008), we investigate the day-of-the-week effect on the basis of a trading day hypothesis whereby returns are calculated on trading days during the week. To be specific, we calculate mean return on each weekday (Monday to Friday), and mean return on other four weekdays. Then we calculate the difference of mean returns and use t-tests to test the statistical significance of test return.
For example, to test the Monday
effect, the t-statistic is calculated as follows:
R R t −= (2) where Mon R is the mean return on Monday, NonMon R is the mean return on the weekdays other than Monday,
2Mon S is the variance of Monday returns, 2
NonMon S is the variance of Non-Mondays returns, and Mon n and NonMon n are
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DAILY PATTERNS IN STOCK RETURNS 1119 the observation numbers of Monday returns and Non-Mondays returns, respectively.
Before conducting the t-tests, we present the mean returns of 20 indexes on each weekday (from Monday to Friday) and their associated standard errors of mean as shown in Table 2. We also report th
e largest return day and the lowest return day for each company in the rightmost two columns of Table 2. Consistent with the US studies and New Zealand research, which found returns on Mondays are much lower than on the other four weekdays, Table 2 shows that three (NZ10CAP, NZMCAP and NZSMCIC) out of four market indexes and three (NZSEPRP, NZSESRV and NZSETRN) out of 16 sectors indexes have significant negative returns on Monday in the New Zealand market while three sectors (NZSEINT, NZSEGOO and NZSEFIN) have significant positive returns. In addition, all company index (NZSEALL) and two sectors (NZSEAGR and NZSEGOO) on Tuesday and one sector (NZSEFOR) on Thursday have significant negative returns. The negative returns of indexes are concentrated on Tuesday. The lowest returns occur in the sector of NZEFOR (-0.176%) on Thursday while largest returns is in the sector of NZSEMIN (0.140%) on Wednesday.
Table 2
Mean Returns on Weekdays
ASX code Monday Tuesday Wednesday Thursday Friday LRD SRD MR SEM MR SEM MR SEM MR SEM MR SEM Market index
NZSEALL 0.028 (0.031) -0.057*(0.031)0.033 (0.031)0.005 (0.030)-0.010 (0.030) Wed Tue
NZ10CAP -0.080*(0.043) 0.005 (0.051)0.011 (0.046)-0.014 (0.043)-0.008 (0.044) Mon Mon
NZMCAPC -0.104**(0.030) 0.033 (0.033)0.033 (0.030)-0.007 (0.028)0.034 (0.028) Fri Mon
NZSMCIC -0.060** (0.026) -0.033 (0.027)0.057**(0.022)0.009 (0.022)0.051** (0.022) Wed Mon Rights Reserved.
Industry index
NZSEAGR 0.007 (0.048) -0.079* (0.046)0.077*(0.046)-0.017 (0.041)-0.062 (0.046) Wed Tue
NZSEBLD 0.069 (0.063) -0.048 (0.052)0.067 (0.062)0.035 (0.064)0.059 (0.061) Mon Tue
NZSECON -0.002 (0.051) -0.016 (0.040)0.029 (0.040)0.055 (0.042)-0.004 (0.039) Thu Tue
NZSEENG 0.058 (0.037) -0.042 (0.035)0.040 (0.039)0.057 (0.036)0.036 (0.034) Mon Tue
NZSEFIN 0.076* (0.040) -0.050 (0.046)0.024 (0.040)0.023 (0.037)0.025 (0.039) Mon Tue
NZSEFOO 0.062 (0.065) -0.015 (0.068)-0.011 (0.060)0.066 (0.066)0.104* (0.058) Fri Tue
NZSEFOR 0.051 (0.090) 0.033 (0.085)-0.079 (0.084)-0.176*(0.090)0.001 (0.095) Mon Thu
NZSEGOO 0.084** (0.041) -0.079*(0.045)0.050 (0.042)0.007 (0.040)0.007 (0.048) Mon Tue
NZSEINT 0.091* (0.048) -0.048 (0.049)0.053 (0.048)-0.021 (0.046)-0.012 (0.056) Mon Tue
NZSEMIN 0.045 (0.099) -0.100 (0.094)0.140 (0.091)-0.057 (0.089)0.113 (0.085) Wed Tue
NZSEPRM -0.056 (0.058) 0.016 (0.069)0.044 (0.059)-0.020 (0.055)-0.029 (0.061) Wed Mon
NZSEPRP -0.083**(0.025) 0.024 (0.027)0.038 (0.029)-0.010 (0.028)-0.029 (0.027) Wed Mon
NZSEPRT -0.078 (0.053) -0.013 (0.052)0.080 (0.050)-0.003 (0.048)0.061 (0.046) Wed Mon
NZSESRV -0.083**(0.040) 0.003 (0.045)0.022 (0.043)-0.020 (0.040)0.007 (0.040) Wed Mon
NZSETRN -0.162**(0.070) 0.082 (0.072)-0.049 (0.074)-0.074 (0.068)0.014 (0.065) Tue Mon
NZSETXT -0.102 (0.075) 0.064 (0.070)-0.018 (0.059)-0.055 (0.070)-0.031 (0.064) Tue Mon Notes. Mean returns (MR) and their associated standard errors of mean (SEM) are expressed in percentages. Mean returns which
are statistically significantly different from zero at the 5% and 10% levels are denoted with ** and *, res
pectively. LRD means largest return day, and SRD means smallest return day. The samples are daily and start from October 1, 1997 and end on April 16,
2009.
Table 3 reports the t-testing results of Equation (2) for the four market indexes and 16 sector indexes. As
DAILY PATTERNS IN STOCK RETURNS
1120
shown in Table 3, two market indexes (mid cap-NZMCAP and small cap-NZSMCIC) and only one sector (property-NZSEPRP) have significantly negative returns on Monday than on the other four weekdays. These findings are basically consistent with Keef and McGuinness (2001) on the New Zealand stock market. However, for other two indexes and all other sectors except for energy (NZSEENG) and goods (NZSEGOO) on Tuesday they have no significant negative returns across all the weekdays. There is week evidence of other-than-Monday-effect in the sample. In addition, the findings are consistent with the studies on different day-of-the-week return behavior between large capitalization and small capitalization stocks (e.g., Connolly, 1989; Kamara, 1997)
Table 3
Test of Mean Difference
ASX code Monday-Non
Monday Tuesday-Non
Tuesday
Wednesday-Non
Wednesday
Thursday-Non
Thursday
Friday-Non Friday
MD SE MD SE
MD SE MD SE MD SE Market index
NZSEALL 0.035 (0.043) -0.071 (0.043) 0.042 (0.044) 0.006 (0.043) -0.012 (0.043)
NZ10CAP -0.078 (0.063) 0.027 (0.067) 0.035 (0.064) 0.004 (0.063) 0.012 (0.063)
NZMCAPC -0.127**(0.042) 0.044 (0.044) 0.044 (0.042) -0.006 (0.041) 0.045 (0.041)
NZSMCIC -0.081** (0.035) -0.048 (0.035) 0.065**(0.033) 0.005 (0.033) 0.058* (0.033)
Industry index
NZSEAGR 0.027 (0.066) -0.080 (0.064) 0.115* (0.065) -0.003 (0.062) -0.059 (0.065)
NZSEBLD 0.041
(0.087)
-0.105 (0.081) 0.038 (0.086) -0.002 (0.087) 0.028 (0.086) NZSECON -0.018 (0.065) -0.035 (0.059) 0.021 (0.059) 0.053 (0.060) -0.020 (0.058)
NZSEENG 0.036
(0.052)
-0.089*(0.051) 0.012 (0.053) 0.034 (0.051) 0.007 (0.050) NZSEFIN 0.070 (0.057) -0.087 (0.061) 0.005 (0.057) 0.005 (0.056) 0.007 (0.056)
NZSEFOO 0.026 (0.091) -0.070 (0.092) -0.065 (0.088) 0.031 (0.091) 0.079 (0.087)
NZSEFOR 0.106 (0.126) 0.084 (0.124) -0.056 (0.123) -0.177 (0.126) 0.043 (0.129)
NZSEGOO 0.088
(0.060)
-0.116*(0.062) 0.045 (0.060) -0.009 (0.059) -0.009 (0.064) NZSEINT 0.098 (0.069) -0.076 (0.069) 0.050 (0.069) -0.042 (0.068) -0.030 (0.074)
NZSEMIN 0.021 (0.134) -0.159 (0.131) 0.139 (0.129) -0.107 (0.128) 0.106 (0.126)
NZSEPRM -0.059 (0.084) 0.031 (0.090) 0.067 (0.085) -0.014 (0.083) -0.025 (0.086)
NZSEPRP -0.088**(0.037) 0.045 (0.039) 0.062 (0.040) 0.002 (0.039) -0.021 (0.039)
NZSEPRT -0.109 (0.072) -0.028 (0.072) 0.088 (0.071) -0.015 (0.070) 0.064 (0.069)
documented evidenceNZSESRV -0.086 (0.058) 0.021 (0.061) 0.045 (0.059) -0.007 (0.058) 0.027 (0.058)
NZSETRN -0.155 (0.099) 0.150 (0.100) -0.015 (0.101) -0.045 (0.098) 0.065 (0.096)
NZSETXT -0.093 (0.100) 0.116 (0.097) 0.014 (0.092) -0.033 (0.097) -0.003 (0.094) Notes. Mean differences (MD) and their associated standard errors (SE) are expressed in percentages. Mean differences which are statistically significantly different from zero at the 5% and 10% levels are denoted with ** and *, respectively. The samples are daily and start from October 1, 1997 and end on April 16, 2009.
Moreover, the magnitude of the difference across the weekdays is large for mid capitalization index, the sectors of goods and agricultural and fishing. The mid capitalization index on Monday and the sector goods on Tuesday have a return of -0.127% and -0.116% per day respectively compared to those on other weekdays that would represent a return difference of a 32.00% and 29.23% on an annualized basis. The largest positive returns occur in the sectors of energy and mining with a 0.115% and 0.139% on Wednesday or a 35.03% and
28.98% per annum.
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