INTRODUCTION
The portfolio management has been considered as one of the most studied topics
in finance area (Ahortor and Olopoenia, 2010; Angabini
and Wasiuzzaman, 2011; Ferruz et al., 2007;
Jasemi et al., 2011; Krishnasamy
et al., 2006; Lye, 2011, 2012;
MatallinSaez, 2009; Ozun and Cifter,
2007; Rahman et al., 2006; Rhaiem
et al., 2007). Especially, over the past years, researchers have
turned their attention on studying the turnoftheyeareffect or January effect
which implies the average rate of return to stock in the month of January is
higher than in any other month of the year. Whether, January effect exists or
not would have significant implication to the portfolio management. Gultekin
and Gultekin (1983) studied January return patterns in 17 countries including
the United States. They documented that there were high returns in January than
in nonJanuary months for all the countries they studied. However, for the period
they studied, the effect was bigger in the 16 nonUnited States markets. Kato
and Shallheim (1985) examined excess return in January and the relationship
between size and the January effect for the Tokyo stock exchange. They found
no relationship between size and return in nonJanuary months. However, they
discovered excess returns in January and the strong relationship between returns
and sizes, with the smallest firms returning 8% and the largest less than by
3%.
An explanation that has been arguing for the high returns in January is a taxselling
hypothesis by Chan (1986) and De
Bondt and Thaler (1987). A popular suggestion of investment advisers, at
the end, is to sell securities for which investors has incurred substantial
losses before the end of the year and purchase a high beta security. This creates
tax loss for investor. If the tax loss is substantial, it should more than cover
transaction costs. Since the selling is in late December and the purchasing
in early January, the argument is that prices are dispersed at the end of December
and rebound in January, creating high returns in January. Reinganum
(1983) and Branch (1977) found that the purchase
of a security that has declined substantially by December has excess return
in January. For example, Branch (1977) analyzed a trading
rule that involved the purchase of a security that reached its annual low in
the last week of trading in December. He found that these securities rose faster
in the first four weeks of the New Year than the market as a whole, with very
little difference in risk. He obtained average returns 8% above the market for
a fourweekholding period. Reinganum (1983) found similar
results.
For this to be partial explanation of the January seasonal, it needs to be
true that small stocks are unusually high percentage of the stocks that are
candidates for tax swapping. This is exactly what Reinganum
(1983) discovered. However, Reinganum argued that it was not the full explanation
since he still found a January effect for firms that show gains in the prior
year. Securities that were being sold for taxloss purposes were more likely
to be at the bid in December. Thus, the taxselling hypothesis explanations
are likely to be partially measuring the same effect. Several studies have provided
evidence that is difficult to reconcile with the taxselling hypothesis. Jones
et al. (1987) studied a period from 1821 to 1971 before the introduction
of the income tax. They found a January effect that the affect is not significantly
different from the January effect found after the introduction of the income
tax. Similarly, Japan and Belgium, which were found to have a January effect,
do not have a capital gain tax. Furthermore, Australia has a nonDecember tax
year so that if the extra returns were tax related, the effect should be present
in a different month.
Ritter and Chopra (1989) found patterns in small risky
firm returns were high when comparing to the big firms and the riskreturn relation
has a January seasonal. Corhay et al. (1987)
accepted this conclusion. Moreover, the January effect is postulated as portfolio
rebalancing by investors at the end of the year. The portfolio rebalancing effect
was hypothesized by Haugen and Lakonishok (1987) and Ritter
(1988). This hypothesis states that the high returns on risky securities
in January are caused by systematic shifts in the portfolio holdings of investors
at the turn of the year. Producing observed high January returns on small and
risky stocks.
Against this background, the objectives of this study were three folds as follows: In the first place, we investigated the existence of monthly pattern or seasonal effect in emerging markets, using a more extensive and latest data sets than any previous studies. Secondly, we examine the persistent of monthly effect in emerging market data by forming 20 portfolios according to size and risk dimension. Thirdly, was to investigate portfolio rebalancing effect. Moreover, this paper gives a substantial contribution to the seasonal anomalies literature in emerging market and gives expected benefits are: To provide the information and help investors (especially, emerging market investors) set up their investment and hedging strategies during hard times. To be able to provide the usefulness for investors who can use the evidence and invest on the stock market.
MATERIALS AND METHODS
The data used for this study are monthly rate of returns including dividends and capital gains adjusted for stock transactions through splits and stock dividend for all common stocks traded in 20 countries in emerging markets. The securities are classified by Morgan Stanley Capital International (MSCI) retrieved from Thompson Reuters Datastream system including alphabetically: Argentina, Brazil, Chile, China, Colombia, India, Indonesia, Israel, Malaysia, Mexico, Pakistan, Peru, Philippines, Russia, South Africa, Sri Lanka, Taiwan, Thailand, Turkey and Venezuela covering the period of January 1999 through September 2007.
The riskfree rate is the Thai longterm government bond and the market index
is emerging market price index retrieved from the datastream. Additionally,
we collected the corresponded market value. Total asset in the twenty emerging
countries are 8966 securities. There are several problems in dealing with returns
when delisted company exists. Reasons for delisting are bankruptcy, merger and
acquisition and liquidation which is very complicated in calculating returns
when there are large numbers of assets (Vaihekoski, 2000).
To avoid this error, we first simply screen to ensure that returns that are
dead in three consecutive trading days are removed.
After the screening procedure is correctly adjusted, we then divide the assets into two subperiods. The first period is from year 1999 to 2003 and the second period from 2004 to 2007. The two periods will applied in the regression model to test riskreturn relation in regards of stock’s market value and risk dimension. The reason behind why the period of 19992007 is selected is 1). The selected period is in the Global Financial Crisis period, which was derived from the liquidity shortfall of U.S. banking system and influenced all countries around the world; 2). The selected period is in the period of impact from Asian financial crisis in 1997; 3). The period of study is in the range of the beginning of the subprime crisis. These events may have a significant impact on the emerging markets, which are sensitive to the major events. Moreover, the reason of dividing the periods to be 2 subperiods because we need to study more specifically on the January effect in the smaller section, not just only the overall period in order to find if there is any major impact or significant pattern that may affect the result of study.
The returns are calculated in percentage term as follows:
Where:
R_{t} 
= 
Monthly return on a stock in period t 
P_{t} 
= 
Current stock price in period t 
P_{t1} 
= 
Stock price at the end of the preceding month 
The market returns are computed as follows:
Where:
R_{mt} 
= 
Monthly return on the market index in period t 
M_{t} 
= 
Current market price in period t 
M_{t1} 
= 
Market price at the end of the preceding month 
Thai government longterm bond returns are computed as follows:
Where:
R_{ft} 
= 
Monthly risk free return in period t 
RF_{t} 
= 
Current riskless rate in period t 
RF_{t1} 
= 
Riskless rate at the end of the preceding month 
The portfolios will be used in this paper are formed by Market size quintiles
and beta quartiles. In total, there will be twenty types of portfolio involved
in the investigation. For example, a portfolio with the smallest size quintile
with leastrisky firm quartile and another portfolio could be the largest market
size quintile with very risky firm. To examine the riskreturn relation, the
approach of Ritter and Chopra (1989) regression is applied.
For each size quintile, we regress monthly equally weighted portfolio returns
on a January intercept dummy variable D_{t}^{jan}, beta β_{pt}
and a crossproduct term β_{pt}D_{t}^{jan} which
produces a January slope dummy variable:
The January dummy variable, D_{t}^{jan}, takes on the value
of one in January and zero in other months. The equally weighted portfolio betas,
β_{pt}, have been estimated by using Fama and
Macbeth (1973) twostep procedure. This model can evidently visualize the
existence of January riskreturn relation.
The turn of the year effect is caused by factors that have been argued from
the previous literatures. The first hypothesis is taxloss selling effects but
may not have been fully interested in our simple measure. For example, Chan
(1986) and De Bondt and Thaler (1987) have found
that the effect appear to exist in January at least five year after a loss is
incurred. A second hypothesis that has been arguably discussed by many authors
is the high return on small firms in January that is the risk missmeasurement
hypothesis Hillion and Sirri cited in Ritter and Chopra
(1989) and Rogalski and Tinic (1986) found that
there is January seasonal in the sensitivity of small firms to market risk,
with the betas of small firms being higher in January than in nonJanuary. A
third hypothesis is the portfoliorebalancing hypothesis of Haugen
and Lakonishok (1987) and Ritter (1988). They argued
that portfolio managers tend to “Window dressing” their stocks out
during the fiscal year end. That is, they rebalance their portfolios prior to
year end to remove securities which might be financially bad if they appeared
on yearend balance sheet.
Portfoliorebalancing hypothesis is tested. For each quartile of realized January
market returns, there is a positive relation between beta and excess returns.
The hypothesis predicts positive slope coefficients. Our regression model used
by Ritter and Chopra (1989) is:
where, e_{pt} is the portfolio excess return and β_{pt} is portfolio betas. While the average portfolio beta is computed as the equally weighted average of the values for the 99 nonJanuary months in 19992007, as follow:
where, n_{t} is the number of firms in portfolio p in month t and
is the estimated individual firm beta, calculated using a twostep procedure
for the previous four calendar years, with January returns excluded. Betas are
computed using nonJanuary (FebDec) monthly returns and the emerging market
index with a oneyear portfolio formation period and a threeyear portfolio
estimation period and the mean number of firm in each portfolios are formed
based upon independent rankings of firms by beta and market equity value. These
pooled timeseries crosssection regressions each use nine observations of four
beta quartile portfolio returns.
The average January return for portfolio p is calculated as:
where, r_{it} is the return on security I in month t and n_{t} is the number of securities in portfolio p in month t.
Excess returns are calculated as:
RESULTS AND DISCUSSIONS
Table 1 shows the average beta and the average number of
firms in each of the twenty types of portfolio ranked by the basis of market
value and beta. Because Fama and French (1992, 1993)
and Banz (1981) found that firm’s beta and size
are negatively correlated, the twenty portfolios do not have the same amount
of firms. As can be seen in Table 1, small and large firms
are likely to have higher beta than the mediumsize firms. In the small firm
size has average beta of 0.604, 0.584, 0.770 and 0.935 in the first, second,
third and forth beta quartile, respectively.
Table 1: 
Mean portfolio betas^{a} and the mean number of firms
in each portfolio^{b}, 19952007 

^{c}The row and column averages are the equally weighted
averages of the nine yearly row and column averages, where each year’s
row and column averages are equally weighted across firms. *Average number
of firms in a portfolio are in parenthesis 
Table 2: 
Average January returns (19992007) for emerging market firms
ranked by market value quintile and beta quartile 

^{a}The returns in the “all” rows and columns
are the equally weighted average returns over the nine Januaries, where
each January’s return is the equally weighted average return of the
firms in the appropriated row or column. ^{b}A return of 0.0865
is 8.65 percent per month, ^{c}Standard deviation of the means for
the nine portfolio returns are in parentheses 
The average beta starts to decrease to mediumsize portfolio at 0.535, 0.529,
0.707 and 0.991 in the first, second, third and forth beta quartile, respectively.
Then the average beta increase to large firm size at 0.603, 0.719, 0.811 and
1.019 in the first, second, third and forth beta quartile, respectively. This
is a surprisingly pattern, however, it is inconsistent with Ritter
and Chopra (1989) findings where they found that small firms are more likely
to have high beta than mediumsize and large firms.
To analyze the relationship between beta and realized security returns, with
keeping the size constant, we show the mean equally weighted returns on portfolios
formed on the basis of market value quintiles and beta quartiles for the 19992007
period. Since the relationship had been previously studied by Keim
(1983) and Rogalski and Tinic (1986), that for smallsize
firms in January returns behave substantially different from their nonJanuary
(FebruaryDecember) return months.
Table 3: 
Average monthly returns for FebruaryDecember (19992007)
for emerging markets firms partitioned by market value quintile and beta
quartile^{a} 

^{a}The returns in the “all” rows and columns
are the equally weighted average returns over the nine month, where each
month return is the equally weighted average return of the firms in the
appropriated row or column. ^{b}A return of 0.0447 is 4.47% month^{1},
^{c}Standard deviation of the means for the nine portfolio returns
are in parentheses 
Moreover, Ritter and Chopra (1989) found that January
returns of the small firms are abnormally higher than nonJanuary months.
Table 2 and 3 report the portfolio monthly
average returns for January and FebruaryDecember, respectively, by forming
the twenty groups of portfolios by their ranked size and beta. Both Table
2 and 3, report that there is no systematic relation between
realized returns and either beta and firm size during February to December months.
However, the significant difference is for all twenty portfolios in Table
3, the average returns are approximately one to two percent per month, except
for the forth and the fifth size quartile exceeding to approximately eight and
five percent, respectively. These findings are quietly inconsistent with other
authors that studied in the developed markets, for instance, Blume
and Stambaugh (1983) and Tinic and West (1984) found
that nonJanuary returns have approximately one percent.
To examine the riskreturn more carefully we follow Ritter
and Chopra (1989) approach. For each size quintile, we regress monthly equally
weighted portfolio returns as the dependent variables on a January intercept
dummy variable, beta and a crossproduct term which produces a January slope
dummy variables:
The January dummy variable, D_{t}^{jan}, takes on the value
of one in January and zero in other months. The equally weighted portfolio betas,
β_{pt}, have been estimated by using Fama and
Macbeth (1973) twostep procedure.
Table 4: 
Ordinary least regression results for size quintiles, with
portfolio returns and betas computed using equal weights (19992007) 

tstatistic is reported in parenthesis and *Statistically
significant at 5% 
The coefficient estimates are reported in Table 4. As can
be seen, only for the mediumsize quintile is the January riskreturn relation
strongly positive at 1.11323 with 2.10 tstatistics. Indeed for the largest
size quintile, the sample January riskreturn relation is actually negative
0.02354, although insignificantly so. The negative slopes for large firms in
January result in a somewhat amazing phenomenon. The intercept terms are higher
in January than in other months for all but the smallest quintile of firms.
The bottom panels of Table 4 report the results from estimating
the parameters in the two fiftyfour month’s subperiods of 19992003 and
20042007. The results for each subperiod are qualitatively similar. An important
issue is raised by Table 4 finding is that there is a statistically
significant positive riskreturn relation in January for mediumsize firm but
not for the small and large firms. For the first hypothesis, however, that taxloss
selling effects may not have been fully discerned by our simple measure. For
example, Chan (1986) and De Bondt
and Thaler (1987) have found that the effects appear to persist in January
at least five years after a loss is incurred.
To investigate and test both portfoliorebalancing and riskmismeasurement hypothesis we used the smallest size quintile portfolios. Within this quintile, we form beta quartile portfolios each year, using the same twostep procedure for calculating betas that we have used throughout the paper. In Table 5, we report evidence both the portfolio rebalancing and riskmismeasurement hypothesis by using realized January equally weighted market return.
Panel A of Table 5 reports the average raw January returns on the small firm beta quartile portfolios. For the nine years in 19992007, the top row reports the average portfolios returns in the three Januaries with the highest realized market returns. The third row reports the average portfolio returns in the three Januaries with steepest market decline. We find that, even in the years when the market drops in January, the average return on the portfolios of small firms is positive, whether the portfolio betas are high or low. Importantly, they have high average returns with the lowbeta portfolio having the highest average returns.
Panel B of Table 5, we report the excess return, defined
for each portfolio as:
where, r_{pt} is the return in January t on portfolio p, r_{ft}
is the riskfree rate of interest, measured as the monthly yield on longterm
government Thai bond, r_{mt} is the emerging market index return and
is the portfolio beta, estimated over the prior one year excluding Januaries.
The same results are present in the excess returns as are present in the raw
returns.
Table 5: 
Mean January returns and excess returns for equally weighted
small firms^{a}, Portfolios formed with an equal number of firms
in each beta quartile in period (19992007) 

^{a}Portfolios are formed from the smallest quintile
firms. Size is measured as firm’s market capitalization. ^{b}The
January market returns are divided into three boundaries. The boundaries
are, respectively, 8.21%, 2.85% and 9.59% for the monthly returns. Negative
monthly returns are put in parenthesis. ^{c}A return of 0.032 corresponds
to a 3.2%, ^{d}Excess returns are calculated as: 
The risk missmeasurement hypothesis predicts that, if the true
betas in January are higher than the FebruaryDecember betas, when the market
return is lower than the riskfree rate, excess returns computed using FebruaryDecember
betas should be negative for small firms. As can be seen from inspection of
the bottom row of Panel B, the data clearly reject the hypothesis that underestimated
betas are causing the patterns.
In panel C of Table 5, we test hypothesis that, for each groups of realized January market returns, there is a negative relation between beta and excess returns. The portfoliorebalancing hypothesis predicts positive slope coefficients. For all three market return groups, the slope coefficients are negative and all of them are insignificant. Thus, we interpret this evidence as not supporting the portfolio rebalancing explanation of the turn of the year effect.
This study investigated the existence of monthly pattern or seasonal effect
in emerging markets, using a more extensive and latest data sets than any previous
studies. Moreover, Riskreturn relation, risk missmeasurement and portfolio
rebalancing effect have been explored. We form portfolios into twenty groups
of portfolio ranked on firm size and beta dimension. We found only mediumsize
firm quintile has the least risky beta, however, the finding is different from
Ritter and Chopra (1989) where they found only the smallest
firm size quintile that has the least risky beta. In Table 2
and 3, we found that there is no systematic relation between
realized return and either beta or firm size during February to December months.
CONCLUSION
From our result, it could be explained that the January seasonal for the small
firms is also exist in the emerging market but there is no sign of riskreturn
relation accorded to the result and there is no evidence of portfoliorebalancing
effect that causes the January seasonal for small firms. Therefore, since the
riskmismeasurement hypothesis and the portfolio rebalancing hypothesis have
been rejected as the root cause for January seasonal, the further study may
need to be conducted in order to find out the evidence or it is just the normal
market mechanism. Furthermore, the booktomarket value may be applied instead
of the market value to detect the significant differences and also the data
set and period of study may be adjusted in order to display the clearly and
stronger results.
However, another point of interest for further study is that we found on the January’s riskreturn relation on the medium size firms. The portfolio rebalancing and riskmismeasurement hypothesis may have to be examined specifically among medium size firms rather than small firms. The different result may be caused by the different perspective and strategy of investors among countries.