TCER Working Paper Series

Value Premium in Japanese Market: Statistical (Re)appraisal
Leonardo Cadamuro
Tokuo Iwaisako
March 2023
Working Paper E-180
https://www.tcer.or.jp/wp/pdf/e180.pdf

TOKYO CENTER FOR ECONOMIC RESEARCH
1-7-10-703 Iidabashi, Chiyoda-ku, Tokyo 102-0072, Japan©2023 by Leonardo Cadamuro and Tokuo Iwaisako.
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explicit permission provided that full credit, including ©notice, is given to the source.

Abstract
This paper examines the recent decline of the value premium in the Japanese market
since the late 2000s, and discuss similarities and differences between the Japaneseand US markets. We adopt the analytical framework of Fama and French (2021) using
predictive regression with the book-to-market (BM) ratio and the framework by Arnott
et al. (2021) based on the return decomposition of HML returns. The level andvolatility of the Japanese BM ratio significantly changed toward the end of 1990s;
thus, careful consideration in splitting the sample periods is needed in examining
the predicting ability of BM ratio about the portfolio returns sorted by the firm
size and BM ratio. We find the predictable component of Japanese HML returns isrelatively stable over time, and the recent decline in HML returns is mostly
explained by the unpredictable decline in the valuation of value stocks relative to
growth stocks after the Global Financial Crisis in the late 2000s. This isconsistent with the results reported in existing studies on the US market. The
evidence provided by the decomposition of HML returns also supports the findings ofthis study’s analysis.

Leonardo Cadamuro
Hitotsubashi University
Graduate School of Economics
Naka 2-1, Kunitachi, Tokyo 186-8601
cadaleo92@gmail.com

Tokuo Iwaisako
TCER
andHitotsubashi University
Institute of Economic Research
Naka 2-1, Kunitachi, Tokyo 186-8603
iwaisako@ier.hit-u.ac.jp

Value Premium in Japanese Market: Statistical (Re)appraisal ∗Leonardo Cadamuro†Tokuo Iwaisako‡
March 20, 2023

Abstract
This paper examines the recent decline of the value premium in the Japanese market since thelate 2000s, and discuss similarities and differences between the Japanese and US markets. We
adopt the analytical framework of Fama and French (2021) using predictive regression with the
book-to-market (BM) ratio and the framework by Arnott et al. (2021) based on the return
decomposition of HML returns. The level and volatility of the Japanese BM ratio significantly
changed toward the end of 1990s; thus, careful consideration in splitting the sample periods isneeded in examining the predicting ability of BM ratio about the portfolio returns sorted by
the firm size and BM ratio. We find the predictable component of Japanese HML returns isrelatively stable over time, and the recent decline in HML returns is mostly explained by theunpredictable decline in the valuation of value stocks relative to growth stocks after the Global
Financial Crisis in the late 2000s. This is consistent with the results reported in existing studies
on the US market. The evidence provided by the decomposition of HML returns also supports
the findings of this study’s analysis.

Keywords: Japanese stock market, value premium, HML factor, Fama-French three-factor
model

JEL Codes: G11, G12, G15∗

The analysis in Section 2 is partially based on Iwaisako (2001), but we reworked our empirical analyses with an
expanded data set, so that the main arguments and conclusions are changed to some extent. We thank Kohei Aono,
Yasushi Ishikawa, Ryuta Sakemoto, Hitoshi Takehara, and the participants of Nippon Finance Association meetingsin 2021 and 2022 for their comments. This work has been supported by 2022 Joint Research Program of Joint Usageand Research Center Programs at the Institute of Economic Research, Hitotsubashi University and JSPS KAKENHI
Grant Number 18H00871.
†
Graduate School of Economics, Hitotsubashi University; cadaleo92@gmail.com‡
Corresponding author; Institute of Economic Research, Hitotsubashi University; iwaisako@ier.hit-u.ac.jp

1

Introduction“Is value investing dead?” — This is a question that has troubled finance researchers and practitioners in recent years (see Lee, 2020, for example, for the discussion about US market). Similarly,
the significant decline in HML return in recent years has become a major issue for the Japanesemarket as well (Ishikawa, 2021, Iwaisako, 2021, Takehara, 2021).
The importance of the HML factor in both academic literature and financial practice was firsthighlighted in the series of works by Fama and French in the early 1990s . They initiated the surgeof the systematic quant-type approach to value investment which utilizes the book-to-market (BM)ratio and HML factor in their three-factor model. 1 Fama and French (1992, 1993), using 28 years’
worth of US market data from July 1963 to June 1991, present clear evidence that value stocks
with a high BM ratio had higher average returns compared to growth stocks with a low BM ratio.
In the subsequent paper, Fama and French (2017) further confirm the existence of significant value
premium in stock markets in European and Asia-Pacific regions including Japan for the period from
July 1990 to December 2015. For the Japanese market in particular, Chan, Hamao, and Lakonishok(1991) are probably the first to find that value premium not only exists in the Japanese market,
but is in fact larger than in the US market, while size premium had been mostly insignificant.
By the mid-2010s, however, the finance industry has recognized that there have been significant
declines in the value premium of US and Japanese stock markets. Fama and French (2017) havealready acknowledged that the value premium in the US became smaller in their latter subsample.
In retrospect, the decline started in Japan and in the US around the time of the Global Financial
Crisis (GFC) which was triggered by the failure of Lehman Brothers in September 2008. Having
observed the value premium diminish in the 2010s, some researchers on the mechanism underlying
the recent decline. This includes Arnott et al. (2020), Fama and French (2021), Israel et al. (2021),
Lee (2020), Lev and Srivastava (2022), Linnainman and Roberts (2018).
In this paper, we use the analytical frameworks by Fama and French (2021) and Arnott etal. (2020) to analuyze how the value premium has declined in Japan and discuss the similarities
1

Often the “value premium” and the “HML factor” refer to the same thing, so the distinction between the two
is not very strict in the general discussions in the introduction and the conlusion of this paper. On the other hand,in Fama and French (2021), the value premium is defined as the excess return of a portfolio of high BM stocks overa market portfolio.

1and the differences from the decline in HML’s return in the US. The remainder of this paper isorganized as follows. Section 2 reports the averages of Fama-French factors and the portfolios
sorted by size/book-to-market ratio, in the full sample and various subsamples. Section 3 explains
how we adopt the framework of Fama and French (2021) to examine if the predictability of HML
returns using the BM ratio varies over time. Section 4 explains how we use return decomposition
proposed by Arnott et al. (2020) to consider the same problem through a different methodology.
Section 5 concludes the paper.

2

The Value Premium in the Japanese Market: An Overview

2.1

Data

We use the NPM Japanese Fama-French benchmark data from Financial Data Solutions, Inc. inthis paper. 2 The stocks included in the universe of the market to be analyzed are all individual
stocks provided in Financial Data Solutions, Inc. data, which consists of all the stocks in the Firstand Second sections of the Tokyo Stock Exchange, the TSE. The sample period is September 1977
to March 2021.
In examining the effectiveness of systematic trading strategies such as the Fama-French threefactor model, we have to take it into account that large institutional investors employing sophisticated trading strategies in Japanese market, in particular major foreign financial institutions, only
consider stocks of relatively large firm size in their actual investments.3 This is due to the extremely
low liquidity of the shares of smallest companies, the lack of mandatory dissemination of accounting
information in English for such companies, or the time lag before such information is included inpublic records. See Kang and Stultz (1997) and Kamesaka et al. (2003) for the analyses of foreign
investors’ trading patterns in the Japanese stock market. There is no clear information on thefirm size that is the dividing line for inclusion or exclusion from the investment universe of large
financial institutions, and such a line should be considered to vary over time. However, we believe2

For the detailed description refer to the following URL: https://fdsol.co.jp/npm.html
We thank several practitioners for pointing out the importance of this issue during our discussion with them on
earlier versions of this paper.
32that stocks in the smallest quartile of the Tokyo Stock Exchange’s Second Section, for example, tobe unlikely targets for frequent trading by major financial institutions.
For this reason, our analyses also use data for two additional sub-universes of stocks which
exclude the smallest firms in the TSE. The benchmark universe comprises the entire individual
stocks provided in Financial Data Solutions, Inc. data, which we call Entire Market. Our firstnarrower universe consists of individual stocks in the top 80% subgroup of Entire Market in termsof firm size, hence is called TOP 80 %. The second narrower universe consists of individual stocks
included in the Nikkei 225 index, and thus, is called Nikkei 225. The universe of stocks with thelargest average firm size is Nikkei 225, followed by TOP 80 %, then Entire Market. On the other
hand, Entire Market contains the largest number of the individual stocks: about 1,600 in 1990,
1,540 in 2000, 1,970 in 2010, and 2,400 in 2020. Owing to the ways in which they have been
constructed, the number of the individual stocks in TOP 80 % is 80% of Entire Market and thenumber in Nikkei 225 is 225. Nikkei 225 contains about 9% to 14% of Entire Market depending on
the time periods.
Following Fama and French (2021), our analyses use the return data for seven portfolios, including market portfolio and six size-BM sorted portfolios. When considering a particular universeof individual stocks discussed above, market portfolio is defined as the value-weighted average of
all individual stocks in the corresponding universe. In calculating the data of the six individual
portfolios, first, the sample of individual stocks are divided into two groups based on firm size.
Small stocks are those whose market capitalization at the end of the month is below the medianof the corresponding universe of individual stocks, while big stocks are classified as those whosemarket capitalization is above the median. Second, stocks are divided into three groups based on
BM ratio. Value stocks are those with BM values above the 70th percentile, while growth stocks
are those below the 30th percentile. As a result, we have three BM-sorted portfolios: Small Value(SV), Big Value (BV), and their combination, Market Value (MV). Similarly, the three growthstock portfolios are Small Growth (SG), Big Growth (BG), and Market Growth (MG).

3

2.2

Value premium in the full sample

In Table 1, we present average excess returns of six size-BM sorted portfolios over the market
portfolio as well as three Fama-French factors for the full sample, for three different universes ofindividual stocks described in the previous subsection.

[Table 1 about here]

Averages of market portfolio over risk-free rate are almost equal regardless of which universeof individual stocks have been considered, and are approximately 0.3% on a monthly basis, and3.6% on an annual basis. The average of the HML factor for Entire Market is 0.79% (annualized,
9.5%), which is almost equal to that of Top 80% at 0.76% and slightly higher for Nikkei 225, at
0.85%. The average of SMB (size premium) is 0.04% for Entire Market and −0.07% for Top 80%.
Average SMB for Nikkei 225 is slightly higher at 0.15%. While size premium has never been clearlyobserved in the Japanese stock market, value premium has definitely existed and has been larger
compared to the US. These results are consistent with those reported in previous studies of theJapanese stock market, such as Chan et al. (1991).
Next, we turn our attention toward the excess returns of six size-BM sorted portfolios overthe market portfolio. The value-growth spread for market average M V − M G is 0.85% for Nikkei
225, 0.77% for Top 80%, and 0.80% for Entire Market. 4 Therefore no significant difference in thevalue-growth spread was observed, regardless of the size of the universes of stocks.

2.3

Value premium in subsamples before and after the Global Financial Crisis

To examine the decline of the value premium in recent years, we present Table 2 where we performthe same calculations as in Table 1 for several subsamples. We split subsamples into two different
dates. The first split date is at the end of 1998 and the beginning of 1999. We chose this as thefirst split date because the mean and volatility of Japanese BM ratios are very different before
4
The value-growth spread for market average is M V − M G and calculated as M V − M G = (M V − Rm) − (M G −Rm).

4and after this point, approximately, as we will discuss in detail later. The second split date is in
August and September of 2008 which roughly corresponds to the onset of the Global Financial
Crisis (GFC) following the collapse of Lehman Brothers in mid-September. We checked robustnessof our empirical analyses, using data delimited by several other neighboring dates such as at theend of 2007 and the end of 2008. The main empirical results have been discussed in detail in thefollowing sections, and were not affected by the delimitating dates, which were until approximately
one year before and after these dates.
In Table 2, we report the averages of Fama-French’s three factors in Panel A and excess returnsof six size-BM sorted portfolios over the market portfolio in Panel B, for five different time periods,
namely: (1) September 1977 to the end of 1998, (2) the beginning of 1999 to September 2008, (3-1)
September 2008 to the end of 2019, (3-2) September 2008 to the end of sample, March 2021, and(4) the beginning of 2020 to the end of sample, March 2021.

[Table 2 about here]

The SMB and HML averages in Panel A of Table 2 reveal some interesting findings. First,
a comparison of two subsamples prior to the global financial crisis shows that the average HML
is slightly larger in the Subsample (2) immediately before GFC than in the earlier Subsample(1) ending at 1998. In particular, for the Top 80% universe, the average HML increased by 0.20
percentage point (= 1.09% − 0.89%) from Subsample (1) to Subsample (2). Then, HML declined
after GFC in Subsample (3-1) by 0.67 percentage point (= 0.42% − 1.09%). The decline fromthe subsample before GFC to after GFC is observed for all three universes of stocks considered
here, but is largest for the Nikkei 225 universe at 0.96 percentage point (= 0.42% − 1.38%), whichcorresponds to a difference of more than 10 between percentage points on an annual basis.
On the other hand, the averages of SMB remain relatively stable over time for the Nikkei 225
universe. However, the SMB factor for Entire Market has increased from −0.13% in the 1999-2007
sample to 0.37% in the 2009-2019 sample, resulting in a 0.5 percentage point increase.
Next, we look at averages of six portfolios’ excess returns shown in Panel B of Table 2. We
found that the difference between the performance of value and growth stocks among large-cap
5stocks is the main cause of the large value premium in over the 10-year period prior to the global
financial crisis. If we take the Top 80% universe as an example, BV − Rm increases from 0.66%
in Subsample (1) to 0.88% in Subsample (2). Then, it goes down to 0.36% in the Subsample (3-1)
after GFC. In the same time periods, BG − Rm goes down from −0.25% in the subsample (1) to−0.37% to the Subsample (2) before GFC, and rebounds to −0.11% in the post GFC Subsample(3-1). As the result, the return difference between value stocks and growth stocks among large-cap
stocks, BV − BG, is a 0.91 percentage point in the Subsample (1). It climbs up to 1.25 percentage
point in the Subsample (2), then goes down to 0.47 percentage point in Subsample (3-1) after the
GFC. In the same time periods, SV − SG starts from 0.87 percentage point, went up slightly to
0.93 percentage point in Subsample (2), then declined to 0.38 percentage point in Subsample (3-1)
after the GFC.
Overall, HML factor’s return was apparently substantially higher before GFC and it was particulary higher in the period from 1999 to the summer of 2008. The increase of HML return fromthe sample before 1999 is mainly due to the higher performance of large-cap value stocks. The
difference in returns between value and growth stocks among large-cap stocks and among small-cap
stocks fluctuates over time, but the pattern of change is similar regardless of the universe of stocks
we are considering. However, time variation is more pronounced for the universe consists of largest
stocks only, Nikkei 225.

2.4

Covid-19 subsample

For researchers conducting empirical analyses today using long-term stock market data, handlingthe latest data period since the onset of the global pandemic caused by Covid-19 in early 2020 istroublesome. It is difficult to interpret the outcome of including post-2020 data in the empiricalanalysis until the impact of the current pandemic has sufficiently attenuated or after the new normal
state of the economy has been established. Thus, the remainder of the paper focuses mainly on theanalyses using data until the end of 2019. In this subsection, we will summarize empirical resultsof the subsample using data after 2020, specifically until the end of March 2021.
In Table 2, subsample (3-2) is the entire period after GFC including the Covid-19 subsample,
6

while Subsample (4) is a 15-month subsample of Covid-19. Sometime after the pandemic had
started in the second half of 2020, the fact that mRNA vaccines were successfully being developedby pharmaceutical companies led to increased financial news and reports by financial institutions
predicting a recovery in stock prices in general and resurrection of value investments.5 However,the Japanese data for the Covid-19 subsample in this paper does not support such wishful thinking about value investment. We found that the averages of market portfolio’s excess returns in
Subsample (4) are all positive, but both SMB and HML are negative in all universes of individual
stocks reported in Table 2, Panel A. Hence, large-cap stocks have outperformed small-cap stocks
and growth stocks have outperformed value stocks in the Covid-19 subsample until early 2021.
When comparing the performance of small-value and large-value stocks, the latter have outperformed for Entire Market and TOP 80%. Both are negative, but the absolute value is greater forsmall-cap value. For individual stocks in the Nikkei 225 universe, on the other hand, small-value
stocks have performed slightly better. However, in all cases, the difference between small-value andlarge-value is not statistically significant.
We observe a similar pattern for growth stocks. Large growth stocks have performed better for
Entire Market and TOP 80%. For Nikkei 225 stocks, small growth stocks have performed slightly
better. Since the Nikkei 225 universe contains only the largest 10% in the Tokyo market, theseresults suggest that it is large-cap growth stocks that have led the market recovery in Covid-19.
This result is in contrast with the result in the post-GFC sample ending in 2019, Subsample (3-1),
in which small growth stocks outperformed large growth stocks.

5
However, as for the US market from the spring of 2021 onwards, growth stocks have caught up and theresurgence of value investment proved to be very short-lived. See, for example, Bloomberg’s report in November 19,
2021, titled “A Year After Vaccine Jolt, Bet on Value Stocks Disappoints”
URL:
https://www.bloomberg.com/news/articles/2021-11-09/a-year-after-vaccine-jolt-the-bet-on-value-stocksdisappoints.

7

3

Predictive Regression

3.1

Predictive regression for excess return over the market portfolio benchmark

Our analyses in section 2 have focused on the unconditional HML return and excess return of sizeBM sorted portfolios relative to the market portfolio. After Cohen, Polk and Vuolteenaho (2003),
many studies have used the regressions for value premiums on lagged BM ratio. Here, we follow
Fama and French (2021) and estimate the following type of predictive regression:

Ri,t − RM,t = αi + βi (BMi,t−1 − BMM,t−1 ) + ϵt .

(1)

In order to make a direct comparison with Table 3 in Fama and French (2021), we estimatedequation (1) for six size-BM sorted portfolios: SV, BV, MV, SG, BG, and MG. We confirmed in
the previous section that there is in fact value premium in the Japanese market in the sample
prior to the GFC and its size is larger than that of the US market. Thus, we hypothesize thatthe coefficients of the predictor variables, one-month lagged excess BM (BMi,t−1 − BMM,t−1 ) is
positive.
Table 3 show the estimation results of equation (1) for using the data of TOP 80 % universe.
For all six portfolios, the estimated coefficients are insignificant and R2 are much smaller than thosereported in Fama and French (2021) for the US data. However, given the magnitude of the HML
factor in the pre-GFC sample in Table 2, we cannot accept the result that there is no predictability
using the BM ratio in the same period without further investigation. It is more plausible to interpretthe results in Table 3 as the consequence of the fact that the predictive ability using the BM ratio
is changing through time. Therefore, the analysis using subsamples is our next task.

[Table 3 about here]

8

3.2

Predicting ability of BM ratio in different sample periods

Fama and French (2021) estimate a variation of the equation (1) that includes dummy variables
both constant term αi and the coefficients of the predicting variable βi .6 However, our preliminary
analysis suggests that this empirical strategy does not work with the Japanese data since there is asignificant change in the behavior of the predicting variable, excess BM ratio, BMi,t−1 − BMM,t−1 ,in the late 1990s. Determining the exact break point of the series is a somewhat complicated matter
since there are many potential events which caused this in the second half of the 1990s and theearly 2000s, including the collapse of the bubble economy in early 1990s, the domestic banking crisis
in 1998, as well as Takenaka plan which involves restructuring of many banks and non-financial
companies in the early 2000s. 7
From a statistical point of view, on the other hand, it is evident that there was a significant
structural change in both the average and volatility of excess BM ratio in late 1990s as shownin Figure 1 and Figure 2. We plot the time series of the BM ratios of the market portfolio, theportfolio of value stocks, and the portfolio of growth stocks using the data of TOP 80% universe
in Figure 1. We show excess BM ratios of value stocks and growth stocks over the BM ratio of
market portfolio in Figure 2. In Figure 1, both the average and volatility of the BM ratio of thevalue stock portfolio increased significantly in 1998/99. As a result, the excess BM ratio of value
stocks in Figure 2 has also increased significantly since 1998/99, both in level and in volatility. The
excess BM ratio of growth stocks exhibits a downward trend since late 1990, but the magnitude of
the change is subtle compared with the change in value stocks.

[Figure 1 and Figure 2 about here]
6

Fama and French (2021) estimate a model that allows for structural changes in both the constant term αi andthe coefficients βi of the predictors in equation (1), and test whether there is a statistically significant decline in thevalue premium in the recent sample:
D
Ri,t − RM,t = αi + αiD D + βi,t (BMi,t−1 − BMM,t−1 ) + βi,tD(BMi,t−1 − BMM,t−1 ) + ϵt .

where D is a dummy variable that takes 0 in the first half and 1 in the second half of the sample. Their choice of abreak point is not obvious, but it is plausible that Fama and French picked 1991 because their earliest paper on thethree-factor model (Fama and French 1992, 1993) used the data until approximately 1990 and 91.
7
We perform the structural break test (Bai and Perron [1998]) which allows unknown multiple break points,
instead of a predetermined break point. Though estimation results are not reported here, we find different breakpoints for value stock and growth stock portfolios, and several structural breaks that were not related to any specificeconomic events.

9

Therefore, we estimate the predictive regression, equation (1), dividing the sample at the endof 1998. We further divide the latter subsample at the onset of GFC, August and September of2008. Table 4 reports these subsample estimation results for the TOP 80% universe. For the
early pre-GFC Subsample (1), predictive regression for large value stocks work well: the estimatedcoefficient of excess BM ratio is 2.698 and statistically significant at 1%. On the other hand, theestimated coefficient is negative and statistically insignificant for small value stocks (SV − Rm).
In the second pre-GFC Subsample (2), the coefficients of excess BM ratio in the regression forlarge-cap value stocks is still positive at 1.658, but becomes insignificant. However, the estimatedcoefficient is 3.199 and significant at 1% for small value stocks.
[Table 4 about here]
In the post-GFC sample (3-1) for 2008:9-2019:12, the predicting ability of excess BM ratio forsmall-cap value stocks remains strong, with the estimated coefficient being 3.485 and statisticallysignificant at 1%. Compared with Subsample (2), the predictability of large value stocks somewhat
regained with the estimated coefficient bounced back to 4.368, though it fell by too little to bestatistically significant at the 10% level since standard error also becomes larger.
The estimate of large-cap value stocks’ constant term αi declined significantly for large value
stocks, from −0.429 in Subsample (2) to −3.125 in Subsample (3-1). At the same time, smallcap value stocks’ αi increased from 0.263 in Subsample (2) to 2.150 in Subsample (3-1), and thelatter estimate is statitically significant at 5%. The estimated coefficient of excess BM ratio forsmall-cap growth stocks in Subsample (3-1) also becomes much larger and statitically siginificant
5% compared to Subsample (2) to in Subsample (3-1). Finally, in the post-GFC sample (3-2) for
2008:9-2021:3 including Covid-19 period, the predictive ability completely vanishes for both small
and large value portfolios.

4

Return Decomposition

In this section, we adopt to the framework of Arnott et al. (2021) to investigate the mechanism
behind the time-variation of HML’s return, particularly the decline of its performance after GFC
10

in 2008-2010. Arnott et al. decompose the difference of the performance between value and growth
portfolios into three components, (1) migration, (2) income yield, and (3) changes in value-versusgrowth relative valuation. In the following, we briefly discuss each of these three components.

The first component, “migration”, captures the process in which the price of some stock in thevalue (growth) portfolio appreciates (depreiciates) and moves into the neutral or growth (value)
portfolios. Through the migration process, value stocks achieve relatively higher returns and growthstocks achieve lower returns, so that the difference between their performances contributes towards
the HML factor positively. The second component, “income yield”, captures the difference in thechange in the book value of equity and dividend yield between the value and growth portfolios.
Growth stocks typically pay out more dividends than value stocks, hence the difference in “income
yield” will take on a negative value and contributes negatively to HML’s return. Arnott et al. callthe sum of “migration” and “income yield”, “structural premium”.
Finally, the third item is called the “revaluation premium” component and refers to the return
coming from changes in relative valuations between the growth and value portfolios. As seen in theaccounting identity below, the sum of the “revaluation premium” and the “structural premium”
constitutes HML return.

HML Return = Revaluation Premium + Income Yield + Migration= Revaluation Premium + Structural Premium

Table 6 reports the above decomposition of HML returns for the periods of Subsample (1), (2)and (3-1) in the previous section. Averages of HML factor slightly increased from Subsample (1)before 1999 to Subsample (2) from 1999 to the summer of 2008. Then, it decreased significantly
in Subsample (3-1) after the fall of 2008 to the end of 2019. The time-variation of average HML
return is more prominent among larger stocks. Overall, patterns of time-variation in the averages
11

of HML factor reported in Table 6 are consistent with the variations reported in Table 2 and Table
3.

[Table 5 about here]

Next, we discuss the time-valuations of individual components of HML factor returns. We
start our discussion from “structural premium”. The average values of “structural premium”
among both small-cap stocks and large-cap stocks declines from Subsample (1) to Subsample (2).
In Subsample (3-1) after GFC in the fall of 2008, they slightly decline but remain approximately
equal in Subsample (2). The differences in the values of “structural premium” before and after GFC
are equal or slightly lower compared with the corresponding difference in the US market reported
in Table 3 of Arnott et al. (2021). It should also be noted that, in the US market, the spread
between value and growth stock returns has been larger among small-cap stock than among largecap stocks. On the other hand, the spreads are mostly equal regardless the size of stocks or evenslightly higher among large-caps in the Japanese market. In addition, among the components of the“structural premium”, the decline of “income yield” and the increase of “migration” components
are pronounced for large-cap stocks from Subsample (1) to Subsample (2).
Finally, we discuss the time-variation of “revaluation premium”. The average values of “revaluation premium” are actually negative in Subsample (1) before 1999. They go up about 5 percentage
point in Subsample (2) in the 2000s before GFC, then drop significantly about 9.5 percentage point
after GFC. Given that the time-variations of “structural premium” are relatively mild, these results
suggest that the biggest contribution to the significant decline of HML returns after GFC is thedecline of “revaluation premium”.
To illustrate this point, Figure 4 shows the relative valuation of value and growth portfolios,
plotting the ratio of BM for the growth portfolio to BM for the value portfolio. While there aresome significant declines of relative valuation of value stocks in the past, in the first half of 1987
and around the turn of the century, previously, value stocks tended to bounce back in relatively
short periods of time. On the other hand, the decline in the 2010s toward the end of the sample
has been mild, but prolonged for nearly ten years.

12[Figure 4 about here]

The fact that the “revaluation premium” explains the majority of the decline of HML returns
after GFC is consistent with the finding by Arnott et al. (2021) for the US Market. Drawing on
the works of Fama and French (2002) and Arnott and Bernstein (2002), Arnott et al. (2021) claimthat “the returns induced by the changes in the valuations should be purged from the estimates of
the risk premium because no a priori reason exists to explain why any trend in valuation should
persist.” However, the fact that the relative level of value stock prices has declined recently bothin Japanese and US markets suggests the importance of explaining such a phenomenon in future
research.

5

Conclusions

In this paper, we examined the decline of value premium in the Japanese stock market. We
compare the performances of HML return and the returns of the size-BM ratio sorted portfolios
in the period from the end of the 1990s until around the time of Global Financial Crisis with theperformances in the period after. We find that performance of value stocks declined significantly
and the performance of growth stocks increased slightly.
We investigate the mechanism behind the recent decline of HML returns using predictive regression and found that while the constant term declined after GFC in late 2000s, the estimatedcoefficients of the predicting variable, excess BM ratio, remain at the same level or even slightly
higher. The decline of constant term is consistent with the declining relative valuation of value
stocks compared with growth stocks, dubbed as “revaluation premium” in the HML return decomposition by Arnott et al. (2021). Similarly, the fact that predicting ability of excess BM ratio
stays at the similar level before and after GFC is consistent with the little variation of “structural
premium” obtained by the return decomposition over time.
The empirical results of this paper imply that recent underperformance value investing in theJapanese market is caused by similar mechanism as in the US market. However, why the relative13

valuation of value stocks has declined after GFC in both countries for a decaderemains unsolved
and is, therefore a potential subject of future research.

14

References
Arnott, Robert D., Campbell R. Harvey, Vitali Kalesnik and Juhani T. Linnainmaa, (2021) “Reports of Value’s Death May Be Greatly Exaggerated,” Financial Analysts Journal, 77:1, 44-67,
DOI: 10.1080/0015198X.2020.1842704.
Bai, Jushan, and Pierre Perron, (1998) “Estimating and testing linear models with multiple structural changes,” Econometrica, 66(1), 47-78.
Chan, Louis K. C., Yasushi Hamao, and Josef Lakonishok, (1991) “Fundamentals and stock returns
in Japan,” Journal of Finance 46:5, 1739-1764.
Cohen, Randolph B., Chritopher Polk, and Tuomo Vuolteenaho (2003) “The value spread,” Journalof Finance 78:2, 609-642.
Fama, Eugene F., and Kenneth R. French, (1992), “The cross-section of expected stock returns,”
Journal of Finance 47:2, 427-465.
Fama, Eugene F., and Kenneth R. French, (1993) “Common risk factors in the returns on stocks
and bonds,” Journal of Financial Economics 33:1, 3-56.
Fama, Eugene F., and Kenneth R. French (1995) “Size and book-to-market factors in earnings andreturns,” Journal of Finance 50:1, 131-156.
Fama, Eugene F., and Kenneth R. French (2017) “International tests of a five-factor asset-pricing
model,” Journal of Financial Economics 123:3, 441-463.
Fama, Eugene F., and Kenneth R. French (2021) “The Value Premium,” Review of Asset Pricing
Studies 11:1, 105 ‒ 121.
Ishikawa, Yasushi (2021) “Macro and micro perspectives on vanished value premium (in Japanese,
Value teimei-ni taisuru macro to micro no shiten),” presentation at the special panel seesion in
NFA annual meeting June 2021.
Israel, Ronen, Kristoffer Laursen, and Scott Richardson (2021) “Is (Systematic) Value Investing Dead?,” Journal of Portfolio Management Quantitative Special Issue, 47:2, 38-62; DOI:
https://doi.org/10.3905/jpm.2020.1.194
15

Iwaisako, Tokuo (2021) “Value Premium in Japanese Market:

Statistical (Re)apparisal (inJapanese, Nihon-no value premium: Toukeiteki Saikensho),” presentation at the special panel
seesion in NFA annual meeting June 2021.
Kamesaka, Akiko , John R Nofsinger, and Hidetaka Kawakita (2003) “Investment patterns andperformance of investor groups in Japan,” Pacific-Basin Finance Journal 11:1, 1-22.
Kang, Jun-Koo and René M. Stulz (1997)“Why is there a home bias? An analysis of foreign

portfolio equity ownership in Japan,” Journal of Financial Economics 46:1, 3-28.
Lee, Justina (2020) “Is Value Dead? Debate Rages Among Quant Greats From Fama to AQR,”
Bloomberg, June 3, 2020, https://www.bloomberg.com/news/articles/2020-06-03/quant-fight2020-inside-wall-street-s-big-argument-on-value
Lev, Baruch Itamar and Srivastava, Anup (2022) “Explaining the Recent Failure of Value Investing,” Critical Finance Review 11:2, 333-360. http://dx.doi.org/10.1561/104.00000115
Linnainmaa, Juhani T., and Michael R. Roberts (2018) “The history of the cross-section of stock
returns,” Review of Financial Studies 31:7, 2606-2649.
Takehara, Hitoshi (2021) “HML may be dead, but value is not (in Japanese, HML shisu-tomo,
value wa shinazu),” presentation at the special panel seesion in NFA annual meeting June 2021.

16

Figure 1: Book to Market ratio for Market Average, Market Value, and Market Growth in Japan:
September 1977 - March 2021

Book-to-Market ratios for Market Average: BMM (green line), Market Value: BMM V (blue line),and Market Growth: BMM G (black line) are poltted.

17

Figure 2: Excess Book to Market ratio for Market Value and Market Growth

Excess book-to-market ratios (BMi − BMM ) for Market Value (i = M V ) and for Market Growth(i = M G) are plotted.

18

Figure 3: Relative Valuations of Growth and Value

The ratio of the BM for the growth portfolio to the BM for the value portfolio (BMM G /BMM V )is plotted.

19

Table 1: Averages of Fama-French factors and Returns of Size-BM sorted portfolios

We report averages of Fama-French factors and excess returns of six Size-BM sorted portfolios
relative to market portfolio for the full sample, September 1977-March 2021. We repeat same
calcurations for three different size-based universes of indvidual stocks, namely Entire Market, Top
80%, and Nikkei 225.
Panel A: Entire Market
AverageStandard Deviation

Rm-Rf
0.31
5.08

SMB
0.04
3.31

HML
0.79
3.27

AverageStandard Deviation

SV-Rm
0.62
3.96

MV-Rm
0.63
3.41

BV-Rm
0.63
3.71

AverageStandard Deviation

Rm-Rf
0.30
5.11

SMB
−0.07
2.98

HML
0.76
3.44

AverageStandard Deviation

SV-Rm
0.49
3.69

MV-Rm
0.55
3.21

BV-Rm
0.60
3.49

AverageStandard Deviation

Rm-Rf
0.30
5.47

SMB
0.15
3.58

HML
0.85
3.89

AverageStandard Deviation

SV-Rm
0.68
4.47

MV-Rm
0.58
3.61

BV-Rm
0.48
3.81

SG-Rm
−0.11
3.83

MG-Rm
−0.17
1.82

BG-Rm
−0.22
1.49

SG-Rm
−0.22
3.44

MG-Rm
−0.22
1.71

BG-Rm
−0.22
1.55

SG-Rm
−0.17
4.10

MG-Rm
−0.27
2.14

BG-Rm
−0.37
2.03

Panel B: Top 80%

Panel C: Nikkei 225

20

Table 2: Averages of Fama-French factors and Portfolios in Subsamples

Panel A: Fama-Frnch factors
Panel A-I: Entire Market
(1)
(2)
(3-1)
(3-2)
(4)

1977:09 - 1998:12
1999:01 - 2008:08
2008:09 - 2019:12
2008:09 - 2021:03
2020:01 - 2021:03

Rm-Rf
0.16
0.24
0.54
0.57
1.25

SMB
-0.04
-0.13
0.37
0.47
-0.29

HML
0.93
1.14
0.41
0.37
-0.73

1977:09 - 1998:12
1999:01 - 2008:08
2008:09 - 2019:12
2008:09 - 2021:03
2020:01 - 2021:03

Rm-Rt
0.15
0.26
0.52
0.60
1.26

SMB
-0.23
-0.10
0.28
0.20
-0.48

HML
0.89
1.09
0.42
0.30
-0.82

1977:09 - 1998:12
1999:01 - 2008:08
2008:01 - 2019:12
2008:01 - 2021:03
2020:01 - 2021:03

Rm-Rt
0.21
0.17
0.46
0.55
1.32

SMB
0.05
0.24
0.25
0.24
0.18

HML
0.95
1.38
0.42
0.27
-1.06

Panel A-II: Top 80%
(1)
(2)
(3-1)
(3-2)
(4)

Panel A-III: Nikkei 225
(1)
(2)
(3-1)
(3-2)
(4)

21

Table 2: Continued

Panel B: Excess Returns of Size-BM sorted portfolios relative to Market Portfolio
Panel B-I: Entire Market
(1)
(2)
(3-1)
(3-2)
(4)

1977:09 - 1998:12
1999:01 - 2008:08
2008:09 - 2019:12
2008:09 - 2021:03
2020:01 - 2021:03

SV-Rm
0.60
0.75
0.70
0.57
−0.63

MV-Rm
0.61
0.87
0.58
0.47
−0.53

BV-Rm
0.62
0.99
0.45
0.37
−0.42

SG-Rm
−0.34
−0.29
0.44
0.38
−0.09

MG-Rm
−0.32
−0.27
0.17
0.17
0.21

BG-Rm
−0.29
−0.31
−0.09
−0.03
0.50

SV-Rm
0.41
0.70
0.60
0.45
−0.89

MV-Rm
0.54
0.79
0.48
0.37
−0.59

BV-Rm
0.66
0.88
0.36
0.29
−0.30

SG-Rm
−0.46
−0.23
0.22
0.19
−0.13

MG-Rm
−0.35
−0.30
0.06
0.07
0.23

BG-Rm
−0.25
−0.37
−0.11
−0.04
0.59

SV-Rm
0.56
1.22
0.57
0.47
−0.37

MV-Rm
0.54
1.11
0.23
0.25
−0.44

BV-Rm
0.52
1.00
0.09
0.03
−0.51

SG-Rm
−0.35
−0.05
−0.03
0.05
−0.37

MG-Rm
−0.42
−0.27
−0.09
−0.02
0.62

BG-Rm
−0.49
−0.50
−0.15
−0.09
0.49

Panel B-II: Top 80%
(1)
(2)
(3-1)
(3-2)
(4)

1977:09 - 1998:12
1999:01 - 2008:08
2008:09 - 2019:12
2008:09 - 2021:03
2020:01 - 2021:03

Panel B-III: Nikkei 225
(1)
(2)
(3-1)
(3-2)
(4)

1977:09 - 1998:12
1999:01 - 2007:12
2008:01 - 2019:12
2008:01 - 2021:03
2020:01 - 2021:03

22

Table 3: Preditive Regression for the Excess Return over the Market Portfolio for Top 80% Stocks:
Full Sample

This table reports the following forecasting regression for the full sample:
Ri,t − RM,t = αi + βi,t (BMi,t−1 − BMM,t−1 ) + ϵt
Subscript i denotes six portfolios sorted by firm size and BM ratio, {SV, M V, SG, BG, M V, M G}.
The estimation period is September 1977 to March 2021 (523 obs.). Standard errors by Newey-West(with 6 lags) are reported in parentheses. (∗∗∗ ) denotes that estimated coefficient is significant at
1% level，(∗∗ ) denotes 5% level, and (∗ ) denotes 10% level.
Dependent variable
SV − Rm

αi
0.140[0.447]βi,t0.505[0.597]R2
0.003

adj.R20.001

BV − Rm

0.312[0.298]0.515[0.654]

0.002

0.000

M V − Rm

0.185
[0.320]0.604[0.591]

0.004

0.002

SG − Rm−0.376[0.356]−0.662[1.194]0.000−0.001

BG − Rm−0.483∗∗[0.210]−1.148[0.876]

0.007

0.005

M G − Rm

−0.510∗∗∗[0.177]−1.255∗[0.742]

0.007

0.005

23

Table 4: Preditive Regression for the Excess Return over the Market Portfolio for Top 80% Stocks:
Subsamples

Subsample (1): Early Subsample, September 1977 - December 1998 (256 obs.)
Dependent variable
SV − Rm

αi
0.651[0.630]βi,t−0.670[1.318]R2
0.001

adj.R2−0.003

BV − Rm−0.143[0.386]2.698[0.850]∗∗∗

0.014

0.011

M V − Rm

0.236
[0.503]0.959[1.078]0.002−0.002SG − Rm

0.754[0.886]7.648[5.348]

0.010

0.006

BG − Rm−0.790∗∗[0.371]−3.498∗[2.120]

0.008

0.004

M G − Rm

−0.265[0.273]0.575[1.791]0.000−0.004

Subsample (2): Before GFC, Januray 1999 - August 2008 (116 obs.)
Dependent variable
SV − Rm

αi−2.286∗∗∗[0.702]βi,t3.199∗∗∗[0.766]R2
0.065

adj.R20.057

BV − Rm−0.429[0.836]1.658[1.271]

0.012

0.004

M V − Rm

−1.349∗∗[0.681]2.533∗∗∗[1.066]

0.043

0.034

SG − Rm

0.263[0.777]2.323[3.808]0.002−0.007

BG − Rm

0.601[0.835]4.507[3.501]

0.023

0.015

M G − Rm

−0.158[0.643]0.665[2.885]0.000−0.00924

Table 4: Continued

Subsample (3-1): After GFC excluding Covid-19: September 2008 - December 2019 (136 obs.)
Dependent variable
SV − Rm

αi−2.985∗∗∗[1.068]βi,t3.485∗∗∗[1.133]

R2
0.059

adj.R20.052

BV − Rm−3.125[1.976]4.368[2.758]

0.050

0.043

M V − Rm

−2.801∗[1.410]3.904∗[2.037]

0.067

0.060

SG − Rm

2.150∗∗[0.850]5.227∗∗[2.295]

0.020

0.012

BG − Rm

0.081[0.452]0.528[1.452]0.001−0.007

M G − Rm

1.059∗[0.591]2.785∗[1.709]

0.014

0.006

Subsample (3-2): After GFC including Covid-19, January 2008 - March 2021 (151 obs.)
Dependent variable
SV − Rm

αi−0.639[1.520]βi,t1.022[1.598]R2
0.007

adj.R20.000

BV − Rm−0.738[1.603]1.219[2.169]

0.007

0.000

M V − Rm

−0.313[1.434]0.774[1.855]0.005−0.002SG − Rm

1.687[0.961]

3.947[2.608]

0.011

0.005

BG − Rm−0.634[0.633]−1.588[1.973]

0.008

0.001

M G − Rm

0.341
[0.804]0.715[2.360]0.001−0.00625

Table 5: Attribution of HML Factor Returns

Total
Return (%)

Revaluation
Premium (pps)

Structural
Premium (pps)

IncomeYield (pps)

Migration
(pps)

10.4
11.1
10.8

−2.4
−3.0
−2.7

12.9
14.0
13.5

−15.1
−8.8
−11.9

28.0
22.8
25.4

(2) 1999:1-2008:8
Small
Value-Growth
Big
Value-Growth
Average
HML

11.8
14.4
13.1

2.9
3.5
3.2

8.9
10.9
9.9

−11.5
−17.0
−14.2

20.4
27.9
24.1

(3-1) 2008:9-2019:12
Small
Value-Growth
Big
Value-Growth
Average
HML

4.4
5.3
4.9

−6.2
−6.1
−6.1

10.6
11.4
11.0

−10.0
−10.4
−10.2

20.5
21.8
21.2

Size
Valuation(1) 1977:9-1998:12
Small
Value-Growth
Big
Value-Growth
Average
HML

26