Evidence of the January Effect in Southeast
Asia
Julien Rey - 10828338 Bsc Economics and Business
BSc ECB
Finance and Organization Supervisor: dr. L. (Liang) Zou
31st January 2018
Abstract
This study extends on previous research about seasonal effects and examines the presence of a January effect on daily stock returns in Southeast Asian economies. Extensive research was carried out about the January effect in developed
economies but little was done in developing economies. The study is carried out on indices representing stocks grouped by market capitalization, country or industry. The results support the theory that a January effect is present in several economies of the region, namely Malaysia, Singapore and Vietnam. Moreover, the effect is found to be stronger for small market capitalization stocks. The effect does not differ significantly across industries for most countries.
Statement of Originality
This document is written by Student Julien Rey who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of Contents
Abstract ... 1
Statement of Originality ... 2
I. Introduction ... 4
II. Literature review ... 6
1. Efficient Market Hypothesis ... 6
2. Tax-loss selling, window dressing and accounting-information ... 7
3. Size effect ... 8
4. Disappearance of the turn of the year effect in developed economies ... 8
III. Methodology and expectations ... 8
IV. Results ... 11
1. Test on Southeast Asian region ... 11
2. Test on national indices ... 12
3. Test on industry sectors in the Philippines ... 13
5. Test on industry sectors in Indonesia ... 14
6. Test on the industry sectors in Singapore ... 15
7. Test on industry sectors in Thailand ... 16
V. Conclusion ... 17
Bibliography ... 18
I. Introduction
Rozeff and Kinney (1976) first presented evidence of a January effect on monthly stock returns on the New York Stock Exchange. The January effect, also referred to as turn-of-the-year effect, is one of many seasonal effects which also include the Monday effect and the May effect. The term January effect refers to the abnormally high returns on certain stocks in the first trading days of January. According to the efficient markets hypothesis, these effects should not occur as all available
information is assumed to be reflected in the stock price (Park & Moskalev, 2010). The effect seems to be larger for smaller firms (Rogalski & Tinic, 1986). The causes of this effect remain largely unexplained, but two hypotheses, the tax-loss selling hypothesis and the window dressing effect hypothesis are often advanced as possible causes.
The tax-loss selling hypothesis is believed to be mostly the action of individual investors and window-dressing that of institutional investors (Sikes, 2014).
Furthermore, Cooper, McConnell and Ovtchinikov found that the returns in the month of January have a significant predictive power for the returns over the next 11
months of the year in their study of stocks trading on the New-York Stock Exchange (2006). Fama and French (1993) found a negative relation between corporate bond ratings and the January excess returns. Evidence of an effect was also found in stock splits where abnormal returns were significantly higher for splits taking place in January than other months (Beladi, Chao & Hu, 2016). Moreover, the effect was stronger for small firms, a size effect is also present.
The rationale behind the tax loss selling hypothesis is that investors sell poor performing stocks in December at the end of the tax year as capital losses are tax deductible, and buy these stocks again in January driving prices up (Jones, Pearce & Wilson, 1987). The window dressing hypothesis implies that mutual fund
managers sell poor performing and risky stocks at the end of the year in order to have a promising portfolio with high returns and low risk at the time of reporting. These stocks are thus priced low and mutual funds can rebuy them in January, hence driving the prices back up.
Empirical research was performed in the past on the markets of the United-States, United Kingdom, Japan (Gong & Li, 2015), as well as Romania (Balint & Gica, 2012) and the Middle-East Region (Konak & Çelik, 2016) to study the January effect. In addition, Asteriou and Kavetsos found strong evidence of a January effect in the transition economies of Hungary, Poland, Romania and Slovakia (2006).
Developed markets have become efficient, and the pricing anomalies such as the January effect disappeared over the years. Hence, this research builds on previous research and extends the study by shifting the focus to developing, less efficient markets of South-East Asia. This study is of interest as it will allow to determine whether the same pricing anomaly applies to the developing markets of the region as do to developed economies.
In addition, the markets of South-East Asia are more segmented than the United-States, United Kingdom and other developed European nations. In this paper will be examined whether evidence of the presence of a turn-of-the-year effect can be found in the region and whether market capitalization has an effect on the presence of a January effect on stock returns, that is whether firms of a certain size are more prone to the January effect than others. It is expected to find a stronger January effect for small market capitalization firms. Furthermore, will be examined whether the effect differs across industries within the countries of interest. As the January effect is the result of investor behaviour, it is expected that the effect will not differ from one industry to another.
The research question of this thesis is thus:
Is there evidence of a January effect on daily stock returns in Southeast Asia?
The analysis will further investigate whether this effect differs across market capitalizations, countries and industries. The organization of this paper is the
following: the upcoming section provides a literature review of previous studies and concepts relating to the topic of interest, then the expectations and hypotheses are formulated in the next section, followed by the methodology, leading to the results section and finally the conclusion.
II. Literature review
1. Efficient Market Hypothesis
Identifying a market pricing anomaly of stocks in the month of January would allow investors to establish a strategy which would outperform the market return. The efficient market hypothesis developed by Eugene Fama, however, states that all available information is priced in securities on the market (1970). There is hence, according to this theory, no opportunity for investors to beat the market and get a higher return using an active management strategy than adopting a passive strategy (Ang, Goetzmann and Schaefer, 2011). Fama (1970) established three different forms of market efficiency theories, namely the weak form, the semi-strong form and the strong form. The weak form implies that stock prices movements are
independent from past performance and follow a random walk. The semi-strong form implies that all publicly available information is priced in stock prices, hence an investment strategy which would outperform the market could only be based on private information (Fama, 1970). The strong-form takes the assumption further by assuming that all information, both public and private, is priced in securities and hence no profitable investment strategy can be established (Fama, 1970). According to this theory, stock prices are influenced by past events.
Calendar effects, also referred to as seasonal effects, such as the January effect are asset pricing anomalies. Holden, Thompson and Ruangrit (2005) state that if these anomalies are regular and predictable, the efficient market hypothesis would imply that these effects would disappear. They further imply however, that the small amplitude of these effects would make transactions costs prohibitively expensive to derive any profit from a strategy exploiting these effects. The theory further
advocates that stock prices follow a random walk (Fama, 1970); hence historical prices cannot be used to predict future performance. Stock prices follow random patterns which are driven by unforeseen forces.
2. Tax-loss selling, window dressing and accounting-information
The tax-loss selling hypothesis implies that investors sell stocks on which they have incurred losses in order to reduce their taxes at year end (Dbouk, Jamali &
Kryzanowski, 2013). Sikes (2014) attributes the January effect to tax-loss selling which she believes to be far more significant than the window dressing hypothesis among small-capitalization stocks. The research is based on monthly returns of stocks from the NYSE, NASDAQ and AMEX. Another theory is the accounting-information hypothesis, which states that the January effect is the result of the uncertainty concerning the accounting results of firms. Reinganum and
Gangopadhyay (1991) rule out this hypothesis as a rational explanation for the January effect, arguing that as the effect would be predictable, the increased returns corresponding to higher levels of risk in January should occur earlier on. Çelik and Konak (2016) on the other hand identify the higher risk in January as the main cause of a turn-of-the-year effect in the Middle-East.
Furthermore, evidence was found that the greater the losses of tax-sensitive
investors on a stock in the fourth quarter of the year, the greater is the return at the beginning of the next year (Sikes, 2014). Sikes suggests that most of the January effect previously attributed to window-dressing is actually the result of tax-loss selling (2014).
However, Jones, Pearce and Wilson (1987) discovered that a January effect was present prior to the introduction of income tax and found no significant change in the effect after the implementation of taxation. Kato and Schallheim (1985) focused their study on Japan where there is no tax on capital gains nor tax benefits on losses and found a January effect, challenging the tax-loss selling hypothesis. The authors however mitigate their findings by mentioning that the potential integration of the American and Japanese stock markets does not allow to completely rule out the effect of tax-loss selling.
3. Size effect
Banz (1981) discovered evidence of higher risk-adjusted returns for small firms than for large firms on the NYSE. Other research has demonstrated the presence of a size factor to the January effect, indeed small capitalization stocks were found to outperform large market capitalization stocks in the beginning of January (Kiyoshi & Schallheim, 1985). Furthermore, Zhang and Li (2006) identified the same size effect on the Chinese stock market where small capitalization firms seemed to be subject to a stronger effect.
4. Disappearance of the turn of the year effect in developed economies
Szakmary and Kiefer (2004) find that the January effect disappeared after 1993, as awareness of the effect increased and futures contracts on small capitalization stocks became available. According to Zhang and Li (2006), a possible explanation to the disappearance of the turn of the year effect is that as investors become increasingly aware of calendar anomalies, they seek to establish a strategy to profit from them, which makes markets more efficient and the anomalies disappear. The decline of calendar anomalies in developed economies makes it interesting to look at emerging markets which are less efficient and where a January effect could still be observed.
III. Methodology and expectations
The research reviewed in the literature review shows evidence of the presence of a January effect in many developed economies. However, as market participants are becoming aware of this anomaly and markets are becoming increasingly efficient and futures contracts available, this effect is disappearing. Hence, this research will build on earlier studies to focus on the developing economies of Southeast Asia and determine whether a January effect is present in these less efficient markets. Earlier research focused mostly on developed markets and this thesis will thus challenge the external validity of earlier results.
The daily returns of stocks trading on exchanges of Southeast Asian countries are calculated from daily historical prices. The nations in the region which have a stock or securities exchange are Cambodia, Indonesia, Laos, Malaysia, Philippines, Singapore, Thailand and Vietnam. However, although Myanmar does have a
securities Exchange created in 1996, no IPO has taken place in recent years, only 5 companies are listed and there is close to no activity, it is hence ruled out of this study. Similarly, only 5 companies are listed on the securities exchanges of Cambodia and Laos, these two countries are hence omitted as well.
Market indices are used to identify how the January Effect differs across countries, market capitalizations and industries. The study focuses on the period from 2007 to 2017. Daily returns are calculated from the daily stock indexes prices retrieved from DataStream.
The data ranges from May 30th 2008 to May 31st 2017 for the indices from Indonesia, Singapore, Malaysia, the Philippines and Vietnam. Due to data avaibility, the range for the Thaï stocks indices is from July 1st 2008 to July 1st 2016. Furthermore, the FTSE ASEAN index is used to test for a January effect in the South-East Asian region as a whole over the same period. Industry specific indices are studied to investigate whether the effect differs across industries. Abdul-Rahim (2007) found the Fama-French 3 factors model to be more efficient in his study of seasonal effects on the Malaysian stock market than the CAPM regression model. Hence the Fama-French model with an added dummy variable for the month of January is used in this study.
The regression model is as follows:
(Ri – Rf) = a + b1.SMB + b2.HML + b3.(Rmkt – Rf) + b4.January + ε
Where:
The dependent variable (Ri – Rf) is the return on the index i minus the risk-free rate Rf
The independent variables are as follows:
• SMB stands for “small minus big” market capitalization and corrects for the size effect on the returns
• HML stands for “high minus low” book-to-market ratio
• January is a dummy variable equal to 1 for values corresponding to the month of January and 0 otherwise
• ε is the error term
The data corresponding to the Fama French factors was retrieved from the Kenneth R. French data library, in the file “Fama/French Asia Pacific ex Japan 3 Factors [Daily]”.
A turn-of-the-year effect is expected to be found for most of the countries of interest. The effect is expected to be stronger for small market capitalization as a size effect was identified in most of the literature review. Furthermore, the effect is expected to remain constant across industries within countries, as the abnormal returns in the month of January, according to the tax-loss selling and window dressing hypotheses are the result of investor behaviour and are not related to the financial performance of firms. The effect would only be stronger in a specific sector if firms in the industry systematically underperformed during the last months of the year. It is expected that the January effect will be weaker the more developed the market is.
Robust standard errors are used in the ordinary least squares regressions on Stata to avoid serial correlation issues emerging from the use of time-series. The monthly abnormal returns are regressed on a binary dummy which takes the value 1 for the month of January and 0 otherwise, and control variables SMB, HML and MKTRF.
The following hypothesis will be tested:
H0: b4 = 0 H1: b4 ≠ 0
The market indices used in the regressions are summarized by country and market capitalization in the following table:
Index Smallest market
capitalization Largest Market Capitalization Country/region of interest
FTSE ASEAN All-Share 0m USD 33,244m USD Southeast Asia
FTSE SET Small Cap 0m THB 16,097 THB Thailand
FTSE SET Large Cap 28,281m THB 588,398m THB
FTSE Indonesia NA NA Indonesia
FTSE ST Mid Cap 230m SGD 4,892m SGD Singapore
FTSE ST Small Cap 19m SGD 1,003m SGD
FTSE Bursa Malaysia Small cap 60m SGD 1,640m SGD Malaysia FTSE Bursa Malaysia Top 100 0m SGD 62,936m SGD
FTSE Philippines Small Cap NA NA Philippines
FTSE Philippines All Cap NA NA
FTSE Vietnam 2,182,220m
VND
39,981,236m VND Vietnam
Constituents sizes, market capitalizations range as of 31 October 2017
IV. Results
1. Test on Southeast Asian region
The following tables summarize the coefficients obtained on the dummy variable January in all regressions in the following series of tables. Detailed output with all regressors can be found in the appendix.
Table 1 (1) Asean_Allcap january 0.0397 (0.524) N 2338 R2 0.056 p-values in parentheses
First of all, the coefficient of the January dummy on the ASEAN all-share index is not significant, hence there is not enough statistical evidence to infer that a January effect is present on the Southeast Asian market as a whole. However, an effect could be present in certain countries only, the following regressions allow to investigate this issue.
2. Test on national indices Table 2
(1) (2) (3) (4) (5)
Thailand_Small Thailand_Large Indonesia Singapore_Mid Singapore_Small
january 0.195 0.0649 0.0370 0.104* 0.118*
(0.058) (0.570) (0.708) (0.037) (0.029)
N 1943 1943 2196 2255 2255
R2 0.220 0.382 0.317 0.598 0.323
(6) (7) (8) (9) (10)
Malaysia_top100 Malaysia_Small Philippines_Small Philippines_All Vietnam
january 0.00967 0.142* 0.0837 -0.0000153 0.275* (0.837) (0.020) (0.403) (0.986) (0.021) N 2216 2216 2195 2195 2239 R2 0.332 0.324 0.226 0.223 0.070 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
Regressions on indices of Southeast Asian economies
The coefficients of the model are jointly significant for all regressions
(Prob > F = 0.000, also F > 10). A positive coefficient on the dummy January for Singapore, Vietnam and the small market capitalization index of Malaysia seem to indicate a January effect, which is significant at the 5 percent significance level. However, the coefficient for the Malaysian top 100 index is insignificant. The
regression of the Vietnam index yields the highest coefficient for the January dummy and the effect is significant at the 5 percent confidence level. The results on the Malaysian data are in line with the expectation that the effect would be stronger for small market capitalizations.
A possible reason for this is that small capitalizations are more volatile than blue chip stocks, and are therefore more likely to lose value and be sold for tax loss selling purposes, to be later rebought in January hence causing a January effect. A similar observation can be made for the Singaporean market, where a significant coefficient
on January was found both for small and mid-market capitalizations but the effect is slightly stronger for small capitalizations. Furthermore, no significant effect was found for large capitalizations on the Thaï market but a positive effect significant at the 10 percent confidence level was identified on the small capitalization index.
The following regressions allow to determine whether the January effect differs across industries within countries.
3. Test on industry sectors in the Philippines Table 3
(1) (2) (3) (4)
Banks BasicMats Beverages Consumersvs
january 0.00404 -19.77 0.0780 -0.0281
(0.971) (0.327) (0.678) (0.830)
N 2182 1214 1185 2162
R2 0.086 0.004 0.006 0.063
(5) (6) (7) (8) (9)
Electricity Financials Transport Industrials Oil_and_Gas
january -0.185 0.0583 -0.209 -0.0316 -0.596
(0.279) (0.601) (0.377) (0.820) (0.068)
N 2105 2192 948 2098 1158
R2 0.037 0.091 0.028 0.084 0.015
Regressions on industry sectors in the Philippines
There appears to be no significant January effect in the Philippines on industry specific indices, no effect was found on the global Philippines indices for “All cap” and “Small cap” indices either.
4. Test on industry sectors in Malaysia Table 4
(1) (2) (3) (4)
Auto_and_parts Banks ConstructionandM
aterials ConsumerGoods january 0.133 -0.0291 0.0554 0.0986 (0.344) (0.667) (0.565) (0.250) N 1937 1937 1937 1937 R2 0.054 0.219 0.189 0.175 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
Regressions on the industry sectors indices in Malaysia
The positive coefficient on the tobacco index in Malaysia, significant at the 5 percent significance level indicates the presence of a turn-of-the-year effect. This result is surprising as an effect was only found for small market capitalizations at the global economy level and tobacco companies are large groups.
5. Test on industry sectors in Indonesia Table 5
(1) (2) (3) (4)
Banks ConsGoods AutoParts BasicMats
january 0.0427 0.227 0.139 0.133
(0.771) (0.152) (0.504) (0.466)
N 1633 1633 1633 1633
R2 0.252 0.255 0.196 0.274
(5) (6) (7) (8)
Oil_and_Gas Telecom Utilities Tobacco
january 0.0175 -0.145 -0.0449 0.141*
(0.839) (0.095) (0.506) (0.048)
N 1937 1937 1937 1993
(5) (6) (7) (8)
Industrials Telecom Tobacco Utilities
january 0.166 -0.0140 0.0393 0.0825 (0.346) (0.920) (0.846) (0.689) N 1633 1633 1633 1633 R2 0.268 0.111 0.100 0.185 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
Regressions on the industry sectors indices in Indonesia
No effect seems to be present on the industry sectors studied above in Indonesia, none of the coefficients on the January dummy variable are significant.
6. Test on the industry sectors in Singapore Table 6
(1) (2) (3)
ConsumerSVS Electronics Healthcare
january -0.0169 0.116 0.102
(0.811) (0.277) (0.269)
N 2083 2083 2083
R2 0.505 0.191 0.281
(4) (5) (6)
Oil_and_Gas Banks Telecom
january 0.0973 0.0186 0.110
(0.409) (0.811) (0.212)
N 2083 2083 2083
R2 0.423 0.541 0.267
Regressions on the industry sectors indices in Singapore
The January coefficients on these industry sectors in Singapore are not significant either, hence no effect is present for the industries, although evidence of an effect exists for the global economy both on small market capitalizations and mid
7. Test on industry sectors in Thailand Table 7
(1) (2) (3) (4) (5)
ConsumerSV S
Electronics Healthcare Oil_and_Gas Banks
january -0.174 0.116 0.102 0.0940 0.220
(0.157) (0.277) (0.269) (0.602) (0.156)
N 1771 2083 2083 1771 1771
R2 0.158 0.191 0.281 0.338 0.328
(6) (7) (8) (9) (10) (11)
Telecom Basicmats Electricity Mining Chemicals Utilities january 0.0337 0.0985 0.0550 0.0126 0.144 0.0550 (0.842) (0.572) (0.625) (0.955) (0.426) (0.625) N 1771 1771 1771 1771 1771 1771 R2 0.080 0.280 0.088 0.185 0.241 0.088 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
Regressions on the industry sectors indices in Thailand
No significant effect exists for these industries in Thailand.
No significant January effect was found on the Singaporean and Thai industry indices, this is surprising as a January effect is present for the Singaporean market as a whole, for mid and small capitalization, and evidence of an effect at the 10 percent significance level was also present for small Thailand market capitalization stocks. The industry specific regressions in most countries do not give evidence of a turn-of-the-year effect either, except for the oil and gas sector in the Philippines which is significant at the 10% confidence level. In Malaysia, the coefficient on the Telecom index is significant at the 10 percent significance level but is negative, and a significant positive coefficient indicates the presence of a January effect in the tobacco industry in Malaysia. All coefficients on the month of January in Indonesia were insignificant.
V. Conclusion
Potential January effects were expected on South-East Asian markets, with a stronger effect for small market capitalization stocks. The effect was expected to remain constant across industries. No January effect was present on the region as a whole, however significant turn-of-the-year effects were identified in Singapore, Malaysia and Vietnam, as well as in Thailand with at a larger significance level.
In line with expectations, the strength of the effect was found to depend negatively on market capitalization in Thailand, Singapore and Malaysia. Stronger effects were found for indices with smaller market capitalization constituents. The effect did not vary across industries in any country and no effect was found on sector specific indices, with exception of the tobacco sector in Malaysia where a significant January effect was identified. Moreover, a negative effect, significant at the 10 percent
significance level was identified on the oil and gas sector in the Philippines. The high volatility of the industry is a possible cause of this effect. Another negative effect significant at the 10 percent level was identified on the telecom sector in Malaysia.
The strongest effect was identified on the Vietnamese market, which is less developed than Malaysia, Thailand and Singapore, the relative inefficiency of this market could thus be advanced as a possible explanation for the stronger effect. However, the regression on Singapore which is a developed market showed evidence of a January effect and there was no evidence of an effect in the
Philippines which is less developed. Hence the expectation that the more developed the market, the weaker the January effect cannot be confirmed.
These results are similar to earlier research done on developed economies, a January effect and a size effect were identified in several countries although no effect was found on the Southeast Asian region as a whole. The effect did not vary significantly across industries. The absence of an effect on most industry specific indices is surprising and could be caused by a high proportion of large market
capitalization constituents in the selected sectors, and thus might be attributed to the size effect.
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Appendix
(1) (2) (3) (4) (5)
Thailand_Small Thailand_Large Indonesia Singapore_Mid Singapore_Small
january 0.195 0.0649 0.0370 0.104* 0.118* (0.058) (0.570) (0.708) (0.037) (0.029) hml 0.373*** 0.451*** 0.457*** 0.549*** 0.701*** (0.000) (0.000) (0.000) (0.000) (0.000) smb 0.372*** 0.0865 0.117 0.242*** 0.592*** (0.000) (0.208) (0.317) (0.000) (0.000) mktrf 0.640*** 0.831*** 0.885*** 0.835*** 0.933*** (0.000) (0.000) (0.000) (0.000) (0.000) _cons 0.00486 -0.0133 -0.0177 -0.0724*** -0.0878** (0.867) (0.637) (0.570) (0.000) (0.002) N 1943 1943 2196 2255 2255 R2 0.220 0.382 0.317 0.598 0.323 (1) (2) (3) (4) (5)
Malaysia_top100 Malaysia_Small Philippines_Small Philippines_All Vietnam
hml 0.248*** 0.414*** 0.335*** 0.00287*** 0.101 (0.000) (0.000) (0.000) (0.000) (0.227) smb 0.167*** 0.413*** 0.430*** 0.00232** 0.420*** (0.000) (0.000) (0.000) (0.003) (0.000) mktrf 0.415*** 0.583*** 0.697*** 0.00589*** 0.412*** (0.000) (0.000) (0.000) (0.000) (0.000) january 0.00967 0.142* 0.0837 -0.0000153 0.275* (0.837) (0.020) (0.403) (0.986) (0.021) _cons -0.0310* -0.0392* -0.00440 0.000440 -0.0682* (0.018) (0.028) (0.878) (0.083) (0.042) N 2216 2216 2195 2195 2239 R2 0.332 0.324 0.226 0.223 0.070 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
(1) (2) (3) (4) (5) Asean_Allcap Banks BasicMats Beverages Consumersvs
january 0.0397 0.00404 -19.77 0.0780 -0.0281 (0.524) (0.971) (0.327) (0.678) (0.830) hml -0.159** -0.0731 -82.64 -0.0264 0.0606 (0.003) (0.418) (0.319) (0.889) (0.492) smb 0.172** 0.206* 63.25 0.0912 0.0921 (0.010) (0.021) (0.328) (0.531) (0.318) mktrf 0.160*** 0.395*** 16.54 0.232** 0.390*** (0.000) (0.000) (0.341) (0.002) (0.000) _cons -0.0239 0.0200 29.53 -0.0122 0.0519 (0.226) (0.546) (0.317) (0.871) (0.169) N 2338 2182 1214 1185 2162 R2 0.056 0.086 0.004 0.006 0.063 (1) (2) (3) (4) (5)
Electricity Financials Transport Industrials Oil_and_Gas
january -0.185 0.0583 -0.209 -0.0316 -0.596 (0.279) (0.601) (0.377) (0.820) (0.068) hml -0.0880 -0.0485 0.0520 -0.106 0.0293 (0.599) (0.578) (0.752) (0.344) (0.870) smb 0.0871 0.184* -0.108 0.185 0.189 (0.554) (0.032) (0.314) (0.095) (0.279) mktrf 0.392*** 0.410*** 0.400*** 0.451*** 0.288** (0.000) (0.000) (0.000) (0.000) (0.004) _cons 0.0921 0.0237 0.0493 0.0181 0.134 (0.105) (0.474) (0.468) (0.647) (0.145) N 2105 2192 948 2098 1158 R2 0.037 0.091 0.028 0.084 0.015 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
Regressions on the indices of the South-East Asian region and industry sectors in the Philippines
(1) (2) (3) (4) Auto_and_parts Banks Construction and
Materials ConsumerGoods january 0.133 -0.0291 0.0554 0.0986 (0.344) (0.667) (0.565) (0.250) hml 0.283*** 0.278*** 0.344*** 0.184** (0.000) (0.000) (0.000) (0.002) smb 0.151 0.189** 0.214** 0.167* (0.053) (0.003) (0.007) (0.037) mktrf 0.342*** 0.409*** 0.542*** 0.410*** (0.000) (0.000) (0.000) (0.000) _cons -0.0433 -0.0106 -0.00101 -0.0414 (0.183) (0.551) (0.970) (0.050) N 1937 1937 1937 1937 R2 0.054 0.219 0.189 0.175 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
Regressions on the industry sectors indices in Malaysia
(5) (6) (7) (1)
Oil_and_Gas Telecom Utilities Tobacco
january 0.0175 -0.145 -0.0449 0.141* (0.839) (0.095) (0.506) (0.048) hml 0.202*** 0.172** 0.199*** -0.0341 (0.000) (0.002) (0.000) (0.608) smb 0.124* 0.0162 0.105 0.185*** (0.032) (0.819) (0.055) (0.000) mktrf 0.290*** 0.292*** 0.325*** 0.00807 (0.000) (0.000) (0.000) (0.944) _cons -0.00848 0.00365 -0.0164 -0.0279 (0.741) (0.861) (0.423) (0.423) N 1937 1937 1937 1993 R2 0.066 0.112 0.129 0.019
(1) (2) (3) (4)
Banks ConsGoods AutoParts BasicMats
january 0.0427 0.227 0.139 0.133 (0.771) (0.152) (0.504) (0.466) hml 0.487*** 0.562*** 0.680*** 0.296 (0.000) (0.000) (0.000) (0.099) smb 0.112 0.354 0.462 0.195 (0.556) (0.065) (0.055) (0.246) mktrf 0.926*** 1.030*** 1.183*** 1.286*** (0.000) (0.000) (0.000) (0.000) _cons 0.0217 0.00525 0.0148 -0.124 (0.644) (0.915) (0.821) (0.052) N 1633 1633 1633 1633 R2 0.252 0.255 0.196 0.274 (5) (6) (7) (8)
Industrials Telecom Tobacco Utilities
january 0.166 -0.0140 0.0393 0.0825 (0.346) (0.920) (0.846) (0.689) hml 0.478*** 0.373*** 0.132 0.479** (0.000) (0.001) (0.442) (0.007) smb 0.365* -0.0121 0.0554 0.00544 (0.046) (0.932) (0.787) (0.981) mktrf 1.099*** 0.562*** 0.749*** 1.068*** (0.000) (0.000) (0.000) (0.000) _cons -0.0415 -0.0197 0.141* -0.0277 (0.411) (0.686) (0.046) (0.687) N 1633 1633 1633 1633 R2 0.268 0.111 0.100 0.185 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
(1) (2) (3) ConsumerSVS Electronics Healthcare
january -0.0169 0.116 0.102 (0.811) (0.277) (0.269) hml 0.545*** 0.269* 0.465*** (0.000) (0.010) (0.000) smb -0.0558 0.107 0.171 (0.284) (0.336) (0.058) mktrf 0.729*** 0.677*** 0.745*** (0.000) (0.000) (0.000) _cons -0.0532** -0.0382 -0.0305 (0.005) (0.287) (0.295) N 2083 2083 2083 R2 0.505 0.191 0.281 (4) (5) (6)
Oil_and_Gas Banks Telecom
january 0.0973 0.0186 0.110 (0.409) (0.811) (0.212) hml 0.888*** 0.574*** 0.368*** (0.000) (0.000) (0.000) smb 0.519*** -0.218*** -0.320** (0.001) (0.000) (0.002) mktrf 1.186*** 0.824*** 0.521*** (0.000) (0.000) (0.000) _cons -0.103** -0.0480* -0.0617* (0.001) (0.021) (0.021) N 2083 2083 2083 R2 0.423 0.541 0.267 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
(1) (2) (3) (4) (5) (6) ConsumerSV
S
Electronics Healthcare Oil_and_Gas Banks Telecom
january -0.174 0.116 0.102 0.0940 0.220 0.0337 (0.157) (0.277) (0.269) (0.602) (0.156) (0.842) hml 0.400*** 0.269* 0.465*** 0.457*** 0.556*** 0.315** (0.000) (0.010) (0.000) (0.000) (0.000) (0.005) smb 0.169* 0.107 0.171 0.213* 0.00843 -0.109 (0.044) (0.336) (0.058) (0.049) (0.923) (0.312) mktrf 0.593*** 0.677*** 0.745*** 1.070*** 0.912*** 0.449*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) _cons 0.0510 -0.0382 -0.0305 -0.0572 -0.0534 -0.0182 (0.164) (0.287) (0.295) (0.162) (0.148) (0.697) N 1771 2083 2083 1771 1771 1771 R2 0.158 0.191 0.281 0.338 0.328 0.080 (7) (8) (9) (10) (11)
Basicmats Electricity Mining Chemicals Utilities
january 0.0985 0.0550 0.0126 0.144 0.0550 (0.572) (0.625) (0.955) (0.426) (0.625) hml 0.613*** 0.138 0.382** 0.765*** 0.138 (0.000) (0.110) (0.003) (0.000) (0.110) smb 0.423*** 0.0806 0.247 0.511*** 0.0806 (0.001) (0.366) (0.080) (0.000) (0.366) mktrf 1.112*** 0.339*** 1.026*** 1.160*** 0.339*** (0.000) (0.000) (0.000) (0.000) (0.000) _cons -0.0487 -0.0194 -0.0688 -0.0359 -0.0194 (0.296) (0.518) (0.248) (0.498) (0.518) N 1771 1771 1771 1771 1771 R2 0.280 0.088 0.185 0.241 0.088 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
(1) Asean_Allcap january 0.0397 (0.524) hml -0.159** (0.003) smb 0.172** (0.010) mktrf 0.160*** (0.000) _cons -0.0239 (0.226) N 2338 R2 0.056 p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
