Fundamental factors and stock returns: evidence from Asian ... · PDF filebetter explain the...
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Fundamental factors and stock returns: evidence from Asian stock markets
Dazhi Zheng*
West Chester University
Thomas C. Chiang
Drexel University
This version: January 15, 2016
Abstract
This paper examines the relation between fundamental factors and stock returns of 9 Asian
markets (Japan, China, South Korea, Hong Kong, Taiwan, Singapore, Indonesia, Malaysia, and
Thailand). Following Fama and French (1993, 2015), we form the market risk premium, size,
B/M, profitability, investment, momentum, P/E, and dividend yields factors for each market. The
empirical results suggest that the eight-factor model that includes all above fundamentals can
better explain the variations of stock returns than the original Fama-French three-factor model.
By replacing local fundamental factors with international factors, we find that the model with
local factors outperforms the models with international factors. In addition, the evidence reveals
that the eight-factor model can better explain stock returns when market is under stress. When
we test the relation between the fundamental factors and industry portfolio stock returns, the
results suggest that portfolio returns of different industries are associated with different sets of
fundamental factors.
JEL Classification: G12, G15, G01
Keywords: Fama-French three-factor model, Fama-French five-factor model, stock fundamentals
Asset pricing model, International stock markets,
___________________________________________________________
*corresponding author; Tel.: +1 610 430 4635; fax: +1 610 436 2592
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1. Introduction
The relation between fundamentals factors and stock returns have been studied on voluminous
research. Banz (1981) finds that smaller firms on average have had higher risk adjusted returns
than larger firms, especially for very small firms, while the return difference between average sized
and large firms is littler. Fama and French (1992, 1993) propose a three factor model indicates that
besides market risk premium and size, book to market ratio also impacts cross sectional average
stock returns. Specifically, high book-to-market value stocks (value stocks) outperform low book-
to-market stocks (growth stocks). Lakonishok, Shleifer,and Vishny (1994) extend the value
strategy and argue that stocks with high fundamentals relative prices generate to higher returns,
the fundamentals include earnings, dividend yields, historical prices, and book assets, etc. In
addition, Jegadeesh and Titman (1993) document that buying stocks performed well in the past
and sell stocking stocks performed poorly in the past generate significant positive returns over 3-
to 12-month holding periods. Carhart (1997) confirms that mutual fund with higher returns last
year are likely to have higher than expected returns next year. The fundamentals are studied in
international markets as well. The empirical evidence from Chan, Hamao, and Lakonishok (1991)
reveals that there is a significant relationship between stock fundamentals, which include earnings
yield, size, book to market ratio, and cash flow yield, and expected returns in Japanese market.
More recently, Fama and French (2012) examine whether the value and momentum premiums in
average stock returns can be captured by size, book-to-market, and momentum factors for four
international regions (North American, Europe, Japan, and Asia Pacific).
Even there are numerous studies investigate the relation between fundamentals and average stock
returns, the studies on emerging markets are much fewer. Some studies investigate only a
particular market. For example, Connor and Sehgal (2001) test Fama and French model in India;
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Iqbal and Brooks compare the CAPM and Fama-French three-factor model in Pakistan; and
Goriaev (2004) examines the fundamentals and other macroeconomic factors in Russian stock
market. Some research discusses only one of two fundamentals in explaining average stock returns
in emerging markets and their findings are also not conclusive. By studying five pacific basin
emerging markets, Chui and Wei (1998) document that book-to-market equity can explain the
cross-sectional variation of expected stock returns in Hong Kong, Korea, and Malaysia, while the
size effect is significant in all markets except Taiwan. Hameed and Kusnadi (2002) investigate
momentum investment strategies in six Asian stock markets and argue that the momentum
phenomenon are not prevalent in the Asian markets. However, in the work of Rouwenhorst (1999),
the author examines stock returns in 20 emerging markets and finds that size, value, and
momentum effects in emerging markets are similar to those in developed markets. In addition,
local factors have stronger explanatory power than global factors for emerging markets. Harvey
(1995) also confirms that emerging market returns are more likely to be influenced by local
information.
Following the findings mentioned above and to fill in the gap of the literature, in this research
we tend to contribute to the literature from the following aspects: first, unlike most studies focus
on advanced markets or some particular emerging market, we examine the relation between
fundamental factors and stock returns in one important region in international financial markets--
-Asian stock markets, which include nine major markets in this study: Japan, China, Korea, Taiwan,
Hong Kong, Singapore, Thailand, Indonesia, and Malaysia. In recent years, investors and money
managers have increased their portfolio proportion substantially in Asian stock markets as this
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region is the fastest growing region in the world.1 However, to our knowledge, no study has been
done to investigate the whole region systematically. Second, financial markets are in different
stages of development in the Asian Pacific region. There is one advanced market and also one of
the major finance markets in the world --- Japan; there is one of the largest emerging markets ---
China; there are small but advanced regional financial centers---Hong Kong and Singapore; there
are financial markets in transitional economies such as Korea and Taiwan; there are also traditional
small and emerging markets such as Thailand, Indonesia and Malaysia. However, those economies
in the region share similar cultural background and investorsβ behavior, so the findings would shed
a light on how important fundamental factors affect stock returns for financial markets in different
development stages and different sizes. Third, most research follow Fama and French (1993) only
adopt their three-factor model to estimate stock returns. However, in Fama and French (2015), the
authors include two more factors, profitability and investment factors to explain average stock
returns and they conclude that the five-factor model performs better than the original three-factor
model. Specifically, the additional profitability and investment are better factors in capturing
average returns than book-to-market factor. Besides these two additional factors, literature
suggests that other important fundamental variables such as the earnings and the dividend yield,
etc. (Lakonishok, et al. 1994, Chan et al. 1991) also affect stock returns, but research on those
variables are not thorough especially in emerging markets. Our research include seven of those
major fundamental factors (size, book-to-market, price-to-earnings ratio, dividend yields,
profitability, investment, and momentum) in the asset pricing model. To our knowledge, it is one
of the most comprehensive study to study the relation between fundamentals and stock returns in
1 According to World Federation of Exchanges 2014 Market Highlights report published in March 2015, the Asia-
Pacific equity market capitalization has reached $21 trillion at the end of 2014 and was 31% of the world market
capitalization. It grew by 13.8% in 2014 compared to 7% in Americas market and -9.6% in Europe-Middle East-
Africa market.
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Asian stock markets. Fourth, in addition to local fundamental factors, we also test asset pricing
models with regional and global fundamental factors in explaining local stock returns for Asian
stock markets. Continue with our second contribution, we could further investigate which set of
fundamental variables and whether local or international information have stronger power in
explaining stock returns for financial markets in different development stages and different sizes.
Fifth, we explore how fundamental factors affect average stock returns under different market
conditions. Specifically, we divide the whole sample into up markets (positive excess industry
returns) and down markets (negative excess industry returns), and crisis periods and tranquil
periods2. Finally, we divide the whole sample into 10 industries and further test the relation
between the fundamental factors and industry portfolio stock returns.
The empirical evidence suggests that both the original three-factor (Fama and French, 1993) and
the newly proposed five-factor model (Fama and French, 2015) can well explain the stock returns
in Asian markets. In addition, with inclusion of the profitability and investment factors, the effect
of the B/M factor on stock returns are weakened, which is consistent with Fama and French (2015)
as well. When we replace the B/M factor with our proposed momentum, P/E, and dividend yield
factors for the three-factor model, the results show that these three fundamental factors also have
significant impact on stock returns in Asian markets, so they should not be excluded from the asset
pricing model. The empirical results of the complete eight-factor model3 show that it is better than
the models with only a subset of fundamental variables in explaining the variations of stock returns
in Asian stock markets, especially for larger markets. When we compare the three-factor model
2 We define the Asian crisis (1997-1998), the dot-com crisis (2000-2001), and the sub-prime mortgage crisis (2007-
2009) as crisis periods and the remaining time periods as tranquil periods. For a more detailed explanation, see
section 4. 3 The eight factors are the market risk premium, size, B/M, profitability, investment, momentum, P/E, and dividend
yield factors.
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with local fundamental factors and with international fundamental factors, the results reveal that
local factors are better than international factors in explaining stock returns in Asian markets. The
test results of our proposed eight-factor model under different market conditions indicate that the
model can better explain the variations of stock returns in all Asian markets when local market is
under stress (under crisis periods or down market) than when the market is not under stress (non-
crisis periods or up market). Finally, when we test the relation between the fundamental factors
and industry portfolio stock returns, the evidence suggests that even in general fundamental factors
can explain the variation of industry portfolio returns for Asian stock markets, the portfolio returns
of different industries are associated with different sets of fundamental factors.
The remainder of this paper is organized as follows. Section 2 explains our estimated asset
pricing model with fundamental factors as independent variables. Section 3 presents the data.
Section 4 reports the empirical evidence from our estimated models. Section 5 summarizes and
concludes.
2. Estimation models and research methodology
2.1 Models
We follow Fama and French (1993) three-factor model to construct our model to examine the
relation between stock returns and stock fundamental factors, and the general form model is as the
following:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + β π½πππ ,π‘ππ =1 + ππ,π‘, (1)
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where π π,π‘ is the monthly return of security or portfolio i for month t and ππ,π‘ is the risk free rate.4
On the right hand side of the model, π π,π‘ is the return of the equal-weight (EW) market portfolio.
Zs,t denotes a set of fundamental variables. In the original Fama-French three-factor model, there
are two fundamental variables: SMBt and HMLt, which represent the return difference between the
small size stock portfolio and the large size stock portfolio, and between the high book-to-market
(B/M) equity stock portfolio and the low B/M stock portfolio, respectively. Therefore, in our
empirical analysis, the first model is:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅π‘ + π½3π»ππΏπ‘ + ππ,π‘, (2)
In a most recent research, Fama and French (2015) argue that the three-factor model above miss
much of the variation in average returns related to profitability and investment, and the evidence
suggests that the new five-factor model includes profitability and investment factors can better
explain the variation of stock returns, so in our next empirical analysis we test the augmented five-
factor model:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅π‘ + π½3π»ππΏπ‘ + π½4π πππ‘ + π½5πΆππ΄π‘ + ππ,π‘, (3)
where RMWt and CMAt represent the profitability and investment factors, and they are defined as
the return difference between the robust profitability stock portfolio and the weak profitability
stock portfolio, and between the conservative (low investment) stock portfolio and the aggressive
(high investment) stock portfolio, respectively. Profitability is measured as EBIT divided by sales,
and investment is the change in total assets from year t-2 to t-1.
However, the evidence from Jegadeesh and Titman (1993), Carhart (1997), Chan et al. (1991),
and Lakonishok et al. (1994), among the others, suggests that the momentum, earnings yield, and
4 We use one month U.S. T-bill rate as the risk free rate.
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dividend yield are also important variables in explaining the variations of stock returns. Motivated
by this evidence and the works from Fama and French (2015). We include the momentum, price-
to-earnings ratio (P/E), and dividend yield factors and write the complete form model as:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅π‘ + π½3π»ππΏπ‘ + π½4π πππ‘ + π½5πΆππ΄π‘ + π½6πππΏπ‘ +
π½7ππππ‘ + π½8πΌπππ‘ + ππ,π‘, (4)
where WMLt, OMUt, and IMNt represent the momentum, P/E ratio and dividend yield factors, and
they are defined as the return difference between the winner stock portfolio and the loser stock
portfolio, between the high P/E (overvalued) stock portfolio and the low P/E (undervalued) stock
portfolio, and between high dividend yield (income) stock portfolio and the low dividend yield
(non-income) stock portfolio, respectively. The winner and loser stock portfolio returns are
calculated from returns between month t-11 to t-1.
2.2 Formation of portfolios and fundamental factors
At end of every year, we sort stocks in each market based on stock fundamentals including size,
book-to-market ratio, momentum, profitability, investment, price-to-earnings ratio, and dividend
yield to form portfolios. Stocks are sorted independently to form two size groups, two or three
B/M, momentum, profitability, investment, P/E ratio, and dividend yield groups. The portfolio
breakpoints is 50% of each of those fundamentals when two portfolios are formed, and are 30%
and 70% when three portfolios are formed. As shown in Table 1, for each market the size factor
SMBt is the average return on the two small stock portfolios minus the average return on the two
large stock portfolios (2 X 2 sorts three-factor model), the average return on the six small stock
portfolios minus the average return on the six large stock portfolios (2 X 2 sorts five-factor model)
and the average return on the twelve small stock portfolios minus the average return on the twelve
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large stock portfolios (2 X 2 sorts eight-factor model). The value factors HML t, (same for RMWt,
CMAt, WMLt, OMUt, and IMNt variables) are calculated as the average return on the two high B/M
portfolios minus the average return on the two low B/M portfolios (2 X 2 sorts) or the average
return on the three high B/M portfolios minus the average return on the three low B/M portfolios
(2 X 3 sorts).5
[Table 1]
3. Data description
Asian stock markets stock data are collected from Thomson Datastream. The data consist of
pricing information and fundamental variables for individual stocks and stock market indexes. At
the market level, the sample covers nine Asian stock markets: Japan (JP), China (CN), South Korea
(KR), Hong Kong (HK), Taiwan (TW), Singapore (SG), Indonesia (ID), Malaysia (MY), and
Thailand (TH). For individual stocks within each market, we collect the following variables: stock
price, trading volume, market capitalization, price-to-book value, price-to-earnings ratio, dividend
yield, earnings before interests and taxes (EBIT), total asset, sales, and interest expense on debt.
Local fundamental factors including size, book-to-market ratio, momentum, etc. are constructed
according to Fama and French (1993, 2012, and 2015).6 Regional and global fundamental factors
are collected from Kenneth R. Frenchβs data library.7
Twenty years monthly data are collected ranging from 11/1995 to 10/2015 for all nine markets.
All stock and stock index returns are calculated as π π‘ = 100 Γ (log(ππ‘) β log (ππ‘β1)), where ππ‘
5 The reported results of this paper are all based on 2 X 2 sorts portfolios, the results are similar with those based on
2 X 3 sorts portfolios and available upon request. 6 See table 1. 7 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
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denotes either the individual stock price or the stock market index. All returns in our estimations
are excess returns over one month U.S. T-Bill rate. All market capitalization and trading volume
data are logged and trading volume data are detrended using the one-year moving average of
logged trading volumes.
[Table 2]
The data in Table 2 shows that from year 1995 to 2015, among nine Asian stock markets, five
have positive monthly market returns (China, Korea, Taiwan, Thailand, and Indonesia) and China
has the highest average monthly return at 0.6%, while four have negative monthly market returns
(Japan, Hong Kong, Singapore and Malaysia) and Hong Kong has the lowest average monthly
return at -0.4%.
For the fundamental factors, SMBt, HMLt, RMWt, CMAt, and IMNt are all positive across nine
Asian markets, indicating that the return premium generally exists in small, high B/M, robust
profitability, conservative, and income stocks, which is consistent with Fama and French (1993,
2012, 2015). On the other hand, OMUt, are mostly negative except for Hong Kong and Singapore,
which indicates that buying stocks with low P/E ratio (undervalued) and selling stocks with high
P/E ratio (overvalued) generate return premiums. However, the signs for WMLt are mixed,
suggesting that the momentum premium does not generally exist in Asian stock markets.
4. Empirical analysis
In this section, we present the empirical results in the following sequence. First, the estimation
results from the original Fama French (1993) three-factor model are presented, where only the size
and book-to-market ratio factors are used as independent variables. Second, follow Fama and
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French (2015), we add the profitability and investment factors to the model and test their
incremental effects. Third, book-to-market ratio is replaced by alternative factors P/E ratio and
dividend yield in the original three-factor model to test which set of variables have better
explanatory power on stock returns. Fourth, we test the full model with all eight fundamental
factors included. Fifth, we replace local fundamental factors with international fundamental factors
in the original three-factor model, the international factors are from the U.S., Japan and global.
Sixth, we test the full model under different market conditions: the crisis periods vs. the tranquil
periods and the up markets vs. the down market. Lastly, we test how the industry portfolio stock
returns can be explained by the eight-factor model we proposed.
4.1. The original three factor model
We start with the original three factor model, following Fama and French (1993). In this setting,
only the size and book-to-market ratio are considered as the pricing factor in explaining asset
returns as shown in model 2, where the left hand dependent variable is the monthly stock return
from nine Asian markets and right hand variables are the market risk premium, size factor (SMBt)
and book-to-market (HMLt) factor.
Table 3 reports the empirical estimates of model 2. All three independent variables are
significant across the nine markets. Specifically, the coefficients of the market risk premium are
all positive and significant, the coefficients of SMBt are mostly positive and significant, with only
negative and significant for Hong Kong, and the coefficients of HMLt are also mostly positive and
significant, with only negative and significant for Taiwan. The results indicate that the original
Fama-French (1993) three-factor model have significant power in explaining stock returns in
Asian markets, and it is consistent with the argument of Fama and French (1993) that small size
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stocks and high book-to market stocks have return premiums over large and low book-to-market
stocks.
[Table 3]
4.2. Profitability and investment factor
Fama and French (2015) follow the evidence from Novy-Marx (2013) and Titman, Wei, and
Xie (2004) and argue that the original three-factor model is an incomplete model in explaining
expected stock returns, since much of the variation in average returns related to probability and
investment is missing in the model. Therefore, the profitability (robust minus weak RMWt) and
investment (CMAt) factors are added to the original three-factor model as shown in model 3. All
other variables are the same as previously defined and the profitability and investment factors are
formed the same way as the size and B/M factors. We adopt this model to test how this newly
proposed five-factor model can explain stock returns in nine Asian markets. The estimation results
are reported in Table 4.
[Table 4]
The results in Table 4 show that the coefficients of original three factors (market risk premium,
size and B/M factors) are still significant in the new model, and they have the same signs as in
Table 3 as well. However, compared to no obvious pattern in change of magnitude of the
coefficients of market risk premium and size factors from Table 3, the coefficients of B/M factor
are generally smaller in Table 4 compared to Table 3 (in 7 out of 9 markets, except for China and
Hong Kong). In addition, the newly added profitability and investment factors are also mostly
significant across the nine markets (except the investment factor in Thailand). However, the signs
of coefficients of profitability and investment factors are mixed, showing that the profitability
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premium and the investment premium donβt generally exist in Asian stock markets. Specifically,
the signs of coefficients of profitability factor are negative for China, Korea, Taiwan, and
Indonesia, but are positive for Japan, Hong Kong, Singapore, and Thailand. The signs of
coefficients of investment factor are negative for Japan, Singapore, and Indonesia, but are positive
for the other markets. The overall evidence is consistent with Fama and French (2015) that
profitability and investment factors have significant power in explaining stock returns in Asian
markets, and after these two factors are included in the model, the explanatory power from B/M
factor is being weakened.
4.3. Alternative factors: P/E and dividend yield
Inspired by the work of Fama and French (2015). We try to examine if other important
fundamental variables also possess significant power in explaining stock returns in Asian stock
markets. Jegadeesh and Titman (1993) and Carhart (1997) argue that buying winning and selling
losing stocks generate significant positive returns. Chan et al. (1991) and Lakonishok et al. (1994)
argue that the trading strategies of buying stocks that have low prices to earnings or dividends
outperform the market. Follow those findings, we form the momentum, P/E and dividend yield
factors and test their relation with stock returns in Asian stock market.
Since the B/M factor, P/E, and dividend yield factors are all price relative ratios. Especially high
P/E stocks and low B/M stocks are both considered high growth stocks, for the test, we modify the
original Fama-French (1993) three-factor model that the B/M factor is replaced by the momentum,
P/E and dividend yield factors. We re-write model 2 as the following:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππΏπ‘ + π½3ππππ‘ + π½4πΌπππ‘ + ππ,π‘, (3)β
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where WMLt, OMUt, and IMNt represent the momentum (winner minus loser), P/E (overvalued
minus undervalued) and dividend yield (income minus non-income) factors and other factors are
the same as in previous sections.
[Table 5]
Table 5 reports the empirical results of model 3β. All coefficients of the momentum, P/E and dividend
yield factors are significant except for IMNt in Japan and OMUt, in Hong King, indicating that
these three factors are important fundamentals in explaining stock returns in Asian stock markets
and therefore should not be excluded from the asset pricing model. For the momentum (WMLt)
and dividend yield (IMNt) factors, signs are mixed across the nine markets, but the signs for the
P/E factor (OMUt) are more consistent that for 8 out of 9 markets (except for Hong Kong) they are
negative and significant, showing that the trading strategy of buying stocks that have low P/E ratio
and selling that have high P/E ratio produces return premiums, while the strategy to trade on
dividend yield and momentum cannot generate return premiums consistently across the nine Asian
markets.
4.4. The complete model
Since the two sets of variables, the size, B/M, profitability, and investment variables from Fama
and French (2015) and the momentum, P/E, and dividend yield we proposed in last section are
mostly significant in explaining stock returns in Asian stock markets, and the two models 3 and 3β
have similar explaining power as the R squared are similar from the estimation results, we decide
to combine the two sets of variables and form the complete model 4 to test the relation between
fundamental factors and stock returns in Asian stock markets. The regression results are presented
in Table 6.
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[Table 6]
The evidence in Table 6 suggests that even we include all fundamental factors in the model, the
coefficients of those factors are still almost all significant (except for the size (SMBt), profitability
(RMWt) and P/E factors (OMUt) for Hong Kong, the dividend yield (IMNt) and momentum (WMLt)
factors for Indonesia, the investment factor (CMAt) for Thailand, and dividend yield (IMNt) for
Japan). Furthermore, the absolute values of the intercept of the regressions for most markets
(Except for Thailand and Malaysia) in Table 6 are significantly lower than those in Table 3, 4, and
5, and with lower absolute values of t-value, indicating that the complete model can better explain
the variations of stock returns in Asian stock markets, especially for larger markets.
4.5. International fundamental factors
Since financial markets become more globalized, international pricing factors have increasing
impact on domestic stock returns (Griffin, 2002; Connolly and Wang, 2003; Chiang and Zheng,
2010). In addition, Fama and French (2012) also compare whether local information or
international information are more successful in explaining local stock returns in their 23 advanced
markets sample. Thus, it is necessary to test the relation between international fundamental factors
and domestic stock returns in our setting.
We collect the international fundamental factors data from Frenchβs data library8 and re-run
model 2 the three-factor model, only replacing all domestic fundamental factors with international
factors:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅πππ‘,π‘ + π½3π»ππΏπππ‘,π‘ + ππ,π‘, (2)β
8 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
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where the subscript βintβ of the independent variables means that the factor is from international
markets. We test three sets of international factors: the U.S. fundamental factors, the global
fundamental factors, and Japanese fundamental factors. The results are shown in Table 7.
[Table 7]
Panel A of Table 7 contains the results with U.S. size and B/M as independent variables, Panel
B contains the results with global fundamental variables, and Panel C contains the results with
Japanese fundamental variables. To save space, only the absolute value of intercepts and R squared
of each regression are reported in the table.9 The results are consistent with the findings from Fama
and French (2012) that the models with international fundamental factors underperform the models
with local fundamental factors in explaining stock returns in Asian markets, as the R squared in
all equations in Table 7 are much smaller to those in corresponding markets in Table 3, while the
intercepts are generally larger in Table 7 than those in corresponding markets of Table 3. However,
there are also some interesting points: almost all marketsβ R squared (except for Thailand) in Panel
B are larger than those in Panel A and Panel C, indicating that global fundamental factors have
stronger power in explaining stock returns in Asian stock markets than the fundamentals from the
U.S. and Japan. When we compare the R squared in Panel A and Panel C, the results suggest that
Japanese fundamentals have stronger power (larger R squared) in explaining stock returns in larger
stock markets (China, Japan, and Korea), while the U.S. fundamentals have stronger power ((larger
R squared)) in explaining stock returns in smaller stock markets (Taiwan, Hong Kong, Singapore,
Indonesia, and Malaysia). Our findings are in line with those from Rouwenhorst (1999) and
Harvey (1995).
9 Most coefficients of the fundamental factors are significant and have the same signs with those of local
fundamental factors. The results are available upon requests.
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4.6. Eight-factor model under different market conditions
The existing literature documents that that risk factors are elevated during recessions (Lundblad,
2007 and Connolly et al., 2005) in explaining stock returns. Investors also tend to avoid risky
assets and behave differently under extreme market conditions. 10 Thus, it is of interest to
investigate whether under extreme market conditions, such as financial crises, the parametric
estimates of the relationship between fundamental factors and stock returns could alter.
We define three crisis periods in our sampleβs time range: the first is the Asian financial crisis,
ranging from July 1997 to August 1998; the second is the dot com bubble crashes, ranging from
March 2000 to March 2001; and the third is the recent global financial crisis, ranging from July
2007 to June 2009. We then divide the whole sample into two group, the crisis and the non-crisis,
and re-run the complete model 4 for each data group.
[Table 8]
The results are reported in Table 8 Panel A. To save space, only the absolute value of intercepts
and R squared of each regression are reported in the table.11 There are sharp differences of the
estimated intercepts and R squared between the regressions of crisis and non-crisis data groups.
First, the estimated R squared of the regressions of the crisis data group are much larger than those
of the non-crisis data group for all nine Asian markets. Second, the absolute value of the intercepts
of the regressions for the crisis data group are also much larger than those for the non-crisis data
group for 8 out of 9 Asian markets (except for Thailand). The findings suggest that the eight-factor
model can better explain the variations of stock returns in all Asian markets under crisis periods.
10 For example, according to Chiang and Zheng (2010), investors are more likely to herd during periods of
financial crisis. 11 Most coefficients of the fundamental factors are significant and have the same signs with those of from Table 6.
The results are available upon requests.
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However, the larger absolute value of intercepts also indicates that there are more pricing factors
affect stock returns in those Asian markets under crisis periods, which may point out a future
research direction.
To further investigate the relation between the fundamental factors and stock returns in Asian
stock markets under different market conditions. We divide the whole sample into two groups
according to the domestic market returns. Specifically, when the local market return is positive we
define it as the up market and when it is negative we define it as the down market. We then re-run
the complete model 4 for each data group.
The results are reported in Table 8 Panel B, and are consistent with our findings from the crisis
vs. non-crisis comparison that the estimated R squared of the regressions of the down market data
group are larger than those of the up market data group for all nine Asian markets. However,
thereβs no obvious pattern when we compare the intercepts of these two groups.
In summary. Our proposed eight-factor model can better explain the variations of stock returns
in all Asian markets when local market is under stress (under crisis periods or down market) than
when the market is not under stress (non-crisis periods or up market). However, when the market
is under stress, more pricing factors may need to be included in the asset pricing model.
4.7. Fundamentals and industry portfolios
Fama and French three-factor and five factor models have been applied to explain portfolio
returns such as microcap stock portfolios, megacap stock portfolios, value stock portfolios, and
growth stock portfolios (Fama and French (1993, 2012, 2015)). However, much fewer attempts
18
have been made to examine industry portfolio returns.12 To examine the relation between the
industry portfolio returns and the fundamental factors, we form ten industry stock portfolios for
each market according to the industry classification of Thomson Reuters Datastream.13 We then
combine the same industry portfolio of all the nine Asian stock markets together to form ten
industry portfolio data groups. We finally analyze the ten industry portfolio data groups with our
complete model 4.
The estimated results are reported in Table 9. The regressionsβ R squared ranges from 0.22
(Telecom industry) to 0.33 (utility industry), indicating that the eight-factor model we proposed
can explain the return of industry portfolios very well. Especially for Oil & Gas industry and Utility
industry, both intercepts are not rejected from zero. However, on the other hand, the coefficients
of some fundamental factors are not significant anymore. Specifically, SMBt for Consumer
Services industry, HMLt for Oil & Gas industry, RMWt for Oil & Gas and Consumer Services, and
Utility industries, WMLt, for Oil & Gas industry, OMUt for Health Care, Financial, and Technology
industries, and IMNt for Oil & Gas industry and Consumer Services industry. The coefficients of
investment factor CMAt are only significant for Basic materials, Industrials, and Consumer Goods
industries.
The evidence suggests that even in general fundamental factors can explain the variation of
industry portfolio returns, portfolio returns of different industries are associated with different sets
of fundamental factors. Especially the investment factor has no explanatory power for 7 out of 10
12 A few studies such as Fama and French (1997), Hou,and Robinson (2006), and Hu (2007), among others, apply
the three-factor model to explain industry portfolio returns. However, their works are mostly focus on the U.S.
markets. 13 The 10 industry sectors are Oil and Gas, Basic Materials, Industrials, Consumer Goods, Consumer Services,
Health Care, Telecommunications, Utilities, Financials, and Technology by level-2 industry classification from
Datastream.
19
industry portfolios and only size and P/E factors have significant impact on Oil & Gas industry
returns. The reason on the differences among industries requires further investigations.
[Table 9]
5. Conclusion
Fama and French (1992, 1993) propose a three-factor asset pricing model and argue that besides
the market risk premium, size and book-to-market ratio are also important pricing factors in
explaining stock returns. Since then, the three-factor models have been widely adopted in empirical
asset pricing studies. In Fama and French (2015), the authors extend the original model to a five-
factor model that include the additional profitability and investment factors and argue that the five-
factor model can better explain the variation of stock returns than the original three-factor model.
Inspired by their works, this study tends to examine the relation between the fundamental factors
and stock returns in nine Asian markets: Japan, China, South Korea, Hong Kong, Taiwan,
Singapore, Indonesia, Malaysia, and Thailand. Furthermore, follow the findings of Lakonishok, et
al. (1994), Chan et al. (1991), Jegadeesh and Titman (1993), and Carhart (1997), among others,
we incorporate the momentum, P/E, and dividend yield factors in the five-factor model framework
and test which factor(s) are better in explaining stock returns for the nine Asian markets. In
addition, this study also tests the relation between the fundamental factors and stock returns under
different market conditions and the relation between the fundamental factors and industry portfolio
returns.
The empirical results from the three-factor model suggest that the size and B/M factor are
significant in explaining stock returns in Asian markets, and the signs of the factorsβ coefficients
are consistent with the findings from Fama and French (1993) in the U.S. stock market. The
empirical results from the five-factor model suggest that the profitability and investment factors
20
also have significant impact on stock returns for Asian markets, and by including these two factors,
the effect of the B/M factor on stock returns is weakened, which is consistent with Fama and
French (2015) as well. However, the signs of the coefficients of profitability and investment factors
are mixed, indicating that the profitability premium and the investment premium donβt generally
exist in Asian stock markets.
When we replace the B/M factor with our proposed momentum, P/E, and dividend yield factors
for the three-factor model, the results show that these three fundamental factors also have
significant impact on stock returns in Asian markets, so they should not be excluded from the asset
pricing model. Therefore, we build a complete eight-factor model to explain the variations of stock
returns in Asian stock markets. The eight factors include the market risk premium, size, B/M,
profitability, investment, momentum, P/E, and dividend yield factors. The estimation results from
the complete model show that it is better than the models with only a subset of fundamental
variables in explaining the variations of stock returns in Asian stock markets, especially for larger
markets.
To test whether local information or international information have stronger impact on stock
returns in Asian markets, we replace the local size and B/M factors in the original three-factor
model with international factors. Three sets of international factors are used: the U.S. fundamental
factors, the global fundamental factors, and Japanese fundamental factors. The results reveal that
local factors model outperforms all three models with international factors. Among the three sets
of international factors, the global factors have the strongest power in explaining Asian markets
stock returns. The U.S. factors are better in explaining small size Asian markets stock returns,
while the Japanese factors are better in explaining large size Asian markets stock returns.
21
The tests of our proposed eight-factor model under different market conditions suggest that the
model can better explain the variations of stock returns in all Asian markets when local market is
under stress (under crisis periods or down market) than when the market is not under stress (non-
crisis periods or up market). However, when the market is under stress, more pricing factors may
need to be included in the asset pricing model.
When we test the relation between the fundamental factors and industry portfolio stock returns,
the evidence suggests that even in general fundamental factors can explain the variation of industry
portfolio returns for Asian stock markets, portfolio returns of different industries are associated
with different sets of fundamental factors. Especially the investment factor has no explanatory
power for 7 out of 10 industry portfolios and only size and P/E factors have significant impact on
Oil & Gas industry returns.
22
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24
Table 1. Construction of size, B/M, profitability, investment, P/E, and D/Y factors
This table illustrate how stocks are sorted to form portfolios. for each market the size factor SMBt is the average return on the two small stock
portfolios minus the average return on the two large stock portfolios (2 X 2 sorts three-factor model), the average return on the six small stock
portfolios minus the average return on the six large stock portfolios (2 X 2 sorts five-factor model) and the average return on the twelve small stock
portfolios minus the average return on the twelve large stock portfolios (2 X 2 sorts eight-factor model). The value factors HML t (same for RMWt,
CMAt, WMLt, OMUt, and IMNt variables) are calculated as the average return on the two high B/M portfolios minus the average return on the two
low B/M portfolios (2 X 2 sorts) or the average return on the three high B/M portfolios minus the average return on the three low B/M portfolios (2
X 3 sorts).14 The factors are SMBt (small minus big), HML t (high minus low B/M), RMWt (robust minus weak OP), and CMAt (conservative minus
aggressive Inv), OMUt, (overvalued minus undervalued), and IMNt (income minus non-income).
Sort Breakpoints Factors and their components
2X2 sorts on Size
and B/M,
Size: 50% median SMB=(SH+SL)/2-(BH+BL)/2
B/M: 50% median HML=(SH+BH)/2-(SL+BL)/2=[(SH-SL)+(BH-BL)]/2
2X2 sorts on Size
and B/M, or Size
and OP, or Size
and Inv
Size: 50% median SMB=(SH+SL+SR+SW+SC+SA)/6-(BH+BL+BR+BW+BC+BA)/6
B/M: 50% median HML=(SH+BH)/2-(SL+BL)/2=[(SH-SL)+(BH-BL)]/2
OP: 50% median RMW=(SR+BR)/2-(SW+BW)/2=[(SR-SW)+(BR-BW)]/2
Inv: 50% median CMA=(SC+BC)/2-(SA+BA)/2=[(SC-SA)+(BC-BA)]/2
2X2 sorts on Size
and B/M, or Size
and OP, Size and
Inv, Size and
MOM, Size and
P/E, or Size and
DY
Size: 50% median SMB=(SH+SL+SR+SW+SC+SA)/6-(BH+BL+BR+BW+BC+BA)/6
B/M: 50% median HML=(SH+BH)/2-(SL+BL)/2=[(SH-SL)+(BH-BL)]/2
OP: 50% median RMW=(SR+BR)/2-(SW+BW)/2=[(SR-SW)+(BR-BW)]/2
Inv: 50% median CMA=(SC+BC)/2-(SA+BA)/2=[(SC-SA)+(BC-BA)]/2
MOM: 50% median WML=(SW+BW)/2-(SL+BL)/2=[(SW-SL)+(BW-BL)]/2
P/E: 50% median OMU=(SO+BO)/2-(SU+BU)/2=[(SO-SU)+(BO-BU)]/2 DY: 50% median IMN=(SI+BI)/2-(SN+BN)/2=[(SI-SN)+(BI-BN)]/2
14 The empirical analysis of this paper are all based on 2 X 2 sorts portfolios, the results are similar with those based on 2 X 3 sorts portfolios and available upon
request.
25
2 X 3 sorts on
Size and B/M, or
Size and OP, or
Size and Inv,
Size: 50% median
SMBB/M= (SH+SN+SL)/3-(BH+BN+BL)/3
SMBOP= (SR+SN+SW)/3-(BR+BN+BW)/3
SMBInv= (SC+SN+SA)/3-(BC+BN+BA)/3
SMB= (SMBB/M +SMBOP +SMBInv)/3
B/M: 30th and 70th
percentiles HML=(SH+BH)/2-(SL+BL)/2=[(SH-SL)+(BH-BL)]/2
OP: 30th and 70th
percentiles RMW=(SR+BR)/2-(SW+BW)/2=[(SR-SW)+(BR-BW)]/2
Inv: 30th and 70th
percentiles CMA=(SC+BC)/2-(SA+BA)/2=[(SC-SA)+(BC-BA)]/2
26
Table 2. Summary statistics
This table presents the mean and standard deviation values of the eight independent variables used in our models for nine Asian markets: Japan
(JP), China (CN), South Korea (KR), Hong Kong (HK), Taiwan (TW), Singapore (SG), Indonesia (ID), Malaysia (MY), and Thailand (TH).
Values are calculated from twenty years monthly data ranging from 11/1995 to 10/2015. All stock and stock index returns are calculated as π π‘ =100 Γ (log(ππ‘) β log (ππ‘β1)), where ππ‘ denotes either the individual stock price or the stock market index.
Mkt_returnt SMBt HMLt RMWt CMAt WMLt OMUt IMNt
China
MEAN 0.6083 0.7345 0.3714 0.3341 -0.0499 -0.2025 -0.2459 0.1081
STD 9.3753 2.4142 1.8452 1.7963 1.6312 2.1260 1.9027 1.5108
Japan
MEAN -0.0879 0.2062 0.7112 0.4297 0.1429 -0.0298 -0.1216 0.5464
STD 5.8844 2.2246 2.0779 1.4399 1.3790 2.2815 1.4340 2.2559
Korea
MEAN 0.1622 0.1553 0.9153 0.6339 0.2802 0.1176 0.1630 0.6308
STD 7.7810 3.3579 2.4334 2.0826 2.7215 3.0425 1.9139 2.3097
Taiwan
MEAN 0.0614 0.4210 0.5068 0.4831 0.0015 -0.2763 -0.2291 0.2024
STD 6.2904 2.0693 3.1924 2.1893 1.7710 2.6910 1.7464 2.5533
HK
MEAN -0.4066 0.8916 0.8390 1.4638 0.4811 0.1569 0.7707 0.9043
STD 9.1235 3.6404 2.0805 2.6105 2.5335 2.9135 2.4153 3.4853
Singapore
MEAN -0.2704 0.2500 0.5960 1.0043 0.3841 0.1807 0.4710 0.8711
STD 7.5022 2.8364 2.0429 2.0844 2.1244 3.1180 2.4755 2.8311
Thailand
MEAN 0.2363 0.7471 0.6201 0.6867 0.2064 -0.1901 -0.0423 0.1827
STD 6.6505 2.8835 2.8745 2.6337 2.5568 3.9926 2.2176 3.2305
Indonesia
MEAN 0.4025 0.6227 0.6604 0.7225 0.1798 -0.1071 -0.6506 0.6548
STD 7.3205 3.1478 3.7774 3.2556 3.6622 4.0896 2.8328 3.3531
Malaysia
MEAN -0.0665 0.0806 0.5157 1.0888 0.6561 0.0029 -0.3363 0.7519
STD 7.3902 2.7765 1.7586 2.1179 1.6041 2.4803 1.4827 2.2593
27
Table 3. Stock returns and the three-factor model
This table reports the estimation results from the following equation:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅π‘ + π½3π»ππΏπ‘ + ππ,π‘, (2)
where π π,π‘ is the monthly return for stock i in month t and ππ,π‘ is the risk free rate.15 On the right hand side of the model, π π,π‘ is the return of the
equal-weight (EW) market portfolio. SMBt and HMLt, represent the return difference between the small size stock portfolio and the large size stock
portfolio, and between the high book-to-market (B/M) equity stock portfolio and the low B/M stock portfolio. The results are based on nine Asian
markets: Japan (JP), China (CN), South Korea (KR), Hong Kong (HK), Taiwan (TW), Singapore (SG), Indonesia (ID), Malaysia (MY), and
Thailand (TH). The sample covers 20 years monthly data ranging from 11/1995 to 10/2015. The t-statistics are in parentheses, ***, ** and *
indicate significance at the 1%, 5% and 10% levels, respectively.
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Intercept -0.1318***
(-18.99)
-0.1263***
(-13.36)
-0.2116***
(-16.06)
-0.1422***
(-15.32)
-0.2298***
(-13.06)
-0.3913***
(-24.64)
-0.2402***
(-15.43)
-0.4022***
(-19.97)
-0.3097***
(-29.41)
Mkt_returnt 0.9916***
(1401.30)
0.8497***
(495.85)
1.0584***
(670.09)
1.1934***
(797.46)
0.9828***
(697.18)
0.9731***
(466.40)
0.9964***
(427.01)
0.9559***
(285.15)
1.0286***
(676.33)
SMBt 0.1510***
(58.80)
0.1983***
(44.70)
0.1719***
(47.51)
0.1204***
(28.61)
-0.0553***
(-16.72)
0.1393***
(26.60)
0.0986***
(18.17)
0.1435***
(22.37)
0.2597***
(76.84)
HMLt 0.0246***
(6.72)
0.1103***
(24.34)
0.1901***
(37.40)
-0.0684***
(-23.23)
0.0311***
(5.23)
0.1895***
(25.42)
0.1001***
(19.24)
0.2081***
(35.28)
0.2148***
(35.95)
R2 0.4203 0.1801 0.2447 0.3146 0.1987 0.2237 0.1636 0.1585 0.3099
15 We use one month U.S. T-bill rate as the risk free rate.
28
Table 4. Stock returns and the five-factor model
This table reports the estimation results from the following equation:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅π‘ + π½3π»ππΏπ‘ + π½4π πππ‘ + π½5πΆππ΄π‘ + ππ,π‘ , (3)
where π π,π‘ is the monthly return for stock i in month t and ππ,π‘ is the risk free rate.16 On the right hand side of the model, π π,π‘ is the return of the
equal-weight (EW) market portfolio. SMBt and HMLt, represent the return difference between the small size stock portfolio and the large size stock
portfolio, and between the high book-to-market (B/M) equity stock portfolio and the low B/M stock portfolio. RMWt and CMAt represent the
profitability and investment factors, and they are defined as the return difference between the robust profitability stock portfolio and the weak
profitability stock portfolio, and between the conservative (low investment) stock portfolio and the aggressive (high investment) stock portfolio,
respectively. Profitability is measured as EBIT divided by sales, and investment is the change in total assets from year t-2 to t-1. The results are
based on nine Asian markets: Japan (JP), China (CN), South Korea (KR), Hong Kong (HK), Taiwan (TW), Singapore (SG), Indonesia (ID), Malaysia
(MY), and Thailand (TH). The sample covers 20 years monthly data ranging from 11/1995 to 10/2015. The t-statistics are in parentheses, ***, **
and * indicate significance at the 1%, 5% and 10% levels, respectively.
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Intercept -0.1206***
(-16.10)
-0.0883***
(-8.59)
-0.1529***
(-10.65)
-0.0529***
(-5.36)
-0.2063***
(-12.17)
-0.5427***
(-29.43)
-0.2989***
(-17.54)
-0.2124***
(-10.00)
-0.3575***
(-28.23)
Mkt_returnt 0.9921***
(1373.12)
0.8513***
(485.08)
1.0556***
(639.01)
1.1704***
(710.48)
0.9742***
(528.52)
0.9680***
(434.61)
0.9838***
(363.74)
0.9753***
(279.91)
1.0273***
(669.14)
SMBt 0.1734***
(49.96)
0.1879***
(42.28)
0.1863***
(43.58)
0.1086***
(22.28)
-0.0091**
(-2.19)
0.1928***
(31.04)
0.1029***
(18.41)
0.0932***
(12.87)
0.2829***
(60.07)
HMLt 0.0321***
(8.32)
0.0323***
(6.92)
0.1248***
(22.80)
-0.0402***
(-11.50)
0.0583***
(9.40)
0.1678***
(21.57)
0.0927***
(15.31)
0.1661***
(26.18)
0.1843***
(29.66)
RMWt -0.0929***
(-18.92)
0.1624***
(25.48)
-0.0869***
(-12.81)
-0.2149***
(-40.73)
0.0278***
(3.63)
0.1138***
(12.53)
0.0463***
(6.15)
-0.1628***
(-25.29)
-0.0810***
(-12.38)
CMAt 0.0948***
(16.93)
-0.3839***
(-56.16)
0.0413***
(8.42)
0.2045***
(30.14)
0.1046***
(13.76)
-0.0156*
(-1.70)
0.0043
(0.57)
-0.0623***
(-10.23)
0.0787***
(9.62)
R2 0.4201 0.1810 0.2454 0.3159 0.1999 0.2240 0.1631 0.1625 0.3112
16 We use one month U.S. T-bill rate as the risk free rate.
29
Table 5. Stock returns and the alternative momentum, P/E ratio, and dividend yield factors
This table reports the estimation results from the following equation:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππΏπ‘ + π½3ππππ‘ + π½4πΌπππ‘ + ππ,π‘ , (3)β
where π π,π‘ is the monthly return for stock i in month t and ππ,π‘ is the risk free rate.17 On the right hand side of the model, π π,π‘ is the return of the
equal-weight (EW) market portfolio. SMBt and HMLt, represent the return difference between the small size stock portfolio and the large size stock
portfolio, and between the high book-to-market (B/M) equity stock portfolio and the low B/M stock portfolio. WMLt, OMUt, and IMNt represent the
return difference between the winner stock portfolio and the loser stock portfolio, between the overvalued stock portfolio (high P/E) minus the
undervalued stock portfolio (low P/E), and between the income stock portfolio (high dividend yield) minus the non-income stock portfolio (low
dividend yield), respectively. The results are based on nine Asian markets: Japan (JP), China (CN), South Korea (KR), Hong Kong (HK), Taiwan
(TW), Singapore (SG), Indonesia (ID), Malaysia (MY), and Thailand (TH). The sample covers 20 years monthly data ranging from 11/1995 to
10/2015. The t-statistics are in parentheses, ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Intercept -0.1279***
(-17.38)
-0.0821***
(-8.58)
0.0157
(1.19)
-0.1652***
(-17.47)
-0.1396***
(-9.28)
-0.1746***
(-10.46)
-0.2075***
(-13.00)
-0.3903***
(-18.54)
-0.2272***
(-20.03)
Mkt_returnt 0.9931***
(1250.43)
0.8001***
(370.81)
1.0496***
(618.45)
1.1279***
(645.13)
1.0071***
(487.12)
0.9073***
(332.58)
0.9766***
(283.51)
0.9721***
(302.39)
1.0345***
(576.53)
SMBt 0.1482***
(39.61)
0.1082***
(21.34)
0.1907***
(50.83)
0.0101**
(2.29)
-0.0221***
(-5.48)
0.1197***
(21.48)
0.1081***
(18.47)
0.1637***
(24.95)
0.2767***
(71.84)
WMLt 0.0199***
(5.54)
-0.0904***
(-20.14)
0.0858***
(18.98)
-0.1998***
(-52.63)
0.0412***
(9.27)
-0.1330***
(-23.59)
-0.0541***
(-12.77)
-0.0683***
(-11.79)
0.0711***
(15.82)
OMUt -0.1275***
(-26.17)
-0.3636***
(-43.66)
-0.1134***
(-15.62)
-0.0460***
(-8.31)
0.0109
(1.49)
-0.3003***
(-38.82)
-0.0621***
(-7.78)
-0.1557***
(-19.95)
-0.0668***
(-8.76)
IMNt -0.1807***
(-33.35)
0.0074
(1.45)
-0.1288***
(-22.45)
-0.1939***
(-46.59)
0.0934***
(14.74)
0.0187***
(2.77)
-0.0155**
(-2.36)
0.0275***
(4.21)
-0.0479***
(-8.35)
R2 0.4204 0.1804 0.2453 0.3166 0.1998 0.2258 0.1631 0.1615 0.3110
17 We use one month U.S. T-bill rate as the risk free rate.
30
Table 6. Stock returns and the eight-factor model
This table reports the estimation results from the following equation:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅π‘ + π½3π»ππΏπ‘ + π½4π πππ‘ + π½5πΆππ΄π‘ + π½6πππΏπ‘ + π½7ππππ‘ + π½8πΌπππ‘ + ππ,π‘, (4)
where π π,π‘ is the monthly return for stock i in month t and ππ,π‘ is the risk free rate.18 On the right hand side of the model, π π,π‘ is the return of the
equal-weight (EW) market portfolio. SMBt and HMLt, represent the return difference between the small size stock portfolio and the large size stock
portfolio, and between the high book-to-market (B/M) equity stock portfolio and the low B/M stock portfolio. RMWt and CMAt represent the
profitability and investment factors, and they are defined as the return difference between the robust profitability stock portfolio and the weak
profitability stock portfolio, and between the conservative (low investment) stock portfolio and the aggressive (high investment) stock portfolio,
respectively. WMLt, OMUt, and IMNt represent the return difference between the winner stock portfolio and the loser stock portfolio, between the
overvalued stock portfolio (high P/E) minus the undervalued stock portfolio (low P/E), and between the income stock portfolio (high dividend yield)
minus the non-income stock portfolio (low dividend yield), respectively. The results are based on nine Asian markets: Japan (JP), China (CN), South
Korea (KR), Hong Kong (HK), Taiwan (TW), Singapore (SG), Indonesia (ID), Malaysia (MY), and Thailand (TH). The sample covers 20 years
monthly data ranging from 11/1995 to 10/2015. The t-statistics are in parentheses, ***, ** and * indicate significance at the 1%, 5% and 10% levels,
respectively.
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Intercept -0.1081***
(-14.18)
-0.0600***
(-5.82)
-0.1122***
(-7.63)
-0.1279***
(-12.86)
-0.0636***
(-4.68)
-0.3688***
(-19.49)
-0.2748***
(-15.98)
-0.2422***
(-11.12)
-0.3690***
(-28.11)
Mkt_returnt 0.9927***
(1216.25)
0.8069***
(373.52)
1.0498***
(607.76)
1.1315***
(600.60)
0.9894***
(415.19)
0.8983***
(319.34)
0.9587***
(256.55)
0.9740***
(273.41)
1.0220***
(553.66)
SMBt 0.1432***
(37.11)
0.0918***
(17.97)
0.1524***
(34.39)
0.0724***
(14.65)
0.0042
(0.91)
0.1242***
(18.86)
0.0879***
(13.99)
0.0949***
(13.05)
0.2806***
(59.53)
HMLt 0.0100**
(2.02)
-0.0312***
(-4.86)
0.1940***
(31.06)
-0.0457***
(-12.17)
0.0537***
(8.59)
0.1339***
(15.01)
0.0758***
(10.44)
0.1231***
(16.32)
0.2393***
(34.95)
RMWt -0.0473***
(-8.70)
0.1422***
(21.88)
-0.0570***
(-7.80)
-0.0817***
(-13.79)
0.0004
(0.05)
0.1969***
(21.20)
0.0484***
(6.43)
-0.1743***
(-24.99)
-0.0773***
(-11.65)
CMAt 0.0516***
(7.80)
-0.3741***
(-51.95)
0.0318***
(6.46)
0.1635***
(23.00)
0.1010***
(13.12)
-0.0982***
(-10.50)
0.0056
(0.73)
-0.0619***
(-10.05)
0.1054***
(11.98)
WMLt 0.0156***
(3.95)
-0.0284***
(-5.90)
0.1035***
(22.36)
-0.1782***
(-44.87)
0.0271***
(5.89)
-0.0994***
(-17.04)
-0.0378***
(-8.25)
0.0022
(0.34)
0.0947***
(20.40)
18 We use one month U.S. T-bill rate as the risk free rate.
31
OMUt -0.0986***
(-16.49)
-0.3723***
(-43.05)
-0.0303***
(-3.92)
-0.0509***
(-8.72)
-0.0053
(-0.70)
-0.3187***
(-39.80)
-0.0444***
(-5.37)
-0.1130***
(-12.97)
0.0358***
(4.26)
IMNt -0.1658***
(-25.90)
-0.0104
(-1.56)
-0.1801***
(-27.70)
-0.1718***
(-34.98)
0.0702***
(10.13)
-0.0460***
(-5.91)
-0.0371***
(-5.21)
-0.0061
(-0.91)
-0.0367***
(-5.96)
R2 0.4204 0.1821 0.2460 0.3173 0.1999 0.2264 0.1632 0.1627 0.3115
32
Table 7. Stock returns and international fundamental factors
This table reports the results from the modified three-factor model with all domestic fundamental factors (SMBt and HMLt,) replaced by international
factors (SMBint,t and HMLint,t,):
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅πππ‘,π‘ + π½3π»ππΏπππ‘,π‘ + ππ,π‘, (2)β
where the subscript βintβ of the independent variables means that the factor are from international markets. We test three sets of international factors:
the U.S., the global, and Japan in Panel A, B, and C, respectively. To save space, only the absolute value of intercepts and R squared of each
regression are reported in the table.19 The results are based on nine Asian markets: Japan (JP), China (CN), South Korea (KR), Hong Kong (HK),
Taiwan (TW), Singapore (SG), Indonesia (ID), Malaysia (MY), and Thailand (TH). The sample covers 20 years monthly data ranging from 11/1995
to 10/2015. The t-statistics are in parentheses, ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
Panel A. Stock returns and the U.S. fundamental factors
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Abs(Intercept) 0.3241***
(36.43)
0.4464***
(-43.90)
0.4337***
(-29.76)
0.5828***
(-52.36)
1.1242***
(-79.70)
1.2379***
(-71.25)
0.6577***
(-39.01)
0.7070***
(-32.13)
0.8941***
(-72.23)
R2 0.0194 0.0386 0.0509 0.0688 0.0557 0.0866 0.1300 0.0400 0.0544
Panel B. Stock returns and the global fundamental factors
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Abs(Intercept) 0.1666***
(19.11)
0.6161***
(-62.88)
0.1746***
(-12.31)
0.6357***
(-58.78)
1.2959***
(-94.98)
1.1848***
(-71.01)
0.3427***
(-20.75)
0.5079***
(-23.57)
0.6611***
(-54.64)
R2 0.0283 0.0784 0.0709 0.0915 0.0871 0.1222 0.0402 0.0480 0.0684
Panel B. Stock returns and Japanese fundamental factors
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Abs(Intercept)
0.5304***
(61.94)
0.4346***
(-46.92)
0.0060
(-0.43)
0.0394***
(-3.61)
0.4307***
(-31.49)
0.6305***
(-37.15)
0.0575***
(-3.54)
0.0709***
(3.32)
0.3671***
(-30.51)
R2 0.0279 0.1385 0.0588 0.0381 0.0470 0.0659 0.0317 0.0240 0.0395
19 Most coefficients of the fundamental factors are significant and have the same signs with those of local fundamental factors. The results are available upon
requests.
33
Table 8. Stock returns and fundamental factors under different market conditions
This table reports the results from the following complete eight-factor model under different market condition:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅π‘ + π½3π»ππΏπ‘ + π½4π πππ‘ + π½5πΆππ΄π‘ + π½6πππΏπ‘ + π½7ππππ‘ + π½8πΌπππ‘ + ππ,π‘, (4)
We divide the data according to two sets of market conditions: the crisis vs. non-crisis and the down markets vs. the up markets, and the results are
reported in Panel A and B, respectively. We define three crisis periods in our sampleβs time period: the first is the Asian financial crisis, ranging
from July 1997 to August 1998; the second is the dot com bubble crashes, ranging from March 2000 to March 2001; and the third is the recent
global financial crisis, ranging from July 2007 to June 2009. We define the up markets when the local market return is positive and the down
markets when it is negative. To save space, only the absolute value of intercepts and R squared of each regression are reported in the table.20 The
results are based on nine Asian markets: Japan (JP), China (CN), South Korea (KR), Hong Kong (HK), Taiwan (TW), Singapore (SG), Indonesia
(ID), Malaysia (MY), and Thailand (TH). The sample covers 20 years monthly data ranging from 11/1995 to 10/2015. The t-statistics are in
parentheses, ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
Panel A. Crisis vs. non-crisis periods
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Crisis Periods
Abs(Intercept) -0.4385***
(-18.00)
-0.9347***
(-28.67)
-0.2169***
(-5.29)
-0.4126***
(-15.35)
-0.2391***
(-5.26)
-1.0669***
(-21.03)
-0.0708
(-1.31)
-0.4490***
(-6.27)
-0.3354***
(-8.20)
R2 0.5109 0.2050 0.3520 0.4144 0.3346 0.3312 0.1817 0.2091 0.3941
Non-crisis
Periods
Abs(Intercept) -0.0603***
(-7.41)
-0.0408***
(-3.60)
-0.0590***
(-3.72)
-0.0343***
(-3.17)
-0.2081***
(-10.49)
-0.1192***
(-5.60)
-0.3595***
(-19.74)
-0.1955***
(-8.60)
-0.2954***
(-20.52)
R2 0.3934 0.1660 0.1733 0.2682 0.1339 0.1644 0.1454 0.1291 0.2380
20 Most coefficients of the fundamental factors are significant and have the same signs with those of local fundamental factors. The results are available upon
requests.
34
Panel B. Down vs. up markets
China Japan Korea Taiwan HK Singapore Thailand Indonesia Malaysia
Down markets
Abs(Intercept) -0.1703***
(-3.34)
-0.5494***
(-7.16)
0.0458
(0.52)
-0.2613***
(-3.62)
0.1283
(1.33)
-0.1158
(-1.06)
-0.0631
(-0.55)
-0.3976***
(-2.73)
-0.2109***
(-2.81)
R2 0.2290 0.0806 0.1781 0.1767 0.1666 0.1477 0.1082 0.1006 0.2417
Up markets
Abs(Intercept) -0.3431***
(-21.66)
0.2484***
(11.35)
0.2031***
(7.42)
0.0131
(0.66)
-0.3494***
(-11.86)
-0.0929**
(-2.44)
-0.1785***
(-5.42)
0.2072***
(5.59)
-0.4405***
(-18.44)
R2 0.2076 0.0649 0.1171 0.1536 0.0792 0.1404 0.0666 0.0864 0.1886
35
Table 9. Industry portfolio returns and fundamental factors
This table reports the results from the following complete eight-factor model to estimate industry portfolio returns:
π π,π‘ β ππ,π‘ = π½0 + π½1(π π,π‘ β ππ,π‘) + π½2πππ΅π‘ + π½3π»ππΏπ‘ + π½4π πππ‘ + π½5πΆππ΄π‘ + π½6πππΏπ‘ + π½7ππππ‘ + π½8πΌπππ‘ + ππ,π‘, (4)
where π π,π‘ is the monthly return of industry portfolio i for month t and ππ,π‘ is the risk free rate.21 The ten industries are defined by the industry
classification of Thomson Reuters Datastream.22 The same industry portfolios of all the nine Asian stock markets are combined to form ten
industry portfolio data groups23 to estimate the relation between fundamental factors and portfolio returns. The t-statistics are in parentheses, ***,
** and * indicate significance at the 1%, 5% and 10% levels, respectively.
Oil&Gas Basic Mat. Industrials Consumer
goods
Health
care
Consumer
services Telecom Utilities Financial Technology
Intercept -0.0441
(-0.24)
-0.4964***
(-13.58)
-0.2302***
(-8.66)
-0.2162***
(-7.04)
0.2853***
(4.29)
-0.1109**
(-2.41)
0.5407***
(2.76)
-0.0519
(-0.66)
-0.2346***
(-6.22)
0.4372***
(8.46)
Mkt_returnt 1.0225***
(42.22)
1.0572***
(236.74)
1.0363***
(298.94)
0.9461***
(239.68)
0.9421***
(119.33)
0.9445***
(154.83)
0.9595***
(35.13)
0.9249***
(101.22)
1.0045***
(202.74)
1.0952***
(147.30)
SMBt -0.1699***
(-2.81)
0.0833***
(6.19)
0.1913***
(19.45)
0.2427***
(22.65)
0.3180***
(13.11)
0.0141
(0.89)
-0.3317***
(-5.14)
-0.1512***
(-5.21)
-0.2136***
(-16.97)
0.0541***
(2.72)
HMLt 0.0791
(1.07)
0.3636***
(23.42)
0.0733***
(6.56)
0.1350***
(10.92)
-0.1750***
(-6.03)
0.0595***
(3.10)
-0.3768***
(-5.02)
0.3313***
(9.32)
0.3806***
(25.53)
-0.6847***
(-34.20)
RMWt 0.0572
(0.72)
0.0805***
(4.55)
-0.0522***
(-3.94)
-0.1146***
(-8.12)
-0.2506***
(-7.59)
-0.0225
(-1.07)
0.2156**
(2.52)
-0.0234
(-0.57)
0.1068***
(6.59)
-0.1674***
(-6.13)
CMAt 0.0974
(1.21)
0.0468***
(2.70)
0.0640***
(4.73)
0.0343**
(2.45)
-0.0517
(-1.64)
-0.0123
(-0.56)
-0.1439
(-1.64)
-0.0591
(-1.41)
-0.0138
(-0.82)
-0.0039
(-0.14)
WMLt 0.0394
(0.70)
0.1105***
(8.60)
-0.0440***
(-4.72)
0.0607***
(5.99)
0.1875***
(8.08)
0.0500***
(3.30)
-0.1856***
(-3.15)
0.1528***
(5.31)
-0.1082***
(-9.11)
-0.3094***
(-16.92)
OMUt -0.2595***
(-3.10)
-0.0791***
(-4.30)
-0.0259*
(-1.92)
0.1054***
(7.16)
0.0050
(0.15)
0.1016***
(4.72)
0.1770**
(2.03)
0.1142***
(2.91)
0.0079
(0.47)
0.0285
(1.09)
IMNt -0.0434
(-0.58)
-0.0591***
(-3.67)
-0.0451***
(-3.80)
0.1713***
(13.43)
0.2177***
(7.20)
0.0202
(1.04)
-0.1787**
(-2.29)
0.1277***
(3.32)
-0.1073***
(-7.12)
-0.3991***
(-16.89)
R2 0.2424 0.3124 0.2809 0.2393 0.2593 0.2260 0.2225 0.3301 0.2640 0.2817
21 We use one month U.S. T-bill rate as the risk free rate. 22 The 10 industry sectors are Oil and Gas, Basic Materials, Industrials, Consumer Goods, Consumer Services, Health Care, Telecommunications, Utilities,
Financials, and Technology by level-2 industry classification from Datastream. 23 Japan (JP), China (CN), South Korea (KR), Hong Kong (HK), Taiwan (TW), Singapore (SG), Indonesia (ID), Malaysia (MY), and Thailand (TH).