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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 investors’ 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:

𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + ∑ 𝛽𝑖𝑍𝑠,𝑡𝑆𝑠=1 + 𝜀𝑖,𝑡, (1)

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where 𝑅𝑖,𝑡 is the monthly return of security or portfolio i for month t and 𝑟𝑓,𝑡 is the risk free rate.4

On the right hand side of the model, 𝑅𝑀,𝑡 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:

𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝜀𝑖,𝑡, (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:

𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝛽4𝑅𝑀𝑊𝑡 + 𝛽5𝐶𝑀𝐴𝑡 + 𝜀𝑖,𝑡, (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:

𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝛽4𝑅𝑀𝑊𝑡 + 𝛽5𝐶𝑀𝐴𝑡 + 𝛽6𝑊𝑀𝐿𝑡 +

𝛽7𝑂𝑀𝑈𝑡 + 𝛽8𝐼𝑀𝑁𝑡 + 𝜀𝑖,𝑡, (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. French’s 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 𝑅𝑡 = 100 × (log(𝑃𝑡) − log (𝑃𝑡−1)), where 𝑃𝑡

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 don’t 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:

𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑊𝑀𝐿𝑡 + 𝛽3𝑂𝑀𝑈𝑡 + 𝛽4𝐼𝑀𝑁𝑡 + 𝜀𝑖,𝑡, (3)’

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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 3’. 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 3’

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 French’s data library8 and re-run

model 2 the three-factor model, only replacing all domestic fundamental factors with international

factors:

𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑆𝑀𝐵𝑖𝑛𝑡,𝑡 + 𝛽3𝐻𝑀𝐿𝑖𝑛𝑡,𝑡 + 𝜀𝑖,𝑡, (2)’

8 http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

15

where the subscript “int” 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 markets’ 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 sample’s 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.

17

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,

there’s 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 regressions’ 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 factors’ 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 don’t 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


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


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 𝑅𝑡 =100 × (log(𝑃𝑡) − log (𝑃𝑡−1)), where 𝑃𝑡 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:

𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝜀𝑖,𝑡, (2)

where 𝑅𝑖,𝑡 is the monthly return for stock i in month t and 𝑟𝑓,𝑡 is the risk free rate.15 On the right hand side of the model, 𝑅𝑀,𝑡 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


28

Table 4. Stock returns and the five-factor model


𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝛽4𝑅𝑀𝑊𝑡 + 𝛽5𝐶𝑀𝐴𝑡 + 𝜀𝑖,𝑡 , (3)



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.



(-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)


(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


29

Table 5. Stock returns and the alternative momentum, P/E ratio, and dividend yield factors


𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑊𝑀𝐿𝑡 + 𝛽3𝑂𝑀𝑈𝑡 + 𝛽4𝐼𝑀𝑁𝑡 + 𝜀𝑖,𝑡 , (3)’



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.



(-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)


(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


30

Table 6. Stock returns and the eight-factor model


𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑆𝑀𝐵𝑡 + 𝛽3𝐻𝑀𝐿𝑡 + 𝛽4𝑅𝑀𝑊𝑡 + 𝛽5𝐶𝑀𝐴𝑡 + 𝛽6𝑊𝑀𝐿𝑡 + 𝛽7𝑂𝑀𝑈𝑡 + 𝛽8𝐼𝑀𝑁𝑡 + 𝜀𝑖,𝑡, (4)



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.



(-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)


(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)


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,):

𝑅𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛽0 + 𝛽1(𝑅𝑀,𝑡 − 𝑟𝑓,𝑡) + 𝛽2𝑆𝑀𝐵𝑖𝑛𝑡,𝑡 + 𝛽3𝐻𝑀𝐿𝑖𝑛𝑡,𝑡 + 𝜀𝑖,𝑡, (2)’

where the subscript “int” 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


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


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


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:


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 sample’s 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


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


(-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


Down markets


(-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


(-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:


where 𝑅𝑖,𝑡 is the monthly return of industry portfolio i for month t and 𝑟𝑓,𝑡 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)


(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).

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