- Email: [email protected]
New evidence on the impact of size and taxation on the seasonality of UK equity returns
|←
RevFin
p93682$$$3
p. 11
08-26-99 08:41:49
→|
Review of Financial Economics 8 (1999) 11–24
New evidence on the impact of size and taxation on the seasonality of UK equity returns Kojo Menyah* Department of Accounting and Financial Services, London Guildhall University, 84 Moorgate, London EC2M 6SQ, UK
Abstract The paper investigates the extent to which capital gains taxation and the portfolio rebalancing hypothesis may account for the seasonality of UK equity returns. The empirical results show that in small firm portfolios during the period of capital gains taxation, April but not January seasonality is consistent with the tax-loss selling hypothesis. The January seasonality, which is detected even before the introduction of capital gains taxation, is also consistent with the portfolio rebalancing hypothesis until the 1980s, when such seasonality becomes increasingly insignificant. 1999 Elsevier Science Inc. All rights reserved. Keywords: Seasonality; Tax-loss selling; Portfolio rebalancing; Hypothesis
1. Introduction and objectives of the study The empirical phenomenon of high monthly equity returns in January relative to other months of the year was first documented by Wachtel (1942) for the New York Stock Exchange (NYSE) over the period of 1927–1942. Rozeff and Kinney (1976) confirmed Wachtel’s finding of a January seasonal from 1904 to 1974. Keim (1983) concluded that about half of the January seasonal was associated with small firms during the period of 1962–1979. Reinganum (1983) investigated the year-end tax-loss selling hypothesis that had been advanced by Wachtel (1942) to explain the unusual January returns. He noted that tax-loss selling of small capitalization stocks explains a significant part of the high January returns. The interaction between firm size and tax-loss selling in January was refined by Ritter (1988). He argued that individual investors who primarily invest in small capitalization stocks tend to sell them in December to realize capital losses and buy into such stocks in January. Eakins and Sewell (1993) and Ligon (1997) found evidence consistent with this hypothesis. * Corresponding author. Tel.: 10171-320-1535; fax: 10171-320-1557. E-mail address: [email protected] (K. Menyah) 1058-3300/99/$ – see front matter 1999 Elsevier Science Inc. All rights reserved. PII: S1058-3300(99)00004-X
TRIM→
|←
12
RevFin
p93682$$$3
p. 12
08-26-99 08:41:49
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
Haugen and Lakonishok (1988), on the other hand, argue that January seasonality could be induced by fund managers rebalancing their portfolios to either hedge or window dress their performance. In performance hedging, fund managers lock in their gains, especially from small stocks, and move into stocks that will closely replicate the features of their performance benchmark during the course of the calender year. They re-enter the market after the last reporting day to acquire undervalued small stocks that consequently appreciate in price at the start of the calender year. In window dressing, fund managers include stocks that have done well in the recent past and eliminate under-performing small stocks for year-end reporting purposes. At the start of the new reporting period, managers choose stocks, especially those of small firms that are likely to provide higher returns during the year. The prices of small stocks therefore rebound at the start of the next reporting period. Evidence consistent with this hypothesis has been reported by Ritter and Chopra (1989) and Porter et al. (1996) for the US and by Athanassakos (1992) and Athanassakos and Schnabel (1994) for Canada. In light of the above, it is clear that tax-loss selling and portfolio rebalancing could interact to generate January seasonality in markets with a December tax year-end. This makes it difficult to test the two hypotheses independently on the same data, as is usually done in US studies. This paper contributes to the literature by using data from the UK, which has a tax year (in April)1 that is different from the calender year, to separately test the tax-loss selling and portfolio rebalancing hypotheses. Specifically, the portfolio rebalancing hypothesis is tested as an explanation of January seasonality, while tax-loss selling and window dressing explanations are investigated for April seasonality. This contrasts with previous UK papers that either focused on tax effects (Reinganum & Shapiro, 1987) or size effects (Levis, 1985; Corhay et al., 1988; Clare et al., 1995) to the neglect of other explanations of January and April seasonalities. The next section describes the evolution of UK capital gains taxation and assesses its impact on tax related explanation of seasonality. The data and sample selection methods are described in section 3. Using a GARCH-M model, section 4 identifies the months and portfolios that show evidence of seasonality during the sample period. Section 5 examines the extent to which January and April seasonalities could be explained by either the portfolio rebalancing hypothesis or tax-loss selling. A summary of the study and its findings are presented in the final section. 2. UK capital gains taxation Compared to the US, which introduced capital gains taxation in 1913, it was not until half a century later that a short-term speculative gains tax became law in the UK.2 A comprehensive capital gains tax was introduced in 1965 after years of public debate.3 The major capital gains tax law changes covered by this study and their possible effects on year-end trading have been summarized in Appendix 1. It is recognized, however, that the effect of legislation on trading behavior is an empirical issue determined by the actions of marginal investors in an efficient market, as noted by Hamada and Scholes (1985)4 and influenced by inframarginal investors5 if the market is not deemed to be efficient. Before the introduction of the capital gains tax
TRIM→
|←
RevFin
p93682$$$3
p. 13
08-26-99 08:41:49
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→|
13
in 1965, relative prices in January and April could have been determined by taxexempt marginal investors, such as pension funds, since taxable investors stood to lose if they traded towards the end of the fiscal year from 1962 onwards and subjected themselves to the short-term speculative gains tax. A non-tax explanation is therefore more likely to be consistent with any seasonalities observed before 1965. After 1965, taxable investors who owned around 73%6 of equity constituted the inframarginal investors whose trading could have affected year-end pricing if they exploited pricing anomalies to their advantage (Seyhun, 1993).7 The extent of their trading activities could have been limited by the restrictive nature of the loss offset provisions, which did not allow short-term losses to be set off against long-term gains. The opportunities for tax year-end trading strategies were increased, however, when the distinction between short- and long-term capital gains tax was abolished in 1971. Taxable investors’ incentive for year-end trading could have increased because of the provision that all losses (short- and long-term) could be set off against all gains. The introduction of indexation in 1982 and the rebasing of capital gains tax in 1988 reduced the amount of taxable realized gains and could have minimized the extent of tax-loss selling by inframarginal investors. The extent to which the above legislative changes could have induced April seasonality is verified empirically in subsequent sections. 3. Data and sample selection The study uses the London Business School Share Price Database for the period between 1955 and 1992. The database contains information on about five and a half thousand companies that were quoted on the London Stock Exchange between 1955 and 1992. The sample used excluded firms quoted on the Unlisted Securities Market, the Third Market, and OTC market.8 Companies that had not entered the database by March 1988 were excluded because the major capital gains tax changes that form the focus of this study had already become law. The annual sample of companies ranged from 906 in 1958 to 2,056 in 1976 with a mean of 1,508.9 To create size portfolios, all the stocks in the database at the beginning of every year (from 1955) were sorted by market value. Ten size-based portfolios were created. During the sample period, the average market value for the smallest decile portfolio was £1.24 million, while that of the largest decile portfolio was £521 million with a median portfolio market value of £8.6 million. For each portfolio, the monthly returns of the member stocks were equally weighted to obtain the average returns. The process was repeated for each year up to 1992. We also used two portfolios that are not related to size—average portfolio of all stocks in our sample each year as well as the Financial Times Institute of Actuaries (FTA) index—in order to assess the extent to which the averaging of returns can affect seasonality. 4. Excess return seasonality and size under capital gains tax regimes Analysis of raw return data shows January and April to have the highest monthly returns for all deciles during the period of 1956–1992.10 Tests of equality of monthly returns based on two-way ANOVA tests and Friedman Rank Sums reinforce the
TRIM→
|←
14
RevFin
p93682$$$3
p. 14
08-26-99 08:41:49
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
inequality of monthly returns. Analysis based on raw returns, however, lack rigor because, as Roll (1983) has pointed out, they do not adequately account for the effect of risk on stock returns. A variety of risk adjustment measures have been used in UK studies of return seasonality. Levis (1985) tested for excess return over the riskfree rate with the Sharpe-Lintner Capital Asset Pricing Model (CAPM). Corhay et al. (1988) used different measures of risk to adjust returns in their study of seasonal risk premium and concluded that variance of return is a better proxy for risk than b or residual risk. Clare et al. (1995) modeled the FTA index return as an ARMA process and used GARCH-M(p,q) to account for risk. They did not, however, adjust for the risk free return. This paper extends previous studies by testing for excess return seasonality using FTA index returns, size-based portfolios of company returns, and average returns of stocks in the London Business School Shareprice Database. The following GARCH-M(p,q) specification is used in the tests to control for timevarying variances. 1
Rpt 2 Rft 5 b0 1 b1h2t 1
N
12
i51
m51
o bi(Rpt 2 i 2 Rft 2 i) 1 o Cmdtm 1 et
ht 5 g0 1 g1e2t 2 1 1 g2ht 2 1 1
(1)
11
o C*mdtm
(2)
m51
where Rpt is the return on a portfolio (index or average return), Rft is the risk free rate and Cmdm is the dummy variable to capture monthly excess returns in Eq. 1. Eq. 2 is the specification of the GARCH-M(p,q) model with appropriate dummy variables to control for changing monthly variances. Several lag structures were investigated for the mean equation as well as the conditional variance equation. On the basis of diagnostic tests, a six-period lag was considered adequate for the mean equation and a GARCH-M(1,1) for the conditional variance equation.11 The tests of seasonality for the sample period using the various portfolios are reported in Table 1. The estimation of the variance term in the mean equation gives a reward to risk ratio of 0.24, which is twice that obtained by Clare et al. (1995). The magnitudes of the excess return range from 1.6% for the fourth smallest portfolio to 2.4% for the third smallest portfolio. Significant January excess return seasonality is detected in all but the two largest portfolios and the FTA index. For April, seasonality is only observed in the three smallest portfolios. In Table 1, an excess return seasonality of 1.68% is detected in January for the average sample portfolio. The FTA and average portfolio returns for April show no evidence of seasonality.12 These findings reinforce the presence of January seasonality and suggest that April excess return seasonality is more likely to be induced by firm size.13 The likely explanations of seasonality in January and April are therefore investigated in the next section. 5. Determinants of January and April seasonalities 5.1. Is January seasonality consistent with the portfolio rebalancing hypothesis? Since the January seasonality detected in the preceding section cannot be attributed to tax-loss selling, the portfolio rebalancing hypothesis is investigated as a possible explanation for the finding. Even though January seasonality is associated with perfor-
TRIM→
|←
RevFin
p93682$$$3
p. 15
08-26-99 08:41:49
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→|
15
Table 1 GARCH-M(1,1) test of excess return seasonality based on 10 size-based portfolios for the period 1956– 1992 Relevant mean equation parameter estimates, t statistics, and p values 1/2
Residuals
Portfolio
Constant
h
Jan
April
Skewness
Kurtosis
Smallest
20.0074 (21.073) (0.28)
0.249 (1.897) (0.05)
0.0218 (3.876) (0.0001)*
0.0129 (2.020) (0.0433)**
20.2587
5.6373
20.0073 (21.160) (0.24)
0.249 (1.900) (0.05)
0.0192 (3.482) (0.0004)*
0.0128 (1.836) (0.0662)***
20.4239
20.0097 (21.381) (0.166)
0.249 (2.042) (0.041)
0.0242 (4.093) (0.0000)*
0.0115 (1.698) (0.0893)***
20.6731
20.0050 (20.728) (0.4663)
0.249 (2.010) (0.0444)
0.0164 (2.543) (0.0109)*
0.0084 (1.185) (0.2358)
20.0078 (20.9572) (0.3384)
0.249 (1.9336) (0.0531)
0.0178 (2.6610) (0.0077)*
0.0073 (0.9736) (0.3302)
20.3831
20.0078 (21.006) (0.3141)
0.249 (1.8879) (0.0590)
0.0215 (3.2161) (0.0012)*
0.0077 (0.9533) (0.3403)
20.3258
20.0106 (21.288) (0.1975)
0.249 (1.843) (0.0653)
0.0211 (2.974) (0.0029)*
0.00905 (1.100) (0.2709)
20.3195
2
3
4
5
6
7
(0.0276)
(0.0003)
(0.0000) 4.6781 (0.0000) 4.7578
(0.0000)
(0.0000)
0.1890
5.4160
(0.1074)
(0.0000)
(0.0011)
(0.0055)
(0.0065)
4.2653 (0.0000) 4.5539 (0.0000) 4.4389 (0.0000) (continued)
mance hedging (because bonuses are paid at the end of the calender year), it could also be caused by window dressing if a fund’s accounting year is December. To investigate the hypothesis, firms are ranked by market value at the beginning of the preceding calender year (beginning in 1955). The price change between the November of the current year and 5 and 11 months previously is calculated to obtain winning and losing stocks as in Lakonishok and Smidt (1986). For 1959 for instance, we divided the November price by the June 1959 and December 1958 month-end prices to obtain the price relatives for the 5- and 11-month holding periods, respectively. Using the procedure by Bolster et al. (1989), stocks that yielded a holding period value of more than 1 were classified as winners and those with a value of less than 1 as losers. Two portfolios of winning and losing stocks were formed after discarding those with no price changes. Within each of the portfolios, stocks were ranked by market value, and two further portfolios based on size were created. Stock returns
TRIM→
|←
RevFin
16
p93682$$$3
p. 16
08-26-99 08:41:49
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
Table 1 (continued ) Relevant mean equation parameter estimates, t statistics, and p values Portfolio
Constant
h
8
20.0086 (21.0733) (0.2831)
9
Largest
Av. Portfolio
FTA Index
1/2
Jan
April
0.249 (1.9581) (0.0502)
0.0173 (2.2112) (0.0270)**
0.0095 (21.0483) (0.2944)
0.249 (1.7948) (0.0726)
20.0081 (20.7996) (0.4238)
Residuals Skewness
Kurtosis
0.0029 (0.3531) (0.7239)
20.1973
4.8573
0.0101 (1.2527) (0.2103)
(0.0038 (0.4210) (0.6736)
20.2596
0.249 (1.6509) (0.0987)
0.0064 (0.7872) (0.4311)
0.0025 (0.2713) (0.7861)
20.1984
20.0067 (20.8895) (0.3736)
0.249 (1.9848) (0.0471)
0.0168 (2.4899) (0.0127)*
0.0074 (0.9722) (0.3309)
20.3606 (0.0021)
(0.0000)
20.0105 (20.995) (0.3192)
0.249 (1.6789) (0.0931)
0.0093 (1.1458) (0.2518)
0.0011 (0.1196) (0.9047)
0.7188
10.0659
(0.0000)
(0.0000)
(0.0929)
(0.0270)
(0.0911)
(0.0000) 4.9289 (0.0000) 4.7622 (0.0000)
5.6145
The estimated equations are: 1
Rpt 2 Rft 5 b0 1 b1h2t 1
7
12
i52
m51
o bi(Rpt 2 1) 1 o
ht 5 g0 1 g1e2t 2 1 1 g2ht 2 1 1
11
o
m51
Cmdtm 1 et
C*m dtm
The first and second numbers under the parameter estimates are the coefficients and their t values, respectively. The third number is the p value. * Statistically significant at 1% or better. ** Statistically significant at 5% or better. *** Statistically significant at 10% or better.
for January and April in the year following that used in generating the winner or loser portfolios were collected and averaged for each year to obtain the data for testing the hypothesis. The descriptive statistics of the data used in the tests are shown in Table 2. The capitalization of large firm winners (averaging £114 million for the 5-month holding period) is about 40 times that of small firm losers, which average £2.6 million for the same holding period. The average portfolio return for small firm losers is higher in January (4.1% for the 11-month holding period) than that of large firm winners (which averages 2.7%). The April returns, on the other hand, indicate that small firm losers with a return of 2.6% (for the 5-month holding period) have lower returns than large firm winners, which generate 3.2%. The portfolio rebalancing hypothesis is supported if the return on the small firm loser portfolios is significantly higher than the return on the portfolio of large firm
TRIM→
|←
RevFin
p93682$$$3
p. 17
→|
08-26-99 08:41:49
K. Menyah / Review of Financial Economics 8 (1999) 11–24
17
Table 2 Descriptive statistics of data on winning and losing portfolios for the period 1956–1992
5-Month holding period Large firm winners Small firm losers 11-Month holding period Large firm winners Small firm losers
Ave. firm size £ million
Ave. portfolio return
Max. return
Min. return
January
April
January
April
January
April
114.4 2.68
3.00 4.2
3.2 2.6
14.7 32.0
9.8 11.6
210.7 210.3
27.8 210.5
94.8 3.07
2.7 4.1
3.2 3.1
15.9 27.6
9.9 12.7
215.5 211.5
27.1 26.0
All returns are in percentages. Thirty-six annual observations were used in the calculations for both holding periods.
winners. The results of test of differences based on a parametric t test and the nonparametric Wilcoxon Signed Rank Test are reported in Table 3. The January results for the period of 1956–1965 show that the small firm losing portfolio returns are significantly higher (by at least 1.7%) than that of the large firm winning portfolios for both holding periods. Such findings are consistent with performance hedging as well as window dressing by fund managers with a December accounting year-end during the period before the introduction of capital gains tax. The April results, however, are not supportive of window dressing by fund managers with a March yearend because they show that the returns to large firm winners are higher (by at least 2%) than those to small firm losers. For the 1966–1992 period, the portfolio rebalancing hypothesis is not supported because the differences are not statistically significant. In order to ascertain the reasons for the insignificance of the results, the capital gains tax period was further analyzed by subdividing the period: 1966–1979 and 1980–1992. Estimates of the significance of January seasonality based on Eqs. 1 and 2 confirmed its significance during the 1966–1979 period but not in the second subperiod. The insignificance of the results for the 1980s is consistent with the findings of Alford and Guffey (1996) that seasonality has been disappearing in the US and UK equity markets since 1983. Tests of the portfolio rebalancing hypothesis in January for the period of 1966–1979 showed that they are statistically significant for both the 5- and 11-month holding periods. For the 5-month holding period, the difference between small firm losers and large firm winners was about 1.7%, which is significant at 10% using the non-parametric Wilcoxon Signed Rank Test. The difference in return for the 11month period is 2.53%, which is significant at 7% on the basis of the non-parametric test. These findings suggest the portfolio rebalancing hypothesis appears to be the main explanation for January seasonality in the UK equity market at least until the early 1980s. 5.2 Did firm size interact with tax-loss selling to induce seasonality? The analysis in section 4 also shows that April seasonality is concentrated in small firms, as evidenced in Table 1. We therefore explored how firm size and tax-loss selling may have interacted to induce high monthly returns in April. The tax-loss
TRIM→
|←
RevFin
18
p93682$$$3
p. 18
→|
08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24
Table 3 Tests of performance hedging and window dressing hypotheses Test type 5-Month holding period January Parametric Non-parametric April Parametric Non-parametric 11-Month holding period January Parametric Non-parametric April Parametric Non-parametric
Before capital gains tax (1956–1965)
After capital gains tax (1966–1992)
Return diff
Return diff
t Stats
1.7 1.8
2.52 —
22.5 22.4
22.67 —
1.9 1.9
2.99 —
22.0 22.2
22.16 —
p Value
0.036* 0.044*
0.9 0.8
0.028 0.44
20.01 0.07
0.017* 0.042* 0.062 0.097
t Stats
p Value
0.97 —
0.34 0.24
20.02 —
0.99 0.91
1.1 0.8
1.42 —
0.17 0.2
0.5 0.4
1.36 —
0.19 0.25
The parametric test is a t test of differences in means that does not assume equality of sample variances. The non-parametric test is the Wilcoxon Signed Rank Test of differences between medians. “Return Diff.” refers to either the difference between the mean or median for the small firm loser portfolio and large firm winner portfolio. For the portfolio rebalancing hypothesis to hold, the return on the small firm loser portfolio should be higher than that on the large firm winner portfolio. p values with aesterisks (*) have the correct sign and are also significant at 5% or better.
selling hypothesis implies that prices of stocks that depreciate towards the end of the tax year should rebound in the following year. This means that returns on small company stocks that depreciate in price before the end of the tax year will exhibit higher returns relative to those of larger companies that had appreciated in price at the start of the tax year. The data for the empirical test is the same as that used in the preceding subsection. If tax-loss selling of small firms induces April seasonality, then the difference in return between the portfolio of small firm losers and large firm winners should be positive and statistically significant during the period of capital gains taxation. This hypothesis was tested by regressing the difference in returns between loser and winner portfolios against an intercept and a dummy variable for the period of 1956–1992.14 The dummy variable takes a value of 1 during the capital gains tax period (1966–1992) and 0 during the period without capital gains taxation.15 If small firm losers out-perform large firm winners in April because of tax-loss selling, then the coefficient of the dummy variable should be statistically different from 0 at conventional levels of significance. The results of the estimates reported in Table 4 show that the dummy coefficient is statistically significant from 0 for both holding periods when taxable investors had the incentive to generate losses to offset gains so as to avoid paying taxes at the higher short-term rate. These findings are consistent with tax-loss selling inducing high returns
TRIM→
|←
RevFin
p93682$$$3
p. 19
08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→|
19
Table 4 Regression equation tests of the tax-loss selling hypothesis Constant Tests with April portfolio returns 5-Month holding periods 11-Month holding periods Tests with January portfolio returns 5-Month holding periods 11-Month holding periods
20.025 (22.30) 20.020 (22.69) 0.017 (1.13) 0.020 (1.60)
Dummy coefficient 0.025 (1.99)** 0.025 (2.95)* 20.008 (20.44) 20.009 (20.60)
Adjusted R2
7.8% 18.0%
0.0% 0.0%
The following equation was estimated: Rloser,t 2 Rwinner,t 5 a 1 bD 1 et, where the dependent variable is the difference between the small firm loser and large firm winner portfolios and the dummy variable takes on a value of 0 during the period before capital gains taxation, 1956–1965, and a value of 1 during the period of capital gains taxation. t statistics are in parenthesis. *Dummy variable coefficient is significant at 1%. **Dummy variable coefficient is significant at 5%.
in small company stocks in April. A second regression was estimated with the difference in portfolio returns between small firm losers and large firm winners in January. The results, also shown in Table 4, reject the suggestion that January seasonality is induced by the tax-loss selling of small firms’ stocks since all the dummy variable coefficients are statistically insignificant. It is therefore reasonable to conclude that the tax-loss selling hypothesis associated with small firm stocks at the beginning of the tax year as proposed by Ritter (1988) is confirmed for the UK. This is related to the previous results of Reinganum and Shapiro (1987), who found April seasonality to be consistent with tax-loss selling but found their significant January returns “difficult to interpret.” Their results could be attributed to the fact that they did not control for firm size in the construction of their loser and winner portfolios and were therefore unable to investigate the hypothesis tested in this paper. 6. Conclusion This paper investigated monthly seasonality of UK equity returns during the 1956– 1992 period. Using a GARCH-M model, evidence of January seasonality is found for all but the two largest size portfolios. On the other hand, April seasonality is detected in only the three smallest size portfolios. Since the tax explanation is not relevant for UK January seasonality, the portfolio rebalancing hypothesis was investigated. Evidence consistent with portfolio rebalancing in January was detected from 1956 (well before the introduction of capital gains taxation) to the early 1980s, when January seasonality starts to become statistically insignificant. The portfolio rebalancing hy-
TRIM→
|←
RevFin
20
p93682$$$3
p. 20
08-26-99 08:41:50
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
pothesis was rejected as a possible explanation for April seasonality. On the other hand, the tax-loss selling hypothesis of Ritter (1988) that small firms’ stock rebound at the start of the tax year was tested in relation to April seasonality. The findings support the proposition that April seasonality of small firms is associated with taxloss selling. This is reinforced by the fact that similar tests rejected the tax-loss selling explanation of January seasonality. Acknowledgments The financial support of the Business Faculty Research Committee and the programming assistance of Pari Jahankhani are gratefully acknowledged. The comments of an anonymous referee and those of Krishna Paudyal, which have significantly helped to focus the paper, are gratefully acknowledged. Notes 1. April is the tax year-end for individuals in the UK. For companies, including institutional investors, the tax year-end is the same as their individual accounting year-end. December and March are the most popular accounting year-ends for most companies and institutions. 2. According to Ilersic (1962), a very active bull market for stocks from October 1959 to January 1960 combined with a property market boom made public opinion receptive to the idea of a tax on speculative gains. 3. A Royal Commission on the Taxation of Profits and Income (1955) had argued against it in its majority report. A three-man minority report, however, advocated the taxation of capital gains, especially on securities, which in their view increased taxable capacity by increasing the power to spend and save. Chancellor James Callaghan relied on the minority report of the Royal Commission on the Taxation of Profits and Income to introduce a comprehensive capital gains tax in 1965. 4. Hamada and Scholes (1985, p. 190), define the “marginal” investors as the class of investors setting the relative prices at the margin in the sense that they are indifferent to purchasing or supplying any more of those securities at those prices and after having considered all the tax implications. There is no more tax gain nor tax loss for marginal investors at the given relative prices. 5. “Inframarginal” investors will have a tax gain or loss by purchasing or selling more of those securities at the given relative prices, unless there are regulatory or tax constraints that prohibit such tax gaming (Hamada and Scholes, 1985). 6. The figure was obtained from a table prepared by the Central Statistical Office, 1994. 7. The tax-loss selling hypothesis, however, implies the absence of weak-form market efficiency because traders can predict the direction of price movement at the turn of the calender or fiscal year.
TRIM→
1962 A tax on short-term speculative gains payable at the marginal tax rate on stocks bought and sold within six months was introduced. There was no taxation of capital gains realized after 6 months. 1965 A comprehensive capital gains tax system was introduced. Short-term gains tax was payable at the marginal rate for stocks held for less than twelve months. Short-term losses could only be offset against short-term gains. Long-term gains on stocks held for more than twelve months were taxed at rates below the marginal rates. Long-term losses could only be offset against long-term gains. 1971 Dual rate capital gains taxation abolished. All capital gains— and long-term—to be taxed at a special rate for all investors.
Main capital gains tax legislation 0 0 0
88.75} 56.25}5 56.25}
0 0 0
91.25} 40.00}6 30.00}
Individuals Industrial companies and banks4 Investment and unit trusts firms
Individuals Industrial companies and banks Investment and unit trusts firms
Individuals Industrial companies and banks Investment and unit trusts firms
30 30 30
08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24 (continued)
Incentive to all taxable investors to undertake year-end trading due to absence of restrictions on loss offsets.
p. 21
Investment and unit trusts could realize short-term losses as well because of identical tax rate on all gains.
Likely year-end trading by individuals and companies of stocks with long-term gains and losses because of lower tax rates.
Taxable investors to hold stocks for more than 6 months to avoid the tax rather than engage in tax loss selling.
Likely trading impact
p93682$$u3
30 30 30
91.25 56.25 30.00
88.75} 53.75}3 53.75}
Highest tax rates after change (%)
RevFin
Individuals Industrial companies and banks Investment and unit trusts
Investors affected1
Highest tax rates prior to change (%)2
Appendix 1 The evolution of UK capital gains tax legislation and likely impact on year-end stock trading
|← →|
21
TRIM→
40 35 40
Incentive for year-end tax motivated trading due to increase in marginal tax rates on capital gains.
2
p. 22
Pension funds did not pay either dividend or capital gains tax during the period under review for UK investments. These were the tax rates in force before the date of change in the tax described in column 1. 3 These rates applied to disposal of assets that had been held for less than 6 months. 4 This includes insurance companies that were usually taxed as other companies. From 1920, investment income apportioned to policyholders was taxed at 37.5% until the corporation tax rate fell below this rate in 1986. 5 The changes in rates for industrial companies, banks, and unit and investment trusts reflect the changes in tax rates. 6 These were the tax rates on short-term gains in force before all capital gains were made taxable at the same rate. 7 From April 1982 to March 1985, gains on stocks held for more than 12 months attracted an inflation indexation allowance that reduced the taxable gain. The holding period qualification for the indexation allowance was abolished in April 1985.
30 30 30
Likely trading impact
p93682$$u3
1
Individuals7 Industrial companies and banks Investment and unit trust holders
Highest tax rates after change (%)
RevFin
1982 & 1988 Indexation allowance for inflation was introduced in 1982. Only real gains were taxable. In 1988, all capital gains that had accrued to stockholders since the introduction of the tax in 1965 were made free of tax. Only gains accruing since 1982 were taxable. All capital gains were made taxable at the marginal tax rate of investors with the abolition of the preferential capital gains tax rate in 1988.
Investors affected1
Highest tax rates prior to change (%)2
22
Main capital gains tax legislation
Appendix 1 (continued)
|← 08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→| TRIM→
|←
RevFin
p93682$$$3
p. 23
08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→|
23
8. The study uses companies quoted on the main market known as the Official List. The Third Market was abolished in 1989, and the USM ceased to exist in 1995. Companies that transferred to the main market either from the Third Market or the USM were included in the sample. 9. Our sample compares reasonably well with other UK studies that included stocks from other market segments. Reinganum and Shapiro’s sample ranged from 1,021 to 2,228, while that of Levis ranged from 1,500 to 2,400. 10. The January and April returns average 3.5 and 3.2%, respectively, for the sample period. This contrasts with average returns ranging from 0.03 to 1.6% (for July and February, respectively) and for 4 months (June, September, October, and November) that have negative average returns. Our results (available from the author on request) are broadly consistent with previous UK findings by Levis (1985) for the period of 1956–1980. The difference in return between the smallest and largest market value portfolios is also significant at the 5% level. This reinforces the view that small firms earn higher returns than large firms. 11. This specification is the same as that used by Clare et al. (1995). 12. Clare et al. (1995) found evidence of January and April seasonalities in the logarithmic changes in the FTA index levels. Their index returns, however, exclude capital gains. We use the logarithmic total returns in our analysis. Our model specification also focuses on excess return over the risk-free rate ,while Clare et al. did not include the risk-free rate. These differences in data used may explain why our results differ from theirs. 13. To examine the robustness of raw return April seasonality detected for the subperiod of 1956–1964, we estimated Eqs. 1 and 2 with subperiod data but found no evidence of seasonality. This finding is consistent with Reinganum and Shapiro (1987), who found no evidence of seasonality for the same subperiod. 14. The period before the introduction of capital gains tax was defined to coincide with that used by Reinganum and Shapiro (1987). When the capital gains tax period was allowed to start in 1965, the results did not change. 15. The regression equation is the same as that estimated by Reinganum and Shapiro (1987). Their winner and loser portfolios, however, were not based on firm size and so could not test how size and tax could interact to induce April return seasonality. References Alford A., & Guffey, D.M. (1996). A re-examination of international seasonalities. Review of Financial Economics 5, 1–17. Athanassakos, G. (1992). Portfolio-rebalancing and the January Effect in Canada. Financial Analysts Journal 48 (November/December), 67–78. Athanassakos, G., & Schnabel, J. A. (1994). Professional portfolio managers and the January Effect: theory and evidence. Review of Financial Economics 4, 79–91. Bolster, P. J., Lindsey, L. B., & Mitrusi, A. (1989). Tax-induced trading: the effect of the 1986 Tax Reform Act on stock market activity. Journal of Finance 44, 327–344.
TRIM→
|←
24
RevFin
p93682$$$3
p. 24
08-26-99 08:41:50
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
Central Statistical Office (1994). Share Register Survey End 1993. London: Her Majesty’s Stationery Office. Clare, A. D., Psaradakis, Z., & Thomas, S. H. (1995). The analysis of seasonality in the U.K. equity market. The Economic Journal 105, 398–409. Corhay, A., Hawawini, G., & Michel, P. (1988). The pricing of equity on the London Stock Exchange: seasonality and size premium. In M. Levis (Ed.), Stock Market Anomalies (pp. 197–212). Cambridge: Cambridge University Press. Eakins, S., & Sewell, S. (1993). Tax-loss selling, institutional investors and the January Effect: a note. Journal of Financial Research 16, 377–384. Great Britain (1955). Final Report: Royal Commission on the Taxation of Profits and Income. CMND 9474. Hamada, R. S., & Scholes, M. S. (1985). Taxes and corporate financial management. In E. I. Altman & M. G. Subrahmanyam (Eds.), Recent Advances in Corporate Finance (pp. 189–226). Homewood, IL: Richard D Irwin. Haugen, R., & Lakonishok, J. (1988). The Incredible January Effect. Homewood, IL: Dow Jones-Irwin. Ilersic A. R. (1962). The Taxation of Capital Gains. London: Staples Press. Keim, D. B. (1983). Size-related anomalies and stock market seasonality: further empirical evidence. Journal of Financial Economics 12, 13–32. Lakonishok, J., & Smidt, S. (1986). Volume for winners and losers: taxation and other motives for stock trading. Journal of Finance 41, 951–974. Levis, M. (1985). Are small firms big performers? The Investment Analyst 76, 21–27. Ligon, J. A. (1997). A simultaneous test of competing theories regarding the January Effect. The Journal of Financial Research 20, 13–32. Porter, D. C., Powell, G. E., & Weaver, D. G. (1996). Portfolio rebalancing, institutional ownership, and the small firm-January Effect. Review of Financial Economics 5, 19–29. Reinganum, M. R. (1983). The anomalous stock market behaviour of small firms in January: empirical tests for tax-loss-selling effects. Journal of Financial Economics 12, 89–104. Reinganum, M. R., & Shapiro, A. C. (1987). Taxes and stock return seasonality: evidence from the London Stock Exchange. Journal of Business 60, 281–295. Ritter, J. R. (1988). The Buying and selling behaviour of individual investors at the turn of the year. Journal of Finance 43, 701–717. Ritter, J. R., & Chopra, N. (1989). Portfolio rebalancing and the turn-of-the-year. Journal of Finance 44, 149–166. Roll, R. (1983). Vas ist das? The turn-of-the-year effect: anomaly or risk measurement? Journal of Portfolio Management 9, 18–28. Rozeff, M. S., & Kinney, W. R., Jr. (1976). Capital market seasonality: the case of stock returns. Journal of Financial Economics 3, 379–402. Seyhun, H. N. (1993). Can omitted risk factors explain the January Effect? A stochastic dominance approach. Journal of Financial and Quantitative Analysis 28, 397–402. Wachtel, S. (1942). Certain observations on seasonal movements in stock prices. Journal of Business 15, 148–193.
TRIM→
RevFin
p93682$$$3
p. 11
08-26-99 08:41:49
→|
Review of Financial Economics 8 (1999) 11–24
New evidence on the impact of size and taxation on the seasonality of UK equity returns Kojo Menyah* Department of Accounting and Financial Services, London Guildhall University, 84 Moorgate, London EC2M 6SQ, UK
Abstract The paper investigates the extent to which capital gains taxation and the portfolio rebalancing hypothesis may account for the seasonality of UK equity returns. The empirical results show that in small firm portfolios during the period of capital gains taxation, April but not January seasonality is consistent with the tax-loss selling hypothesis. The January seasonality, which is detected even before the introduction of capital gains taxation, is also consistent with the portfolio rebalancing hypothesis until the 1980s, when such seasonality becomes increasingly insignificant. 1999 Elsevier Science Inc. All rights reserved. Keywords: Seasonality; Tax-loss selling; Portfolio rebalancing; Hypothesis
1. Introduction and objectives of the study The empirical phenomenon of high monthly equity returns in January relative to other months of the year was first documented by Wachtel (1942) for the New York Stock Exchange (NYSE) over the period of 1927–1942. Rozeff and Kinney (1976) confirmed Wachtel’s finding of a January seasonal from 1904 to 1974. Keim (1983) concluded that about half of the January seasonal was associated with small firms during the period of 1962–1979. Reinganum (1983) investigated the year-end tax-loss selling hypothesis that had been advanced by Wachtel (1942) to explain the unusual January returns. He noted that tax-loss selling of small capitalization stocks explains a significant part of the high January returns. The interaction between firm size and tax-loss selling in January was refined by Ritter (1988). He argued that individual investors who primarily invest in small capitalization stocks tend to sell them in December to realize capital losses and buy into such stocks in January. Eakins and Sewell (1993) and Ligon (1997) found evidence consistent with this hypothesis. * Corresponding author. Tel.: 10171-320-1535; fax: 10171-320-1557. E-mail address: [email protected] (K. Menyah) 1058-3300/99/$ – see front matter 1999 Elsevier Science Inc. All rights reserved. PII: S1058-3300(99)00004-X
TRIM→
|←
12
RevFin
p93682$$$3
p. 12
08-26-99 08:41:49
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
Haugen and Lakonishok (1988), on the other hand, argue that January seasonality could be induced by fund managers rebalancing their portfolios to either hedge or window dress their performance. In performance hedging, fund managers lock in their gains, especially from small stocks, and move into stocks that will closely replicate the features of their performance benchmark during the course of the calender year. They re-enter the market after the last reporting day to acquire undervalued small stocks that consequently appreciate in price at the start of the calender year. In window dressing, fund managers include stocks that have done well in the recent past and eliminate under-performing small stocks for year-end reporting purposes. At the start of the new reporting period, managers choose stocks, especially those of small firms that are likely to provide higher returns during the year. The prices of small stocks therefore rebound at the start of the next reporting period. Evidence consistent with this hypothesis has been reported by Ritter and Chopra (1989) and Porter et al. (1996) for the US and by Athanassakos (1992) and Athanassakos and Schnabel (1994) for Canada. In light of the above, it is clear that tax-loss selling and portfolio rebalancing could interact to generate January seasonality in markets with a December tax year-end. This makes it difficult to test the two hypotheses independently on the same data, as is usually done in US studies. This paper contributes to the literature by using data from the UK, which has a tax year (in April)1 that is different from the calender year, to separately test the tax-loss selling and portfolio rebalancing hypotheses. Specifically, the portfolio rebalancing hypothesis is tested as an explanation of January seasonality, while tax-loss selling and window dressing explanations are investigated for April seasonality. This contrasts with previous UK papers that either focused on tax effects (Reinganum & Shapiro, 1987) or size effects (Levis, 1985; Corhay et al., 1988; Clare et al., 1995) to the neglect of other explanations of January and April seasonalities. The next section describes the evolution of UK capital gains taxation and assesses its impact on tax related explanation of seasonality. The data and sample selection methods are described in section 3. Using a GARCH-M model, section 4 identifies the months and portfolios that show evidence of seasonality during the sample period. Section 5 examines the extent to which January and April seasonalities could be explained by either the portfolio rebalancing hypothesis or tax-loss selling. A summary of the study and its findings are presented in the final section. 2. UK capital gains taxation Compared to the US, which introduced capital gains taxation in 1913, it was not until half a century later that a short-term speculative gains tax became law in the UK.2 A comprehensive capital gains tax was introduced in 1965 after years of public debate.3 The major capital gains tax law changes covered by this study and their possible effects on year-end trading have been summarized in Appendix 1. It is recognized, however, that the effect of legislation on trading behavior is an empirical issue determined by the actions of marginal investors in an efficient market, as noted by Hamada and Scholes (1985)4 and influenced by inframarginal investors5 if the market is not deemed to be efficient. Before the introduction of the capital gains tax
TRIM→
|←
RevFin
p93682$$$3
p. 13
08-26-99 08:41:49
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→|
13
in 1965, relative prices in January and April could have been determined by taxexempt marginal investors, such as pension funds, since taxable investors stood to lose if they traded towards the end of the fiscal year from 1962 onwards and subjected themselves to the short-term speculative gains tax. A non-tax explanation is therefore more likely to be consistent with any seasonalities observed before 1965. After 1965, taxable investors who owned around 73%6 of equity constituted the inframarginal investors whose trading could have affected year-end pricing if they exploited pricing anomalies to their advantage (Seyhun, 1993).7 The extent of their trading activities could have been limited by the restrictive nature of the loss offset provisions, which did not allow short-term losses to be set off against long-term gains. The opportunities for tax year-end trading strategies were increased, however, when the distinction between short- and long-term capital gains tax was abolished in 1971. Taxable investors’ incentive for year-end trading could have increased because of the provision that all losses (short- and long-term) could be set off against all gains. The introduction of indexation in 1982 and the rebasing of capital gains tax in 1988 reduced the amount of taxable realized gains and could have minimized the extent of tax-loss selling by inframarginal investors. The extent to which the above legislative changes could have induced April seasonality is verified empirically in subsequent sections. 3. Data and sample selection The study uses the London Business School Share Price Database for the period between 1955 and 1992. The database contains information on about five and a half thousand companies that were quoted on the London Stock Exchange between 1955 and 1992. The sample used excluded firms quoted on the Unlisted Securities Market, the Third Market, and OTC market.8 Companies that had not entered the database by March 1988 were excluded because the major capital gains tax changes that form the focus of this study had already become law. The annual sample of companies ranged from 906 in 1958 to 2,056 in 1976 with a mean of 1,508.9 To create size portfolios, all the stocks in the database at the beginning of every year (from 1955) were sorted by market value. Ten size-based portfolios were created. During the sample period, the average market value for the smallest decile portfolio was £1.24 million, while that of the largest decile portfolio was £521 million with a median portfolio market value of £8.6 million. For each portfolio, the monthly returns of the member stocks were equally weighted to obtain the average returns. The process was repeated for each year up to 1992. We also used two portfolios that are not related to size—average portfolio of all stocks in our sample each year as well as the Financial Times Institute of Actuaries (FTA) index—in order to assess the extent to which the averaging of returns can affect seasonality. 4. Excess return seasonality and size under capital gains tax regimes Analysis of raw return data shows January and April to have the highest monthly returns for all deciles during the period of 1956–1992.10 Tests of equality of monthly returns based on two-way ANOVA tests and Friedman Rank Sums reinforce the
TRIM→
|←
14
RevFin
p93682$$$3
p. 14
08-26-99 08:41:49
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
inequality of monthly returns. Analysis based on raw returns, however, lack rigor because, as Roll (1983) has pointed out, they do not adequately account for the effect of risk on stock returns. A variety of risk adjustment measures have been used in UK studies of return seasonality. Levis (1985) tested for excess return over the riskfree rate with the Sharpe-Lintner Capital Asset Pricing Model (CAPM). Corhay et al. (1988) used different measures of risk to adjust returns in their study of seasonal risk premium and concluded that variance of return is a better proxy for risk than b or residual risk. Clare et al. (1995) modeled the FTA index return as an ARMA process and used GARCH-M(p,q) to account for risk. They did not, however, adjust for the risk free return. This paper extends previous studies by testing for excess return seasonality using FTA index returns, size-based portfolios of company returns, and average returns of stocks in the London Business School Shareprice Database. The following GARCH-M(p,q) specification is used in the tests to control for timevarying variances. 1
Rpt 2 Rft 5 b0 1 b1h2t 1
N
12
i51
m51
o bi(Rpt 2 i 2 Rft 2 i) 1 o Cmdtm 1 et
ht 5 g0 1 g1e2t 2 1 1 g2ht 2 1 1
(1)
11
o C*mdtm
(2)
m51
where Rpt is the return on a portfolio (index or average return), Rft is the risk free rate and Cmdm is the dummy variable to capture monthly excess returns in Eq. 1. Eq. 2 is the specification of the GARCH-M(p,q) model with appropriate dummy variables to control for changing monthly variances. Several lag structures were investigated for the mean equation as well as the conditional variance equation. On the basis of diagnostic tests, a six-period lag was considered adequate for the mean equation and a GARCH-M(1,1) for the conditional variance equation.11 The tests of seasonality for the sample period using the various portfolios are reported in Table 1. The estimation of the variance term in the mean equation gives a reward to risk ratio of 0.24, which is twice that obtained by Clare et al. (1995). The magnitudes of the excess return range from 1.6% for the fourth smallest portfolio to 2.4% for the third smallest portfolio. Significant January excess return seasonality is detected in all but the two largest portfolios and the FTA index. For April, seasonality is only observed in the three smallest portfolios. In Table 1, an excess return seasonality of 1.68% is detected in January for the average sample portfolio. The FTA and average portfolio returns for April show no evidence of seasonality.12 These findings reinforce the presence of January seasonality and suggest that April excess return seasonality is more likely to be induced by firm size.13 The likely explanations of seasonality in January and April are therefore investigated in the next section. 5. Determinants of January and April seasonalities 5.1. Is January seasonality consistent with the portfolio rebalancing hypothesis? Since the January seasonality detected in the preceding section cannot be attributed to tax-loss selling, the portfolio rebalancing hypothesis is investigated as a possible explanation for the finding. Even though January seasonality is associated with perfor-
TRIM→
|←
RevFin
p93682$$$3
p. 15
08-26-99 08:41:49
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→|
15
Table 1 GARCH-M(1,1) test of excess return seasonality based on 10 size-based portfolios for the period 1956– 1992 Relevant mean equation parameter estimates, t statistics, and p values 1/2
Residuals
Portfolio
Constant
h
Jan
April
Skewness
Kurtosis
Smallest
20.0074 (21.073) (0.28)
0.249 (1.897) (0.05)
0.0218 (3.876) (0.0001)*
0.0129 (2.020) (0.0433)**
20.2587
5.6373
20.0073 (21.160) (0.24)
0.249 (1.900) (0.05)
0.0192 (3.482) (0.0004)*
0.0128 (1.836) (0.0662)***
20.4239
20.0097 (21.381) (0.166)
0.249 (2.042) (0.041)
0.0242 (4.093) (0.0000)*
0.0115 (1.698) (0.0893)***
20.6731
20.0050 (20.728) (0.4663)
0.249 (2.010) (0.0444)
0.0164 (2.543) (0.0109)*
0.0084 (1.185) (0.2358)
20.0078 (20.9572) (0.3384)
0.249 (1.9336) (0.0531)
0.0178 (2.6610) (0.0077)*
0.0073 (0.9736) (0.3302)
20.3831
20.0078 (21.006) (0.3141)
0.249 (1.8879) (0.0590)
0.0215 (3.2161) (0.0012)*
0.0077 (0.9533) (0.3403)
20.3258
20.0106 (21.288) (0.1975)
0.249 (1.843) (0.0653)
0.0211 (2.974) (0.0029)*
0.00905 (1.100) (0.2709)
20.3195
2
3
4
5
6
7
(0.0276)
(0.0003)
(0.0000) 4.6781 (0.0000) 4.7578
(0.0000)
(0.0000)
0.1890
5.4160
(0.1074)
(0.0000)
(0.0011)
(0.0055)
(0.0065)
4.2653 (0.0000) 4.5539 (0.0000) 4.4389 (0.0000) (continued)
mance hedging (because bonuses are paid at the end of the calender year), it could also be caused by window dressing if a fund’s accounting year is December. To investigate the hypothesis, firms are ranked by market value at the beginning of the preceding calender year (beginning in 1955). The price change between the November of the current year and 5 and 11 months previously is calculated to obtain winning and losing stocks as in Lakonishok and Smidt (1986). For 1959 for instance, we divided the November price by the June 1959 and December 1958 month-end prices to obtain the price relatives for the 5- and 11-month holding periods, respectively. Using the procedure by Bolster et al. (1989), stocks that yielded a holding period value of more than 1 were classified as winners and those with a value of less than 1 as losers. Two portfolios of winning and losing stocks were formed after discarding those with no price changes. Within each of the portfolios, stocks were ranked by market value, and two further portfolios based on size were created. Stock returns
TRIM→
|←
RevFin
16
p93682$$$3
p. 16
08-26-99 08:41:49
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
Table 1 (continued ) Relevant mean equation parameter estimates, t statistics, and p values Portfolio
Constant
h
8
20.0086 (21.0733) (0.2831)
9
Largest
Av. Portfolio
FTA Index
1/2
Jan
April
0.249 (1.9581) (0.0502)
0.0173 (2.2112) (0.0270)**
0.0095 (21.0483) (0.2944)
0.249 (1.7948) (0.0726)
20.0081 (20.7996) (0.4238)
Residuals Skewness
Kurtosis
0.0029 (0.3531) (0.7239)
20.1973
4.8573
0.0101 (1.2527) (0.2103)
(0.0038 (0.4210) (0.6736)
20.2596
0.249 (1.6509) (0.0987)
0.0064 (0.7872) (0.4311)
0.0025 (0.2713) (0.7861)
20.1984
20.0067 (20.8895) (0.3736)
0.249 (1.9848) (0.0471)
0.0168 (2.4899) (0.0127)*
0.0074 (0.9722) (0.3309)
20.3606 (0.0021)
(0.0000)
20.0105 (20.995) (0.3192)
0.249 (1.6789) (0.0931)
0.0093 (1.1458) (0.2518)
0.0011 (0.1196) (0.9047)
0.7188
10.0659
(0.0000)
(0.0000)
(0.0929)
(0.0270)
(0.0911)
(0.0000) 4.9289 (0.0000) 4.7622 (0.0000)
5.6145
The estimated equations are: 1
Rpt 2 Rft 5 b0 1 b1h2t 1
7
12
i52
m51
o bi(Rpt 2 1) 1 o
ht 5 g0 1 g1e2t 2 1 1 g2ht 2 1 1
11
o
m51
Cmdtm 1 et
C*m dtm
The first and second numbers under the parameter estimates are the coefficients and their t values, respectively. The third number is the p value. * Statistically significant at 1% or better. ** Statistically significant at 5% or better. *** Statistically significant at 10% or better.
for January and April in the year following that used in generating the winner or loser portfolios were collected and averaged for each year to obtain the data for testing the hypothesis. The descriptive statistics of the data used in the tests are shown in Table 2. The capitalization of large firm winners (averaging £114 million for the 5-month holding period) is about 40 times that of small firm losers, which average £2.6 million for the same holding period. The average portfolio return for small firm losers is higher in January (4.1% for the 11-month holding period) than that of large firm winners (which averages 2.7%). The April returns, on the other hand, indicate that small firm losers with a return of 2.6% (for the 5-month holding period) have lower returns than large firm winners, which generate 3.2%. The portfolio rebalancing hypothesis is supported if the return on the small firm loser portfolios is significantly higher than the return on the portfolio of large firm
TRIM→
|←
RevFin
p93682$$$3
p. 17
→|
08-26-99 08:41:49
K. Menyah / Review of Financial Economics 8 (1999) 11–24
17
Table 2 Descriptive statistics of data on winning and losing portfolios for the period 1956–1992
5-Month holding period Large firm winners Small firm losers 11-Month holding period Large firm winners Small firm losers
Ave. firm size £ million
Ave. portfolio return
Max. return
Min. return
January
April
January
April
January
April
114.4 2.68
3.00 4.2
3.2 2.6
14.7 32.0
9.8 11.6
210.7 210.3
27.8 210.5
94.8 3.07
2.7 4.1
3.2 3.1
15.9 27.6
9.9 12.7
215.5 211.5
27.1 26.0
All returns are in percentages. Thirty-six annual observations were used in the calculations for both holding periods.
winners. The results of test of differences based on a parametric t test and the nonparametric Wilcoxon Signed Rank Test are reported in Table 3. The January results for the period of 1956–1965 show that the small firm losing portfolio returns are significantly higher (by at least 1.7%) than that of the large firm winning portfolios for both holding periods. Such findings are consistent with performance hedging as well as window dressing by fund managers with a December accounting year-end during the period before the introduction of capital gains tax. The April results, however, are not supportive of window dressing by fund managers with a March yearend because they show that the returns to large firm winners are higher (by at least 2%) than those to small firm losers. For the 1966–1992 period, the portfolio rebalancing hypothesis is not supported because the differences are not statistically significant. In order to ascertain the reasons for the insignificance of the results, the capital gains tax period was further analyzed by subdividing the period: 1966–1979 and 1980–1992. Estimates of the significance of January seasonality based on Eqs. 1 and 2 confirmed its significance during the 1966–1979 period but not in the second subperiod. The insignificance of the results for the 1980s is consistent with the findings of Alford and Guffey (1996) that seasonality has been disappearing in the US and UK equity markets since 1983. Tests of the portfolio rebalancing hypothesis in January for the period of 1966–1979 showed that they are statistically significant for both the 5- and 11-month holding periods. For the 5-month holding period, the difference between small firm losers and large firm winners was about 1.7%, which is significant at 10% using the non-parametric Wilcoxon Signed Rank Test. The difference in return for the 11month period is 2.53%, which is significant at 7% on the basis of the non-parametric test. These findings suggest the portfolio rebalancing hypothesis appears to be the main explanation for January seasonality in the UK equity market at least until the early 1980s. 5.2 Did firm size interact with tax-loss selling to induce seasonality? The analysis in section 4 also shows that April seasonality is concentrated in small firms, as evidenced in Table 1. We therefore explored how firm size and tax-loss selling may have interacted to induce high monthly returns in April. The tax-loss
TRIM→
|←
RevFin
18
p93682$$$3
p. 18
→|
08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24
Table 3 Tests of performance hedging and window dressing hypotheses Test type 5-Month holding period January Parametric Non-parametric April Parametric Non-parametric 11-Month holding period January Parametric Non-parametric April Parametric Non-parametric
Before capital gains tax (1956–1965)
After capital gains tax (1966–1992)
Return diff
Return diff
t Stats
1.7 1.8
2.52 —
22.5 22.4
22.67 —
1.9 1.9
2.99 —
22.0 22.2
22.16 —
p Value
0.036* 0.044*
0.9 0.8
0.028 0.44
20.01 0.07
0.017* 0.042* 0.062 0.097
t Stats
p Value
0.97 —
0.34 0.24
20.02 —
0.99 0.91
1.1 0.8
1.42 —
0.17 0.2
0.5 0.4
1.36 —
0.19 0.25
The parametric test is a t test of differences in means that does not assume equality of sample variances. The non-parametric test is the Wilcoxon Signed Rank Test of differences between medians. “Return Diff.” refers to either the difference between the mean or median for the small firm loser portfolio and large firm winner portfolio. For the portfolio rebalancing hypothesis to hold, the return on the small firm loser portfolio should be higher than that on the large firm winner portfolio. p values with aesterisks (*) have the correct sign and are also significant at 5% or better.
selling hypothesis implies that prices of stocks that depreciate towards the end of the tax year should rebound in the following year. This means that returns on small company stocks that depreciate in price before the end of the tax year will exhibit higher returns relative to those of larger companies that had appreciated in price at the start of the tax year. The data for the empirical test is the same as that used in the preceding subsection. If tax-loss selling of small firms induces April seasonality, then the difference in return between the portfolio of small firm losers and large firm winners should be positive and statistically significant during the period of capital gains taxation. This hypothesis was tested by regressing the difference in returns between loser and winner portfolios against an intercept and a dummy variable for the period of 1956–1992.14 The dummy variable takes a value of 1 during the capital gains tax period (1966–1992) and 0 during the period without capital gains taxation.15 If small firm losers out-perform large firm winners in April because of tax-loss selling, then the coefficient of the dummy variable should be statistically different from 0 at conventional levels of significance. The results of the estimates reported in Table 4 show that the dummy coefficient is statistically significant from 0 for both holding periods when taxable investors had the incentive to generate losses to offset gains so as to avoid paying taxes at the higher short-term rate. These findings are consistent with tax-loss selling inducing high returns
TRIM→
|←
RevFin
p93682$$$3
p. 19
08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→|
19
Table 4 Regression equation tests of the tax-loss selling hypothesis Constant Tests with April portfolio returns 5-Month holding periods 11-Month holding periods Tests with January portfolio returns 5-Month holding periods 11-Month holding periods
20.025 (22.30) 20.020 (22.69) 0.017 (1.13) 0.020 (1.60)
Dummy coefficient 0.025 (1.99)** 0.025 (2.95)* 20.008 (20.44) 20.009 (20.60)
Adjusted R2
7.8% 18.0%
0.0% 0.0%
The following equation was estimated: Rloser,t 2 Rwinner,t 5 a 1 bD 1 et, where the dependent variable is the difference between the small firm loser and large firm winner portfolios and the dummy variable takes on a value of 0 during the period before capital gains taxation, 1956–1965, and a value of 1 during the period of capital gains taxation. t statistics are in parenthesis. *Dummy variable coefficient is significant at 1%. **Dummy variable coefficient is significant at 5%.
in small company stocks in April. A second regression was estimated with the difference in portfolio returns between small firm losers and large firm winners in January. The results, also shown in Table 4, reject the suggestion that January seasonality is induced by the tax-loss selling of small firms’ stocks since all the dummy variable coefficients are statistically insignificant. It is therefore reasonable to conclude that the tax-loss selling hypothesis associated with small firm stocks at the beginning of the tax year as proposed by Ritter (1988) is confirmed for the UK. This is related to the previous results of Reinganum and Shapiro (1987), who found April seasonality to be consistent with tax-loss selling but found their significant January returns “difficult to interpret.” Their results could be attributed to the fact that they did not control for firm size in the construction of their loser and winner portfolios and were therefore unable to investigate the hypothesis tested in this paper. 6. Conclusion This paper investigated monthly seasonality of UK equity returns during the 1956– 1992 period. Using a GARCH-M model, evidence of January seasonality is found for all but the two largest size portfolios. On the other hand, April seasonality is detected in only the three smallest size portfolios. Since the tax explanation is not relevant for UK January seasonality, the portfolio rebalancing hypothesis was investigated. Evidence consistent with portfolio rebalancing in January was detected from 1956 (well before the introduction of capital gains taxation) to the early 1980s, when January seasonality starts to become statistically insignificant. The portfolio rebalancing hy-
TRIM→
|←
RevFin
20
p93682$$$3
p. 20
08-26-99 08:41:50
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
pothesis was rejected as a possible explanation for April seasonality. On the other hand, the tax-loss selling hypothesis of Ritter (1988) that small firms’ stock rebound at the start of the tax year was tested in relation to April seasonality. The findings support the proposition that April seasonality of small firms is associated with taxloss selling. This is reinforced by the fact that similar tests rejected the tax-loss selling explanation of January seasonality. Acknowledgments The financial support of the Business Faculty Research Committee and the programming assistance of Pari Jahankhani are gratefully acknowledged. The comments of an anonymous referee and those of Krishna Paudyal, which have significantly helped to focus the paper, are gratefully acknowledged. Notes 1. April is the tax year-end for individuals in the UK. For companies, including institutional investors, the tax year-end is the same as their individual accounting year-end. December and March are the most popular accounting year-ends for most companies and institutions. 2. According to Ilersic (1962), a very active bull market for stocks from October 1959 to January 1960 combined with a property market boom made public opinion receptive to the idea of a tax on speculative gains. 3. A Royal Commission on the Taxation of Profits and Income (1955) had argued against it in its majority report. A three-man minority report, however, advocated the taxation of capital gains, especially on securities, which in their view increased taxable capacity by increasing the power to spend and save. Chancellor James Callaghan relied on the minority report of the Royal Commission on the Taxation of Profits and Income to introduce a comprehensive capital gains tax in 1965. 4. Hamada and Scholes (1985, p. 190), define the “marginal” investors as the class of investors setting the relative prices at the margin in the sense that they are indifferent to purchasing or supplying any more of those securities at those prices and after having considered all the tax implications. There is no more tax gain nor tax loss for marginal investors at the given relative prices. 5. “Inframarginal” investors will have a tax gain or loss by purchasing or selling more of those securities at the given relative prices, unless there are regulatory or tax constraints that prohibit such tax gaming (Hamada and Scholes, 1985). 6. The figure was obtained from a table prepared by the Central Statistical Office, 1994. 7. The tax-loss selling hypothesis, however, implies the absence of weak-form market efficiency because traders can predict the direction of price movement at the turn of the calender or fiscal year.
TRIM→
1962 A tax on short-term speculative gains payable at the marginal tax rate on stocks bought and sold within six months was introduced. There was no taxation of capital gains realized after 6 months. 1965 A comprehensive capital gains tax system was introduced. Short-term gains tax was payable at the marginal rate for stocks held for less than twelve months. Short-term losses could only be offset against short-term gains. Long-term gains on stocks held for more than twelve months were taxed at rates below the marginal rates. Long-term losses could only be offset against long-term gains. 1971 Dual rate capital gains taxation abolished. All capital gains— and long-term—to be taxed at a special rate for all investors.
Main capital gains tax legislation 0 0 0
88.75} 56.25}5 56.25}
0 0 0
91.25} 40.00}6 30.00}
Individuals Industrial companies and banks4 Investment and unit trusts firms
Individuals Industrial companies and banks Investment and unit trusts firms
Individuals Industrial companies and banks Investment and unit trusts firms
30 30 30
08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24 (continued)
Incentive to all taxable investors to undertake year-end trading due to absence of restrictions on loss offsets.
p. 21
Investment and unit trusts could realize short-term losses as well because of identical tax rate on all gains.
Likely year-end trading by individuals and companies of stocks with long-term gains and losses because of lower tax rates.
Taxable investors to hold stocks for more than 6 months to avoid the tax rather than engage in tax loss selling.
Likely trading impact
p93682$$u3
30 30 30
91.25 56.25 30.00
88.75} 53.75}3 53.75}
Highest tax rates after change (%)
RevFin
Individuals Industrial companies and banks Investment and unit trusts
Investors affected1
Highest tax rates prior to change (%)2
Appendix 1 The evolution of UK capital gains tax legislation and likely impact on year-end stock trading
|← →|
21
TRIM→
40 35 40
Incentive for year-end tax motivated trading due to increase in marginal tax rates on capital gains.
2
p. 22
Pension funds did not pay either dividend or capital gains tax during the period under review for UK investments. These were the tax rates in force before the date of change in the tax described in column 1. 3 These rates applied to disposal of assets that had been held for less than 6 months. 4 This includes insurance companies that were usually taxed as other companies. From 1920, investment income apportioned to policyholders was taxed at 37.5% until the corporation tax rate fell below this rate in 1986. 5 The changes in rates for industrial companies, banks, and unit and investment trusts reflect the changes in tax rates. 6 These were the tax rates on short-term gains in force before all capital gains were made taxable at the same rate. 7 From April 1982 to March 1985, gains on stocks held for more than 12 months attracted an inflation indexation allowance that reduced the taxable gain. The holding period qualification for the indexation allowance was abolished in April 1985.
30 30 30
Likely trading impact
p93682$$u3
1
Individuals7 Industrial companies and banks Investment and unit trust holders
Highest tax rates after change (%)
RevFin
1982 & 1988 Indexation allowance for inflation was introduced in 1982. Only real gains were taxable. In 1988, all capital gains that had accrued to stockholders since the introduction of the tax in 1965 were made free of tax. Only gains accruing since 1982 were taxable. All capital gains were made taxable at the marginal tax rate of investors with the abolition of the preferential capital gains tax rate in 1988.
Investors affected1
Highest tax rates prior to change (%)2
22
Main capital gains tax legislation
Appendix 1 (continued)
|← 08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→| TRIM→
|←
RevFin
p93682$$$3
p. 23
08-26-99 08:41:50
K. Menyah / Review of Financial Economics 8 (1999) 11–24
→|
23
8. The study uses companies quoted on the main market known as the Official List. The Third Market was abolished in 1989, and the USM ceased to exist in 1995. Companies that transferred to the main market either from the Third Market or the USM were included in the sample. 9. Our sample compares reasonably well with other UK studies that included stocks from other market segments. Reinganum and Shapiro’s sample ranged from 1,021 to 2,228, while that of Levis ranged from 1,500 to 2,400. 10. The January and April returns average 3.5 and 3.2%, respectively, for the sample period. This contrasts with average returns ranging from 0.03 to 1.6% (for July and February, respectively) and for 4 months (June, September, October, and November) that have negative average returns. Our results (available from the author on request) are broadly consistent with previous UK findings by Levis (1985) for the period of 1956–1980. The difference in return between the smallest and largest market value portfolios is also significant at the 5% level. This reinforces the view that small firms earn higher returns than large firms. 11. This specification is the same as that used by Clare et al. (1995). 12. Clare et al. (1995) found evidence of January and April seasonalities in the logarithmic changes in the FTA index levels. Their index returns, however, exclude capital gains. We use the logarithmic total returns in our analysis. Our model specification also focuses on excess return over the risk-free rate ,while Clare et al. did not include the risk-free rate. These differences in data used may explain why our results differ from theirs. 13. To examine the robustness of raw return April seasonality detected for the subperiod of 1956–1964, we estimated Eqs. 1 and 2 with subperiod data but found no evidence of seasonality. This finding is consistent with Reinganum and Shapiro (1987), who found no evidence of seasonality for the same subperiod. 14. The period before the introduction of capital gains tax was defined to coincide with that used by Reinganum and Shapiro (1987). When the capital gains tax period was allowed to start in 1965, the results did not change. 15. The regression equation is the same as that estimated by Reinganum and Shapiro (1987). Their winner and loser portfolios, however, were not based on firm size and so could not test how size and tax could interact to induce April return seasonality. References Alford A., & Guffey, D.M. (1996). A re-examination of international seasonalities. Review of Financial Economics 5, 1–17. Athanassakos, G. (1992). Portfolio-rebalancing and the January Effect in Canada. Financial Analysts Journal 48 (November/December), 67–78. Athanassakos, G., & Schnabel, J. A. (1994). Professional portfolio managers and the January Effect: theory and evidence. Review of Financial Economics 4, 79–91. Bolster, P. J., Lindsey, L. B., & Mitrusi, A. (1989). Tax-induced trading: the effect of the 1986 Tax Reform Act on stock market activity. Journal of Finance 44, 327–344.
TRIM→
|←
24
RevFin
p93682$$$3
p. 24
08-26-99 08:41:50
→|
K. Menyah / Review of Financial Economics 8 (1999) 11–24
Central Statistical Office (1994). Share Register Survey End 1993. London: Her Majesty’s Stationery Office. Clare, A. D., Psaradakis, Z., & Thomas, S. H. (1995). The analysis of seasonality in the U.K. equity market. The Economic Journal 105, 398–409. Corhay, A., Hawawini, G., & Michel, P. (1988). The pricing of equity on the London Stock Exchange: seasonality and size premium. In M. Levis (Ed.), Stock Market Anomalies (pp. 197–212). Cambridge: Cambridge University Press. Eakins, S., & Sewell, S. (1993). Tax-loss selling, institutional investors and the January Effect: a note. Journal of Financial Research 16, 377–384. Great Britain (1955). Final Report: Royal Commission on the Taxation of Profits and Income. CMND 9474. Hamada, R. S., & Scholes, M. S. (1985). Taxes and corporate financial management. In E. I. Altman & M. G. Subrahmanyam (Eds.), Recent Advances in Corporate Finance (pp. 189–226). Homewood, IL: Richard D Irwin. Haugen, R., & Lakonishok, J. (1988). The Incredible January Effect. Homewood, IL: Dow Jones-Irwin. Ilersic A. R. (1962). The Taxation of Capital Gains. London: Staples Press. Keim, D. B. (1983). Size-related anomalies and stock market seasonality: further empirical evidence. Journal of Financial Economics 12, 13–32. Lakonishok, J., & Smidt, S. (1986). Volume for winners and losers: taxation and other motives for stock trading. Journal of Finance 41, 951–974. Levis, M. (1985). Are small firms big performers? The Investment Analyst 76, 21–27. Ligon, J. A. (1997). A simultaneous test of competing theories regarding the January Effect. The Journal of Financial Research 20, 13–32. Porter, D. C., Powell, G. E., & Weaver, D. G. (1996). Portfolio rebalancing, institutional ownership, and the small firm-January Effect. Review of Financial Economics 5, 19–29. Reinganum, M. R. (1983). The anomalous stock market behaviour of small firms in January: empirical tests for tax-loss-selling effects. Journal of Financial Economics 12, 89–104. Reinganum, M. R., & Shapiro, A. C. (1987). Taxes and stock return seasonality: evidence from the London Stock Exchange. Journal of Business 60, 281–295. Ritter, J. R. (1988). The Buying and selling behaviour of individual investors at the turn of the year. Journal of Finance 43, 701–717. Ritter, J. R., & Chopra, N. (1989). Portfolio rebalancing and the turn-of-the-year. Journal of Finance 44, 149–166. Roll, R. (1983). Vas ist das? The turn-of-the-year effect: anomaly or risk measurement? Journal of Portfolio Management 9, 18–28. Rozeff, M. S., & Kinney, W. R., Jr. (1976). Capital market seasonality: the case of stock returns. Journal of Financial Economics 3, 379–402. Seyhun, H. N. (1993). Can omitted risk factors explain the January Effect? A stochastic dominance approach. Journal of Financial and Quantitative Analysis 28, 397–402. Wachtel, S. (1942). Certain observations on seasonal movements in stock prices. Journal of Business 15, 148–193.
TRIM→