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Lecture 4 – Capital Markets, Investment and Finance
Market Efficiency - Anomalies
• Impossible to consistently beat the market!
• Cannot 'predict' prices from past prices.
• Anomalies, seasonalities, regularities, calendar effect
(i) Weekend effect - lower returns between Friday's close and Monday's close.
(ii) January effect - returns much higher during January.
(iii) Trading month effect - mean returns positive only for days in first half of trading month.
(iv) Many others - 'Friday 13' - data miningThe Essay is provided by UK Assignment http://www.ukassignment.org
Implications for market efficiency and asset pricing model:
• Mainly US, UK, Spain, Japan, Australia, etc
Imperfections in trading process; regularities of more general concern.
• Require support for different data sets, over different time periods;
'conflicting' evidence!!
Need inter-country comparisons
Little work done for UK data
Not interested in explanations - only their existence
Authentic, Identifiable, Material, Persistent
Mills & Coutts (1995)
• October1992 FT-SE Mid 250, FT-SE 350, FT-SE 100
• Associated industry baskets - range of indices
broad industrial base - no 'thin' trading
• 01/86 - 16/10/92 - 1719 observations - events for index P1
daily return = 100 log (Pt/Pt-1)
• returns not adjusted for dividends - implications?!
Methodology
Vector of returns P = (P1,.....,PT)
Matrix of dummy variables for anomaly = X
Vector of coefficients (means) = β
Deg model p = X β + u u = (u1 ,.....,uT)
Much evidence to suggest that returns are not normally distributed and display serial correlation and heteroskedasticity.
Strong mixing allows for both the above but still allows the OLS estimator of β,
β = (X1X)-1 X1 p to be consistent and asymptotically normally distributed.
Statistical EvidenceThe Essay is provided by UK Assignment http://www.ukassignment.org
Sample means, sample distributions and t-stats.
No Friday 13 effect (if anything higher returns!)
Weekend Effect
• Table 1 - means returns significant negative for 250 and 350 on Monday
• Consistent with Board and Sutcliffe (1988) but note that effect had died out by mid 80's.
• Mean returns positive for all days being largest on Wednesday and Friday - variance largest for Monday for all indices.
• Bank Holiday return positive but insignificant from zero
• Incorporation of S.E. Account Effects.
Account begins on Monday for two (3) weeks.
Payment for shares bought during account is not due until second Monday following end of account.
If purchase delayed from last Friday of account to following Monday, buyer has an extra 11 days of interest free credit between purchase and payment.
M T W T F
e
e
M T W T F
M T W T F
e e
M T W T F
M T W T F
Buy
pay
pay
Buy
Buy The Essay is provided by UK Assignment http://www.ukassignment.org
Implications of S.E account:
• Above average return on Monday not first day of account
• If share purchase delayed from Friday to following Monday not first day of account - period of interest free credit reduced lower than average weekend return.
• Dividends usually paid on Mondays - excluded expected on average negative Monday return - shares 'ex dividend'
• Sometimes first day of account not a Monday and do not follow a bank holiday - delaying purchase gives six additional days of free credit therefore higher return.
Splitting Monday into account and non-account days leads to these results:
• Non account Mondays have significant negative returns for all in
• Account Monday positive returns for 100 but zero for 250 account days not Mondays large positive mean
• Monday effect is in part a settlement effect; similar to Condanyanni et al (1987) for US data.
• High positive returns on account Mondays indicate settlement effect is dominant factor and may mask strength of the underlying negative weekend effect.
When does negative return occur - 'close to close' data, need 'real time' data.
• Yadav and Pope (1992) using hourly data on the FT - SE 100 suggest that 'abnormal' Monday returns accrue during trading on the Monday, not over weekend.
• Tentaviely conclude that weekend effect is the 'norm' for LSE - supports Kim (1988) for FT OSI - also concluded that 'timing the trade' eradicated by 'round trip' costs as low as 0.5% cf Board and Sutcliffe (1988).
Seasonality
Table 2 - indices split into calendar months
• Mean daily returns signify positive for January and February thus 'turn of year' effect confirmed, all indices. More marked for 250 - 'firm size' explanation.
• For tax-loss selling hypothesis (returns higher in first month of tax year) returns for small firms should be signify positive in April - this is not the case.
• Returns for summer/autumn months small or negative gives support for 'sell in May and go away'
• No information on liquidity constraints or timing of cash into market, etc.
Trading Month EffectThe Essay is provided by UK Assignment http://www.ukassignment.org
• Ariel (1987): returns only positive at beginning and during first half of month.
• Cadsby & Radner (1982) confirmed this for UK data, conflict with Jaffe and Westerfield (1989).
• Table 3 repeats Ariel's methodology and confirms existence for all 3 indices.
• For 350 cumulative returns over first half of months
59.6% compared with -0.6% for second half of months!!!
• Similar cumulative returns for other two indices
Holiday Effect
• Ariel (1990): trading day prior to holidays on average has higher returns.
• Suggest one-third of return earned by market over 20 years accrued on eight trading days which annually fall before holidays.
• Table reports similar results for indices, mean returns on pre-holidays approximately seven times size of mean returns on other days.
• In contrast Cadsby & Ratner (1992) for FT-SE 500 four pre-holiday effect insignificant.
Conclusions
• Broadly support similar evidence for various countries for the 'January', 'weekend', 'half of month' and 'holiday' effects.
• Due to dividend effects - tentative conclusions!
• Explanations trading volume data, timing of information release.
• Fundamental question "How can this information be translated into improved portfolio performance?"
• Even if persistent in timing and magnitude 'trading rules' may be prohibitive.
• 'round trip' transactions costs render 'market timing' strategy unprofitable - consistent with notion of market efficiency.
• No strategy available which consistently yields abnormal returns!!
• Documented 'regularities' may only have practice value for investors planning to trade; committed agents therefore only incur cost of 'timing the trade'.
Authentic Identifiable Material Persistent
√ √ X √
Table 1
Descriptive Statistics Concerning the
Weekend Effect in the FT-SE IndicesThe Essay is provided by UK Assignment http://www.ukassignment.org
100
Daily
Return Mean St. Dev. t-stat observations
All days 0.034 1.059 1.33 1719
Monday -0.092 1.222 -1.36 322
Tuesday 0.031 1.169 0.50 350
Wednesday 0.073 0.973 1.41 350
Thursday 0.038 0.939 0.76 352
Friday 0.111 0.965 2.13 345
Bank Holidays 0.212 1.681 2.13 39
Monday
(account day) 0.124 1.186 1.32 158
Monday
(non-account) -0.287 1.238 -2.97 164
Account day
not Monday 1.138 0.286 8.89 5
Mid 250
Daily
Return Mean St. Dev. t-stat observations
All days 0.031 0.938 0.75 1719
Monday -0.136 1.201 -2.30 322
Tuesday 0.029 0.958 0.86 350
Wednesday 0.095 0.791 3.77 350
Thursday 0.067 0.838 1.61 352
Friday 0.087 0.856 2.45 345
Bank Holidays 0.052 0.831 0.50 39
Monday
(account day) -0.021 1.354 -0.20 158
Monday
(non-account) -0.233 1.040 -3.23 164
Account day
not Monday 0.691 0.349 4.95 5
350
DailyThe Essay is provided by UK Assignment http://www.ukassignment.org
Return Mean St. Dev. t-stat observations
All days 0.034 1.013 0.96 1719
January 0.171 0.885 1.79 149
February 0.151 0.783 1.58 141
March 0.082 0.831 1.08 149
April 0.068 0.966 0.74 141
May 0.100 0.813 1.29 142
June 0.040 0.668 0.56 150
July 0.025 0.787 0.36 155
August -0.100 0.905 -1.08 147
September 0.015 0.951 0.14 150
October -0.247 1.930 -1.20 147
November 0.002 1.117 0.02 127
December 0.124 0.896 1.57 121
350
Daily
Return Mean St. Dev. t-stat observations
All days 0.034 1.013 0.96 1719
Monday -0.148 1.204 -2.33 322
Tuesday 0.052 1.083 1.09 350
Wednesday 0.103 0.892 3.16 350
Thursday 0.062 0.943 1.17 352
Friday 0.088 0.910 2.19 345
Bank Holidays 0.093 0.955 0.78 39
Monday
(account day) 0.043 1.220 0.42 158
Monday
(non-account) -0.318 1.176 -3.55 164
Account day
not Monday 1.072 0.181 14.82 5
The Essay is provided by UK Assignment http://www.ukassignment.org
Table 2
Descriptive Statistics Concerning Seasonality
in the FT-SE Indices
100
Daily
Return Mean St. Dev. t-stat observations
All days 0.034 1.059 1.33 1719
January 0.159 0.934 2.08 149The Essay is provided by UK Assignment http://www.ukassignment.org
February 0.136 0.851 1.89 141
March 0.087 0.876 1.21 149
April 0.048 0.972 0.58 141
May 0.122 0.867 1.68 142
June 0.034 0.745 0.55 150
July 0.022 0.831 0.33 155
August -0.095 0.918 -1.25 147
September 0.031 0.985 0.39 150
October -0.266 2.019 -1.60 147
November 0.022 1.156 0.21 127
December 0.130 0.912 1.57 121
Mid 250
Daily
Return Mean St. Dev. t-stat observations
All days 0.031 0.938 0.75 1719
January 0.190 0.766 1.99 149
February 0.196 0.690 1.89 141
March 0.092 0.703 1.17 149
April 0.077 0.913 0.77 141
May 0.098 0.643 1.43 142
June 0.039 0.545 0.48 150
July 0.012 0.636 0.13 155
August -0.149 0.900 -1.41 147
September 0.022 0.881 0.20 150
October -0.259 1.863 -1.37 147
November -0.032 1.094 -0.53 127
December 0.092 0.846 0.91 121
Table 3
Descriptive Statistics Concerning
a Monthly Effect in the FT-SE Indices
First
half of month
Daily
Returns Mean St. Dev. t-stat observations
100 0.071 0.945 2.18 843
Mid 250 0.064 0.858 2.08 843
350 0.071 0.908 2.45 843
The Essay is provided by UK Assignment http://www.ukassignment.org
Second
half of month
Daily
Returns Mean St. Dev. t-stat observations
100 -0.001 1.173 -0.03 876
Mid 250 -0.002 1.009 -0.05 876
350 -0.001 1.104 -0.02 876
Table 4
Descriptive Statistics Concerning a
Pre-Holiday Effect in the FT-SE Indices
Pre-Holiday
returns Mean St. Dev. t-stat observations
100 0.205 0.910 1.41 39
Mid 250 0.195 0.630 1.95 39
350 0.225 0.810 1.62 39
Other day
returns Mean St. Dev. t-stat observations
100 0.030 1.062 1.06 1680
Mid 250 0.027 0.944 0.69 1680
350 0.030 1.017 0.86 1680
Table 5
Industry Baskets and Categories
Finance
Banks Merchant Banks
Insurance (Brokers) Property
Insurance (Composite) Other Financial
Investment (Life) Investment Funds
Consumer
Brewers & Distillers Hotels & Leisure
Food Manufacture Packaging, Paper etc
Food Retailing Stores
Health & Household Textiles
Industrial
Building Materials Electronics
Contracting, Construction Metals
Electricals Motors
Other Industrials
OthersThe Essay is provided by UK Assignment http://www.ukassignment.org
Chemicals Oil & Gas
Conglomerates Telephone Networks
Miscellaneous Transport
Arsad and Coutts (1995)
• FT-30, broad industrial base, constituents frequently traded - no 'thin trading'
• 01/07/35 - 31/12/94 - 14888 observations excluding holidays
• Cover majority of economic events in the 20th century
• Rt = log (Pt / Pt-1) No dividends!!
Weekend Effect
• Not only test for whole sample, but also for 12 sub-samples of five years - persistent effect
• Employ t-stats, Kruskall-Wallis H stats and F-stats; stock prices non-normal and leptokustic (fat-tailed)
• Conventional methodology
Rt = a1 D1t + a2 Dzt + a3 D3t + a4 D4 + a5 D5 + єt
Rt = return on FT-30
D1t = dummy variable, 1 if day1 = Monday, 0 = otherwise
• OLS coefficients a1 to a2 are mean returns for Monday to Friday respectively.
єt = stochastic term
• H : a1 = a2 = .... = a5
• If null rejected then the stock returns most exhibit some form of day-of-the-week seasonality.
• Table 1: for entire sample period and 12 sub-samples, mean return for Monday is negative.
• For whole sample and six (50%0 of sub-samples, there is a significant weekend effect.
• For entire sample and 7 sub-samples, largest variance occurs on Mondays,- but only 'slightly' larger.
• Conclude - FT-30 displayed a weekend effect 1935-94 but this was not persistent.
• Have not incorporated stock exchange account effects.
January EffectThe Essay is provided by UK Assignment http://www.ukassignment.org
• Table 2 provides stats for entire sample period:
Rt = β1 M1t t,...., + β2 M2t + β5 M3t .... + β12 M12t + єt
Hu = β1 = β2 = β3 = β4,....., = β12
• At the 1% level of significance we see that the mean daily returns are significantly positive in January, April and December.
• Further evidence is offered by the significant F-stat.
• Table 3 decomposes the entire sample to 12 sub-samples
• April is only month which displays positive mean daily returns for all the sub-samples.
• Of these only 4 are significantly positive: 1940-44, 1945-4, 1950-54 and 1955-59
• These sub-samples occur before individual investors became eligible to pay C.G.T on their portfolios.
• Similarly, January has four significantly positive mean returns; all following the introduction of C.G.T in 1965
As for the 'tax-loss' selling hypothesis, the mean daily returns are higher in January than April - this refutes this hypothesis.
• This may be due to the 'information hypothesis': many important informational events occur in January release of potentially important information!
• High positive returns in January lend support to the 'Jan' or 'turn-of-year' effect, whilst high positive returns in April lens support to the 'turn-of-tax' year effect.
• Tables 2 and 3 shows mean returns for summer/autumn months May-October are negative/small, this supports the old market adage of 'sell in May and go away'.
The Holiday Effect
• French (1980) suggests that expected returns will be low following a holiday.
• Mean Return following holiday < mean return for non-holiday for each day of the week.
• If negative mean returns for Monday due to a weekend effect, the opposite should hold.
• Monday, Wednesday, Thursday and Friday returns will be higher than normal because of the additional positive return for the holiday itself.
• Similarly return on a Tuesday following a holiday is expected to be lower due to expected negative return over weekend prior to holiday on Monday.
• Ariel supports this for US, Kim and Park (1994) for US, UK and Japan. Cadsby and Ratner found effect was insignificant for UK - challenged by Mills & Coutts (1995).
• Table 4 reports that for the FT-30 data mean returns on pre-holidays for Monday, Wednesday, Thursday and Friday are 5-9 times the size of mean returns for non-holidays.
• This would lend support to the weekend effect over the 'closed market' effect.
• Table 5 reports that returns immediately before and after holidays are much higher than non-holiday returns.
• Table 5 demonstrates that returns are overwhelmingly high on the day immediately prior to holidays.
ConclusionsThe Essay is provided by UK Assignment http://www.ukassignment.org
• 'weekend', 'January' and 'holiday' effects appear to be present in our date set - to some extent.
• No dividends, no stock Exchange account effects.
• Still cannot bear the market
Table 1 (cont'd)
1960-64 Mean Std. Dev t-stat F-stat K-W stat Observations
Monday 0.140 0.817 -2.75*** 3.76*** 21.73*** 242
Tuesday -0.060 0.833 -1.17 (0.005) (0.000) 241
Wednesday 0.082 0.872 1.66** 255
Thursday 0.090 0.743 1.83* 258
Friday -0.014 0.682 -0.28 254
1965-69 Mean Std. Dev t-stat F-stat K-W stat Observations
Monday -0.094 0.914 -1.47 1.84 13.91*** 241
Tuesday 0.025 1.220 0.39 (0.118) (0.008) 243
Wednesday 0.122 1.070 1.84* 256
Thursday 0.064 0.869 1.05 257
Friday -0.055 0.778 -0.89 252
1970-74 Mean Std. Dev t-stat F-stat K-W stat Observations
Monday -0.327 1.405 -3.55*** 4.94** 16.20*** 238
Tuesday 0.197 1.499 2.14** (0.001) (0.0003) 237
Wednesday -0.090 1.375 -1.10 255
Thursday -0.178 1.417 -1.99** 257
Friday 0.058 1.466 0.64 254
1975-79 Mean Std. Dev t-stat F-stat K-W stat Observations
Monday 0.262 1.834 -2.45** 4.64** 23.95*** 235
Tuesday 0.305 1.718 2.84*** (0.001) (0.000) 234
Wednesday 0.095 1.524 0.91 255
Thursday -0.029 1.611 -0.28 257
Friday 0.256 1.583 2.46** 252
1980-84 Mean Std. Dev t-stat F-stat K-W stat Observations
Monday -0.019 1.150 -0.25 1.09 6.08 235
Tuesday 0.076 1.251 0.99 (0.361) (0.194) 234
Wednesday 0.151 1.202 2.07** 255The Essay is provided by UK Assignment http://www.ukassignment.org
Thursday 0.006 1.174 0.09 257
Friday 0.143 1.043 1.95** 252
1985-89 Mean Std. Dev t-stat F-stat K-W-stat Observations
Monday -0.235 1.291 -3.14*** 5.62*** 21.63*** 233
Tuesday 0.086 1.264 1.15 (0.000) (0.000) 236
Wednesday 0.184 0.998 2.58*** 256
Thursday 0.025 1.123 0.35 256
Friday 0.196 1.022 2.71*** 250
1990-94 Mean Std. Dev t-stat F-stat k-W stat Observations
Monday -0.054 1.013 0.80 0.66 13.91*** 193
Tuesday 0.023 0.878 0.33 (0.620) (0.008) 191
Wednesday 0.018 0.823 0.27 208
Thursday 0.097 0.998 1.50 210
Friday 0.028 0.967 0.42 206
*** and ** denote statistical significance at the 1%, 5% and 10% level respectively, for a two tailed test.
Table 2: Summary Statistics for the January Effect in the FT 30 Index
Month Mean Std. Dev t-stat Skewness Kurtosis Observations
January 0.104 1.041 3.60*** 1.175 11.503 1305
February 0.012 1.033 0.40 0.305 11.679 1170
March 0.008 0.997 0.30 -0.575 7.216 1285
April 0.0135 0.935 4.49*** 0.750 5.902 1244
May -0.040 0.945 -1.36 -0.242 5.63 1282
June -0.051 0.964 -1.76* -0.155 3.094 1244
July -0.012 0.888 -0.44 0.269 6.599 1308
August 0.072 0.958 2.48** -0.124 6.180 1305
September -0.050 1.043 -1.75* 0.287 10.476 1265
October 0.009 1.186 0.31 -1.685 19.653 1306
November 0.008 1.070 0.29 -0.153 7.341 1264
December 0.071 0.961 2.39** -0.057 26.486 1308
F-stat 4.30*** K-W stat 51.81***
***, ** and * denote statistical significance at the 1%, 5% and 10% level respectively.
Table 3: Summary Statistics for the January Effect for the Twelve Sub-Samples
1935-39 Mean Std. Dev t-stat F/K-W stat Observations
January 0.058 0.699 -0.61 F-stat = 1.33 87
February 0.068 0.725 0.73 (0.203) 80
March -.0.200 1.170 -2.26 91
April 0.021 0.697 0.22 K-W = 22.21 85
May -0.076 0.702 -0.82 (0.024) 87
June -0.024 0.628 -0.26 88
July 0.073 0.468 0.92 110
August 0.077 0.703 -0.94 111
September -0.174 1.431 -2.15 108
October 0.121 0.877 1.53 109
November -0.044 0.708 -0.55 108
December 0.020 0.508 0.24 111
1940-44 Mean Std. Dev t-stat F/K-W stat Observations
January 0.057 0.367 1.15 F-stat=3.98*** 110
February -0.025 0.427 -0.49 (0.000) 102
March 0.040 0.264 -0.82 110
April -0.003 0.299 0.05 K-W=41.19*** 108
May -0.081 0.644 -1.65* (0.000) 110
June 0.111 1.094 -2.22** 107
July 0.026 0.607 5.37*** 112The Essay is provided by UK Assignment http://www.ukassignment.org
August 0.053 0.428 1.04 109
September 0.045 0.309 0.92 108
October 0.063 0.341 1.31 111
November 0.118 0.399 2.39** 106
December -0.002 0.327 -0.05 112
1945-49 Mean Std. Dev t-stat F/K-W stat Observations
January -0.019 0.402 -0.27 F-stat=3.55*** 112
February -0.163 0.746 -0.29***1 (0.000) 100
March -0.006 0.428 -0.12 110
April 0.143 0.366 2.54** K-W=36.63 108
May -0.020 0.459 -0.36 (0.000) 111
June -0.131 0.505 -2.36** 106
July -0.169 0.739 -3.18*** 111
August 0.027 0.571 0.47 111
September 0.012 .0.780 0.22 107
October 0.034 0.656 0.64 111
November 0.086 0.466 1.58 107
December 0.134 0.290 2.31 111
1950-54 Mean Std. Dev t-stat F/K-W stat Observations
January 0.021 0.358 0.47 F-stat=2.69 111
February 0.040 0.474 0.85 (0.002) 101
March -0.026 0.483 -0.58 111
April 0.019 0.541 3.95** K-W=22.21 107
May 0.008 0.450 0.17 (0.017) 110
June 0.034 0.458 0.74 108
July 0.018 0.521 0.41 111
August 0.192 0.400 4.13** 110
September 0.031 0.451 0.68 107
October 0.050 0.438 1.13 111
November -0.066 0.503 -1.46 107
December 0.007 0.519 0.15 111
Table 3 (Contd)
1955-59 Mean Std. Dev. t-stat F/K-W stat Observations
January -0.044 0.631 -0.58 F-stat=1.63* 111
February -0.116 0.905 -1.46 (0.086) 101
March 0.069 0.835 0.89 109
April 0.161 0.876 2.01** K-W=18.77*** 108
May 0.117 0.565 1.49 (0.067) 111
June 0.081 0.754 1.04 106
July 0.070 0.591 0.92 112
August 0.040 0.857 0.51 110
September -0.081 0.859 -1.05 107
October 0.087 1.046 1.15 112
November 0.008 0.805 0.10 106
December 0.241 0.692 2.98*** 111
1960-64 Mean Std. Dev. t-stat F/K-W stat Observations
January -0.007 0.814 -0.89 F-stat=1.53 112
February 0.079 0.559 1.01 (0.116) 101
March 0.036 0.527 0.47 111
April 0.103 0.725 1.28 K-W=18.03* 106The Essay is provided by UK Assignment http://www.ukassignment.org
May -0.129 1.070 -1.70 (0.083) 112
June -0.104 0.934 -1.31 107
July -0.062 0.741 -0.82 110
August 0.206 0.816 2.63 112
September -0.072 0.512 -0.93 106
October 0.004 0.942 0.06 111
November -0.014 0.912 -0.18 108
December -0.030 0.713 -0.37 109
1965-69 Mean Std. Dev. t-stat F/K-W stat Observations
January 0.202 0.896 2.09** F-stat=2.01** 110
February -0.214 0.683 2.20** (0.020) 101
March 0.150 0.713 1.58 111
April 0.106 0.891 1.06 K-W=29.17 107
May -0.094 0.958 -0.99 (0.002) 111
June -0.031 0.962 -0.32 107
July -0.240 0.850 -2.58*** 110
August 0.073 0.885 0.76 111
September 0.091 1.339 0.95 108
October 0.019 1.345 0.20 110
November 0.018 1.060 0.19 107
December 0.090 0.764 0.91 111
1975-79 Mean Std. Dev. t-stat F/K-W stat Observations
January 0.558 2.233 3.39*** F-stat=1.99*** 111
February 0.241 2.086 1.45 (0.027) 100
March 0.077 1.829 0.48 112
April 0.258 1.666 1.53 K-W=25.13*** 106
May -0.046 1.666 -0.28 (0.009) 111
June -0.244 1.446 -1.51 108
July -0.087 1.493 -0.55 109
August 0.264 1.451 1.61 112
September -0.019 1.341 -0.12 107
October -0.218 1.493 -1.38 110
November 0.006 1.686 0.04 108
December 0.170 1.217 1.00 110
Table 3 (Contd)
1980-84 Mean Std. Dev. t-stat F/K-W stat Observations
January 0.309 1.170 2.66*** F-stat=2.25** 109
February 0.064 1.468 0.55 (0.011) 102
March 0.076 1.089 0.69 111
April 0.325 1.108 2.70*** K-W=25.13*** 108
May -0.243 1.016 -2.01** (0.009) 109
June 0.093 1.079 0.83 108
July -0.024 1.091 -0.22 111
August 0.189 1.109 1.64 110
September -0.186 1.293 -1.64 108
October 0.050 1.169 0.45 110
November 0.226 1.203 2.02** 107
December -0.005 1.017 -0.04 112
1990-94 Mean Std. Dev. t-stat F/K-W stat Observations
January 0.077 0.873 0.83 F-stat=0.71 111
February 0.100 0.806 0.95 (0.727) 80The Essay is provided by UK Assignment http://www.ukassignment.org
March -0.021 0.845 -0.21 88
April 0.002 1.001 0.01 87
May 0.145 0.773 1.32 88
June -0.090 0.729 -0.88 85
July -0.020 0.776 -0.21 90
August -0.116 1.095 -1.11 88
September -0.070 1.264 -0.68 85
October 0.106 1.212 1.07 89
November 0.075 0.831 0.74 86
December 0.101 0.873 0.94 89
***, ** and * denote statistical significance at the 1%, 5% and 10% level respectively.
Table 4: Summary Statistics for the Holiday Effect in the FT30 Index
Day After Hol. Non-Holiday Holiday Std. Dev. t-stat Observations
Monday -0.129 0.085 0.975 0.74 82
Tuesday 0.052 0.024 0.942 0.35 235
Wednesday 0.066 0.326 1.214 2.00** 41
Thursday 0.035 0.245 1.697 1.29 30
Friday 0.074 0.675 0.805 3.31 26
F-stat 2.89** (0.022) K-Wallis 16.37*** (0.003)
Table 5: Summary Statistics for the Holiday Effect in the FT30 Index
Holiday = day 0 Mean Return Std. Dev. t-stat Observations
-3 0.135 0.952 2.41** 299
-2 0.119 1.009 2.14** 299
-1 0.269 0.938 4.81*** 299
+1 0.071 1.013 1.27 299
+2 0.148 0.916 2.64*** 299
*** and ** denote significance at the 1% and 5% level respectively.
Additional Reading for Lecture 3 – Stock Market Anomalies and Market Efficiencies
Mills, T C and Coutts, J A (1995), "Calendar Effects in the London FT-SE Indices", The European Journal of Finance, 1, 79-93.
Arsad, Z and Coutts, J A (1996) "The Weekend Effect, Good news, Bad News and The Financial Times Industrial Ordinary Shares Index: 1935-1994", Applied Economics Letters, 3, 797-801.
Arsad, Z and Coutts, J A (1997), "The Trading Month anomaly in the Financial Times Industrial Ordinary Shares Index: 1935-1994", Applied Economics Letters, 3, 297-299.
Arsad, Z and Coutts, J A (1997), "Security Price Anomalies in the London International Stock Exchange: A sixty year Perspective", Applied Financial Economics, 7, 455-464.
Coutts, J A and Hayes, P (1999), "The Weekend Effect, The Stock Exchange Account and The Financial Times Industrial Ordinary Shares Index: 1987-1994", Applied Financial Economics, 9, 67-71.
Coutts, J A (1999), "Friday the Thirteenth and the Financial Times Industrial Ordinary Shares Index 1935-94", Applied Economics Letters, 6, 35-37.
Cheung, K -C and Coutts, J A (1999) "The January Effect and Monthly Seasonality in the Hang Seng Index: 1985-1997", Applied Economics Letters, 6, 121-123.
Coutts, J A and Sheikh, M A (2000), "The January Effect and Monthly Seasonality in the All Gold Index on the Johannesburg Stock Exchange 1987-1997", Applied Economics Letters, 7, 489-492.
Coutts, J A, Kaplanidis, C and Roberts, J (2000), "Security Price Anomalies in an Emerging Market: The Case of the Athens Stock Exchange", Applied Financial Economics, 10, 561-571.The Essay is provided by UK Assignment http://www.ukassignment.org
Coutts, J A and Cheung K -C (2000), "Trading Rules and Stock Returns: Some Preliminary Short Run Evidence from the Hang Seng 1985-1997", Applied Financial Economics, 10, 579-586.
Cheung, K -C and Coutts, J A (2001), "A Note on Weak Form Market Efficiency in Security Prices: Evidence from the Hong Kong Stock Exchange", Applied Economics of Letters, 2001, 8, 407-410.
Coutts, J A and Sheikh, M A, (2002), "A Note on the Anomalies that Aren't There: The Weekend and Pre-Holiday Effects on the All Gold Index on the Johannesburg Stock Exchange 1987-1997", Applied Financial Economics, 12, 863-871.
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