2.
Journal of Money, Investment and Banking - Issue 8 (2009) 6
different valuation ratios seems to vary both across stock markets and sample period examined; e.g.,
Chan et al. (1993) document that during the 1971-1988 period stock returns in Japan were positively
related to such valuation ratios as book-to-price (B/P) and cash flow-to-price (CF/P) ratios while the
results of Suzuki (1998) show the superiority of sales-to-price (S/P) criterion in the same stock market
during the 1983-1996 period. The parallel results are also reported by Barbee et al. (1996) who find
that S/P ratios explain U.S. stock returns better than corresponding B/P ratios during the 1979-91
period, and by Bird and Casavecchia (2007a, 2007b) who document the superiority of S/P ratios in the
European markets during the 1989-2004 period.
Fama and French (1998) compare the value premiums obtained from using four different
criteria for portfolio formation (i.e., B/P, CF/P, earnings-to-price (E/P) and dividend yield (D/P)) in 13
major stock markets. According to their results, the classification criterion leading to the greatest value
premium for the 1975-1995 period vary across countries; in 6 out of 13 regional stock markets B/P
criterion resulted in the greatest value premium (in USA, UK, Belgium, Switzerland, Singapore, and in
Japan where the B/P criterion resulted in the widest and the most significant value premium), while
CF/P criterion was the best in 4 stock markets (i.e., in Germany, Italy, Hong Kong, and in Australia
where the premium was both the greatest and the most significant). In Netherlands and Sweden the
greatest value premium was achieved by following E/P criterion, while in France D/P criterion
generated the largest premium. According to Kyriazis and Diacogiannis (2007) the dividend yield
provided the best and only basis for value strategy also in the Greece stock market during the 1995-
2002 period. Dhatt et al. (2004) show that the most efficient individual valuation multiples based on
U.S. data were CF/P and S/P the 1980-1999 period. The authors show further that by using composite
value measures the set of efficient portfolios can be expanded, enabling investors to achieve a wider
range of risk-return trade-offs. Instead, Bird and Casavecchia (2007b) do not find evidence of added
value of using composite value measures in the European stock markets.
This paper examines the performance of value strategies in the Finnish stock market during the
1993-2008 period. The Finnish stock market is an interesting research subject for the value strategies
since it suffers from intermittent “periphery syndrome” caused by the herding behavior of international
institutional investors who cash their equity positions first from the furthest stock markets during the
turbulent times. Coupled with relatively low liquidity of the Finnish stock market the withdrawal
process results in drops of stock prices that are steeper than simultaneous drops in larger and more
developed stock markets. During the current global financial crisis the phenomenon has again repeated
itself. On the other hand, due to relatively thin trading during bullish sentiment stock prices tend to rise
more during bullish sentiment in Finland than they do in the major stock markets. As a consequence,
the average stock market volatility is also higher in Finland. Therefore, it is presumable that also
pricing errors causing the value premium are larger and the opportunities to earn abnormal profits by
active investment strategies are better.
We contribute to the existing literature on value strategies in several ways: First, we use
EBITDA/EV multiple (i.e., Earnings Before Interest, Taxes, Depreciations and Amortizations to
Enterprise Value) as a basis of value strategies while comparable studies have for the most part
concentrated on E/P and CF/P ratios as representatives of earnings multiples. Since Enterprise Value
takes account also the debt of a company, the use of EBITDA/EV multiple might cope with the
problem of spurious undervaluation stemming from the characteristics of the price-based earnings
multiples. As stated by Bird and Casavecchia (2007a), a relatively low valuation can be a reflection of
parlous financial health about which the price-based valuation multiples tell nothing. Our results show
that additional dimensions included in EBITDA/EV as a measure of relative value can enhance the
performance of portfolios that are formed on the basis of composite value measures. Second, we
examine whether the potential excess returns of value portfolios are explained by differences in shapes
of return distributions being compared by applying a recently innovated method for this purpose. To
our knowledge, this is the first time when this approach developed by Pätäri (2009) is employed in the
3.
7 Journal of Money, Investment and Banking - Issue 8 (2009)
studies on performance of value strategies. 2 Third, we form 3-quantile portfolios based on composite
value measures that combine the different dimensions of relative value into one composite measure. To
our knowledge, this is the second time when the relative efficiency of composite value measures that
are constructed on the basis of financial statement information are tested outside the U.S. stock markets
.3 Fourth, in order to avoid erroneous conclusions stemming from the possible model misspecification
bias we analyze both the total risk-based performance of value strategies and corresponding
performance based on market risk. For the same reason, we test whether our results are robust to size
factor. Fifth, motivated by the recent results of Kyriazis and Diacogiannis (2007) we test also the
efficiency of D/P as a portfolio formation criterion. Sixth, unlike done in most previous studies on
value investing, we do not exclude the stocks of the companies with negative earnings since the
majority of the Finnish companies in this category operate in ICT industry and are classified as
glamour companies on the basis of all other valuation ratios. In addition, earnings multiples of loss-
making companies can be considered extremely high, and therefore, they could be classified as non-
value stocks. Given the research design, it is interesting to see whether the value premium exists when
the unprofitable companies are not excluded from the sample. 4 Seventh, we analyze the corresponding
performance difference during bull and bear market conditions to find out whether its degree is
dependent on stock market sentiment. The 15-year sample period that extends over several economic
cycles enables this kind of analysis.
The structure of the article is as follows: Section 2 outlines the data and the research methods
employed. We report our findings in Section 3 by presenting first the results obtained from using
individual valuation multiples as portfolio formation criterion (Subsections 3.1), and second, the results
based on composite value measures (Subsections 3.2–3.4), respectively. Third, the performance
differences between value and growth portfolios are analyzed separately during bull and bear market
periods in Subsection 3.6. Finally, the robustness of the results against size effect is discussed in
Subsection 3.7. Section 4 concludes with suggestions for future research.
2. Data and Methodology
The portfolios are composed of Finnish stocks quoted in the main list of the Helsinki Stock Exchange
(HEX; later OMX Helsinki) during the 1993–2008 period. To avoid survivorship bias, the sample
includes also the stocks of the companies that were delisted during the observation period. Adjustments
for dividends, splits and capitalization issues are done appropriately. If an issuer has had two or more
stock series listed only that with higher liquidity is included in the sample. The stocks of the companies
that did not have fiscal year ending in December are excluded from the sample. Stock market data as
well as financial statement data are from Datastream and the latter is supplemented by collecting data
from financial statements of the companies not included in Datastream. The final sample size ranges
from 51 companies in the year 1993 to 110 in the year 2007, and the number of companies increases
gradually during the sample period.
The stocks in the sample are first ranked based on valuation multiples or composite value
measures that are calculated in every rebalancing date that is the first trading day of May, at 1-year
frequency. The stocks are then divided into three portfolios based on the ranks of the valuation
multiples and those of composite value measures. Both individual valuation ratios and composite value
measures are calculated using the latest information that is available at the time when rebalancing is
2
The topic is indirectly discussed by Rousseau and van Rensburg (2004) who report positive skewness in return distributions of value portfolios that
becomes more pronounced over longer holding periods.
3
To our knowledge, Bird and Casavecchia (2007b) were the first to follow this approach outside the U.S. markets but their examination is based on only
three individual valuation ratios (i.e., on B/P, S/P and E/P).
4
The impact of the inclusion of unprofitable companies on the value premium is bidirectional; if those companies continue making losses in the near
future, the inclusion of such companies will most probably increase the value premium. On the other hand, if they succeed in turning their negative
earnings to positive, the inclusion of such companies will most probably decrease the value premium since the highest returns are often gained by stocks
of such turnaround companies. However, the inclusion of unprofitable companies does not affect the absolute performance of value portfolios. Also
their performance relative to the market portfolio remains unchanged
4.
Journal of Money, Investment and Banking - Issue 8 (2009) 8
made. Stock prices are the closing quotes of rebalancing days, and variables from financial statements
(e.g., enterprise value and profitability measures) are picked from the latest financial statements that
have been published prior to the moment of rebalancing.
The performance evaluation of strategies is based on time series of monthly returns of each
portfolio. Portfolios are equally weighted at the outset and then monthly returns are calculated by
taking account of changes in portfolio weights during the 1-year holding period. The intermediate cash
flows obtained from delisted stocks during the holding periods are reinvested in the remaining stocks
of the same portfolio according to prevailing portfolio weights in the beginning of the next month
following the date of delisting. Taking account of implications of rebalancing, continuous stacked
time-series of monthly returns for portfolios can be generated throughout the sample period for each
stock selection strategy. The performance of the portfolios is evaluated based on their average returns
and several risk-adjusted performance metrics introduced later. The performance of each portfolio is
evaluated against the market portfolio and the performance of the value portfolio (P1) is compared to
that of the glamour portfolio (P3).
To extend the interpretation of relative value to the full range of valuation multiples including
negative ones we use inverses of the traditional valuation multiples as ranking criterion. The valuation
multiples used in forming 3-quantile portfolios are E/P (earnings yield), EBITDA/EV, CF/P (Cash
flow is calculated as the sum of fully diluted EPS excluding extraordinary items and depreciations and
amortizations per share), D/P (dividend yield), B/P and S/P.
We also test the performance of 3-quantile portfolios that are based on eight different
composite value measures. All the composite value measures are based on combination of earnings
multiple and one or several other valuation ratio(s). We use mainly EBITDA/EV as earnings multiple
instead of the most-typically used E/P since EBITDA/EV takes the leverage differences between the
firms into account better than E/P does, and provides thus one additional dimension to relative
valuation. All of these composite value measures are obtained by standardizing first all the valuation
multiples employed by the median of each multiple and calculating then the simple average of these
ratios for each stock.
The first of the composite value measure is named as 2A and it is obtained with combination of
D/P and EBITDA/EV. The second composite measure 2B is based on combination of B/P and
EBITDA/EV and the third one 2C on combination of S/P and EBITDA/EV, respectively. The inclusion
of volume multiple can be motivated by the results of previous studies that have shown the added value
of using S/P as a valuation criterion (e.g., Barbee et al., 1996; Dhatt et al., 2004). We include also the
composite measures based on three valuation ratios in our research; the first one of these, named as 3A,
is based on combination of B/P, D/P and E/P. The fifth composite measure 3B reminds closely 3A with
the exception that E/P is replaced with EBITDA/EV. Correspondingly, 3C composite measure is based
on combination of S/P, B/P and EBITDA/EV. Parallel to the recent study of Dhatt et al. (2004) we test
also performance of portfolios that are formed on the basis of 4-composite measures of which 4A is
based on combination of CF/P, B/P, D/P and EBITDA/EV, and 4B on S/P, B/P, D/P and EBITDA/EV,
respectively.
2.1. Test Procedures
The performance of 3-quantile portfolios is evaluated based on average return, the Sharpe ratio
(Sharpe, 1994), the adjusted Sharpe ratio, the Jensen alpha (Jensen 1969), and 2-factor alpha. To avoid
the validity problems stemming from the negative excess returns in the context of the Sharpe ratio
comparisons we use the modified versions of Sharpe ratios throughout the study as follows 5:
Ri − R f
Sharpe ratio = ( ER / ER ) (1)
σi
where
5
The modification procedure was first introduced by Israelsen (2003).
5.
9 Journal of Money, Investment and Banking - Issue 8 (2009)
Ri= the average monthly return of a portfolio i
Rf = the average monthly risk free rate of the return6
σi = standard deviation of the monthly excess returns of a portfolio i
ER = average excess return of portfolio i.
We use the Sharpe ratio as a major representative of total risk-based performance metrics.
However, the Sharpe ratio is often criticized of oversimplifying the concept of risk since all the
deviations from the mean, including positive ones, have direct impact on the value of standard
deviation. If the return distributions being analyzed are right-skewed the use of standard deviation as a
risk surrogate penalizes from the upside potential that is desirable rather than undesirable from the
viewpoint of the investor. Therefore, the use of standard deviation as a measure of investment risk has
been questioned by many scholars and many alternative risk measures aimed to match better with the
investor’s true perception of risk has been suggested in the financial literature (see e.g., Pätäri, 2008 for
a comprehensive summary of downside dispersion measures based on total risk). For that purpose we
employ the adjusted Sharpe ratio whose risk metrics captures the skewness and kurtosis of return
distributions being analyzed. 7 Analogously to the approach used by Favre and Galeano (2002) in
determining modified Value-at-Risk, the adjusted Z value (i.e., ZCF) that corresponds to the Z value of
normal distribution is calculated first. The so-called Cornish-Fisher expansion is applied to calculate
ZCF as follows:
1
(
Z CF = Z C + Z C − 1 S +
6
2 1
24
)
Z C − 3Z C K −
3
( 1
36
)
2 Z C − 5Z C S 2
3
( )
(2)
where Zc is the critical value for the probability based on standard normal distribution, and S denotes
skewness and K kurtosis of the return distribution. Respectively, formulas for skewness and kurtosis
formula are given as follows:
1 T ⎛ rt − r ⎞
3
S = ∑⎜ ⎟
T t =1 ⎝ σ ⎠ (3)
4
1 T ⎛r −r⎞
K = ∑⎜ t ⎟ −3
T t =1 ⎜ σ ⎟
⎝ ⎠ (4)
where, T is number of outcomes, r is the return of a portfolio and σ is the standard deviation of a
portfolio. Next, we calculate the skewness- and kurtosis-adjusted deviation (SKAD, henceforth) by
multiplying the standard deviation by the ratio ZCF/ Zc. We use the 95% probability level in this paper
in determining the ratio ZCF/ Zc. Finally, we substitute SKAD for standard deviation and modify the
resulting ratio to capture the validity problem stemming from negative excess returns analogously to
the refinement procedure of Israelsen (2003; 2005) as follows:
Ri − R f
adjusted Sharpe ratio = ( ER / ER )
(5)
SKADi
where SKADi = skewness- and kurtosis-adjusted deviation of the monthly excess returns of a portfolio i
The inclusion of higher moments of return distributions in performance evaluation of value
stock portfolios can also be motivated by the recent results of Rousseau and van Rensburg (2004) who
report significant distributional asymmetries and differences in returns of value and growth portfolios.
In order to get more in-depth view of relative performance of 3-quantile portfolios we employ
also performance metrics that are based on asset pricing models. To find out whether the potential
value premium is explained by size effect, we compare single-factor alphas based on the standard
Capital Asset Pricing Model (CAPM) to 2-factor alphas based on the pricing model that includes also
6
A proxy for risk-free rate is obtained from the Research Institute of the Finnish Economy (ETLA) database from May 1991 till the end of 1998 (1-month
Helibor) and from Datastream database from January 1999 till the end of the sample period (1-month Euribor)
7
The adjusted Sharpe ratio is developed by Pätäri (2009) and employed in the hedge fund study of Pätäri and Tolvanen (2009), for example
6.
Journal of Money, Investment and Banking - Issue 8 (2009) 10
the size factor (SMB) as another explanatory variable. 8 The standard CAPM alpha known also as the
Jensen alpha (Jensen, 1969) indicates the abnormal return of the portfolio over that predicted by
CAPM as follows:
α i = Ri − Rf − βi ( Rm − Rf ) (6)
where
αi = the Jensen alpha
βi = the beta coefficient of a portfolio i
Rm = the stock market return
We employ also 2-factor model to isolate the potential size effect from alphas. The SMB factor
is constructed by classifying the stocks quoted in the main list of OMX Helsinki Stock Exchange into
three size portfolios based on the market cap of companies included. The monthly return time-series
for SMB factor are generated by subtracting value-weighted monthly return of the large-cap portfolio
from the comparable return of the small-cap portfolio. Respectively, we calculate 2-factor “size-
adjusted” alphas as follows:
α i = Ri − R f − β i1 (Rm − R f ) − β i 2 SMB
(7)
where
αi = the two-factor alpha (the abnormal return over to what might be expected based on the
two-factor model employed)
SMB = the return of size factor (i.e., the return difference between small- and large-cap
portfolios)
βi1, and βi2 are factor sensitivities to stock market and SMB factors, respectively.
Knowing that there are much sophisticated pricing models we restrict our regression tests to
these two simple models since our main interest is to examine the impact of the size effect on portfolio
alphas.
Significance Tests and Statistical Adjustments
The statistical significances of differences between comparable pairs of the Sharpe ratios are given by
p-values of the Ledoit-Wolf test 9 that is based on the circular block bootstrap method. We also test the
statistical significance of differences between portfolio alphas by the appropriate alpha spread as
follows:
αi − α j
t= (8)
SEαi + SEαj
2 2
where
α ∗ is alpha of portfolio *,
SEα ∗ is standard error of portfolio *,
The degrees of freedom for the test statistic are given as:
v=
(SE 2
αi + SEαj
2
)
2
(9)
SEαi SEαj
4 4
+
vi vj
where νi and νj are the degrees of freedom determined on the basis of number of time-series returns in
samples i and j (ν = n - 1)
8
The authors would like to thank Professor Mika Vaihekoski for providing us the Finnish SMB factor returns from May 1993 till December 2004. The
remaining monthly returns required for the tests from January 2005 till the end of the sample period (i.e., April 2008) were calculated by the authors,
respectively
9
We do not describe the Ledoit-Wolf test here in more detail because of the complexity of the test procedure but recommend the interested reader to see
the original article (Ledoit and Wolf, 2008; corresponding programming code is freely available at
http://www.iew.uzh.ch/chairs/wolf/team/wolf/publications.html#7).
7.
11 Journal of Money, Investment and Banking - Issue 8 (2009)
We use Newey-West (1987) standard errors in statistical tests throughout the study to avoid
problems related to autocorrelation and heteroscedasticity. In addition, we performed the normality test
of Jarque and Bera (1980) for regression residuals but the assumption of their normality is not
generally violated except for few random cases. Moreover, we test the existence of multicollinearity in
regressions of two explanatory variables. In spite of significant negative correlation between market
and SMB factors, the variance inflation factor (VIF) that is typically used in detecting the degree of
multicollinearity did not indicate that it would have been severe in either full-length sample period or
in any of sub-periods examined.
2.2. Descriptive Statistics
The descriptive statistics of both individual valuation multiples and composite value measures are
presented in Table 1 separately for value and glamour portfolios over the 15-year sample period. Since
the extreme valuation ratios are not excluded the most descriptive characteristic presented in Table 1 is
the median which reveals the scale differences between the individual valuation ratios best.
3. Results
3.1 Performance of Portfolios Based on Individual Valuation Ratios
The comparison of individual valuation multiples reveals the dividend yield to be the best criterion for
selection of value portfolio (Table 2). Over the 15-year period D/P value portfolio generates 10.03 %
annual alpha that is highly significant (at the 1 % level). It outperforms the market portfolio also based
on the Sharpe ratio difference at the same significance level. Moreover, it has the highest average
return, and the lowest volatility, as well as the lowest beta, among all 3-quantile portfolios based on
individual valuation ratios. In addition, the adjusted Sharpe ratio is the highest for D/P value portfolio
though it has somewhat greater skewness- and kurtosis-adjusted dispersion (measured by SKAD) than
some other 3-quantile portfolios.
The relative efficiency of earnings multiples as selection criterion is not unambiguous.
Regarding value portfolios, EBITDA/EV criterion results in the greatest alpha, while the Sharpe ratio
difference against the market as well as t-statistic of alpha is the most significant based on E/P
criterion. Both alpha and the Sharpe ratio indicate significant outperformance over the market for E/P
value portfolio. Value portfolio alphas are also significant for two other earnings multiples, but their
Sharpe ratios do not significantly exceed that of the market portfolio. Instead, based on the adjusted
Sharpe ratio EBITDA/EV value portfolio is the only value portfolio that significantly outperforms the
market portfolio. Interestingly, also E/P and CF/P middle portfolios outperform the market portfolio
significantly while all the glamour portfolios based on individual earnings multiples underperform
against the market, though not significantly in statistical sense. In addition, CF/P middle portfolio
dominates the comparable value portfolio in the mean-variance framework, and all performance
metrics of E/P middle portfolio are also greater than those of E/P value portfolio, though the
differences are not statistically significant.
8.
Journal of Money, Investment and Banking - Issue 8 (2009) 12
Table 1: Descriptive Statistics for Portfolio Selection Criteria
Panel A Panel B
minimum mean median maximum minimum mean median maximum
EBITDA/EV 2A (D/P EBITDA/EV)
ALL -0,4762 0,1452 0,1320 27,027 ALL -118,333 22,383 20,529 327,899
P1 0,1099 0,2557 0,2128 27,027 P1 24,055 39,379 32,589 327,899
P3 -0,4762 0,0474 0,0692 0,1549 P3 -118,333 0,7215 0,9417 18,280
E/P 2B (B/P EBITDA/EV)
ALL -32,700 0,0187 0,0574 10,874 ALL -95,698 23,382 21,054 282,230
P1 0,0551 0,1257 0,1029 10,874 P1 22,724 38,755 31,228 282,230
P3 -32,700 -0,1220 0,0085 0,0656 P3 -95,698 10,537 12,826 18,782
CF/P 2C (S/P EBITDA/EV)
ALL -31,429 0,1239 0,1189 102,384 ALL -96,306 26,726 21,047 408,870
P1 0,1033 0,2984 0,2335 102,384 P1 24,062 50,164 40,173 408,870
P3 -31,429 -0,0430 0,0545 0,1426 P3 -96,306 0,9121 11,382 18,339
D/P 3A (B/P D/P E/P)
ALL 0,0000 0,0391 0,0338 12,852 ALL -48,021,692 -74,318 31,092 1,844,715
P1 0,0200 0,0750 0,0625 12,852 P1 35,317 82,169 49,330 1,844,715
P3 0,0000 0,0086 0,0030 0,0310 P3 -48,021,692 -320,806 14,070 27,711
B/P 3B (B/P D/P EBITDA/EV)
ALL -0,3333 0,7851 0,6363 100,000 ALL -95,698 34,988 32,416 346,600
P1 0,5469 14,004 11,741 100,000 P1 35,397 56,607 47,941 346,600
P3 -0,3333 0,3231 0,2884 0,9517 P3 -95,698 16,417 19,851 27,996
S/P 3C (S/P B/P EBITDA/EV)
ALL 0,0119 19,787 12,095 405,751 ALL -73,671 41,137 31,803 2,295,296
P1 11,198 41,740 32,404 405,751 P1 12,324 67,169 55,066 448,901
P3 0,0119 0,5098 0,4491 17,743 P3 -73,671 18,558 19,713 100,670
4A (CF/P B/P D/P EBITDA/EV)
ALL -937,405 43,787 43,063 959,521
P1 48,197 76,665 64,489 959,521
P3 -937,405 11,982 24,646 38,521
4B (S/P B/P D/P EBITDA/EV)
ALL -73,671 50,785 43,613 429,333
P1 49,895 84,376 72,636 429,333
P3 -73,671 24,278 27,351 44,753
Note: The table presents minimum, mean, median, and maximum values for both each individual valuation multiple
(Panel A) and each composite measure (Panel B) employed as a basis of portfolio formation for the full sample
period (May 1993- May 2008). The comparable figures for value portfolio (P1) and glamour portfolio (P3) are also
reported separately.
Also the middle portfolios based on S/P multiple outperform the market on the basis of both
alphas and Sharpe ratios. The same holds also for B/P middle portfolio except that the corresponding
Sharpe ratio difference is not statistically significant. Instead, neither value nor glamour portfolios
either outperform or underperform the market portfolio significantly when the portfolios are formed on
10.
Journal of Money, Investment and Banking - Issue 8 (2009) 14
dominate both the market portfolio and the corresponding glamour portfolio in the mean-variance
framework also for this particular sample.
We test the presence of value premium between value and glamour portfolios analogously.
Table 3 indicates that the performance differences between the value and glamour portfolios are
significant in many cases when individual valuation ratios are used as a basis of portfolio selection;
Both the alpha spreads and the Sharpe ratio differences are significant for dividend yield criterion and
moreover, for all earnings multiple criteria. Concerning individual valuation ratios, the most significant
value premium is generated by dividing stocks into 3-quantile portfolios based on dividend yield (The
significance level of both performance difference tests are less than 1 per cent for this valuation ratio).
Instead, for S/P and B/P criteria the results are not significant, though the corresponding value premia
are still positive and the dominance of value portfolios over corresponding glamour portfolios exists in
the mean-variance framework.
Table 3: Performance Comparison of Value (P1) and Glamour (P3) Portfolios (1993-2008)
Sharpe ratio Adjusted SR 2-factor alpha 2-factor
alpha spread 2-factor beta(Rm)
difference difference spread beta(SMB)
P1 vs. P1 vs. P1 vs. P1 vs.
(sign.) (sign.) (sign.) (sign.) P1 P3 P1 P3
P3 P3 P3 P3
EBITDA/EV 27,478 (0,006) 26,144 (0,009) 10,23 % (0,025) 9,83 % (0,013) 0,83 1,06 0,34 0,17
E/P 30,969 (0,002) 23,556 (0,018) 10,31 % (0,016) 10,16 % (0,006) 0,77 1,15 0,29 0,23
CF/P 27,701 (0,006) 24,114 (0,016) 9,84 % (0,028) 9,60 % (0,008) 0,83 1,12 0,32 0,22
D/P 39,847 (0,000) 32,412 (0,001) 13,91 % (0,001) 13,65 % (0,001) 0,75 1,12 0,28 0,17
B/P 0,5203 (0,603) 13,389 (0,181) 4,23 % (0,332) 3,57 % (0,287) 0,84 1,02 0,40 0,12
S/P 0,4617 (0,644) 0,6334 (0,526) 3,24 % (0,455) 2,63 % (0,457) 0,90 0,93 0,41 0,15
2A (D/P EBITDA/EV) 39,471 (0,000) 31,237 (0,002) 14,27 % (0,001) 13,98 % (0,000) 0,76 1,10 0,29 0,17
2B (B/P EBITDA/EV) 17,109 (0,087) 21,800 (0,029) 9,64 % (0,031) 8,88 % (0,018) 0,83 1,08 0,39 0,08
2C (S/P EBITDA/EV) 13,674 (0,171) 14,931 (0,135) 6,77 % (0,134) 6,13 % (0,086) 0,87 1,04 0,41 0,15
3A (B/P D/P E/P) 37,844 (0,000) 30,928 (0,002) 13,41 % (0,002) 13,09 % (0,000) 0,76 1,17 0,31 0,18
3B (B/P D/P EBITDA/EV) 40,302 (0,000) 35,640 (0,000) 15,34 % (0,000) 14,96 % (0,000) 0,74 1,12 0,31 0,15
3C (S/P B/P EBITDA/EV) 18,371 (0,066) 27,633 (0,006) 9,41 % (0,040) 8,73 % (0,033) 0,90 0,99 0,41 0,13
4A (CF/P B/P D/P EBITDA/EV) 34,435 (0,001) 28,501 (0,004) 13,26 % (0,003) 12,86 % (0,001) 0,78 1,13 0,32 0,15
4B (S/P B/P D/P EBITDA/EV) 25,549 (0,011) 25,665 (0,010) 11,58 % (0,010) 10,97 % (0,002) 0,82 1,11 0,38 0,13
Note: The table presents performance differences between value (P1) and glamour (P3) portfolios on the basis of several
performance metrics (i.e., the Sharpe ratio, the adjusted Sharpe ratio, the Jensen alpha, and size-adjusted 2-factor
alpha) for each portfolio formation criterion (significance levels are in parentheses). Both the Sharpe ratio
difference and the adjusted Sharpe ratio difference are evaluated with the Ledoit-Wolf test statistics. In addition,
corresponding 2-factor betas are reported in the two last columns.
3.2. Performance of Portfolios Based on 2-Composite Value Measures
Value portfolio alphas are all significantly positive when using 2-composite measures as portfolio
selection criterion (Table 2). Instead, based on the Sharpe ratio differences, only 2A value portfolio
outperforms the market portfolio significantly. Moreover, the significance level of its alpha is clearly
the highest among 2-composite portfolios. For the sample employed, the performance of value
portfolio can be somewhat enhanced by using 2A composite value measure instead of forming it based
on the best individual selection criterion (i.e., D/P). Noteworthy is also that when B/P and S/P ratios
are combined with EBITDA/EV ratios both of these 2-composite value portfolios generate positive and
significant alpha while the value portfolios based solely on either B/P or S/P do not make it.
The value premium tests indicate that outperformance of value portfolio over corresponding
glamour portfolio can be increased only marginally compared to that based on the best individual
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15 Journal of Money, Investment and Banking - Issue 8 (2009)
valuation ratio (i.e., D/P) by incorporating EBITDA/EV besides D/P into selection criterion (Table 3).
The value premium is also significant on the basis of 2B selection criteria, while insignificant based on
2C selection criteria.
3.3 Performance of Portfolios Based on 3-Composite Value Measures
For the sample employed, the best value portfolio based on the 3-composite measures is that based on
3B selection criterion (Exhibit 2). However, the performance difference between the best 2-composite
portfolio and the best 3-composite portfolio is negligible. Thus, adding B/P to composite value
measure besides D/P and EBITDA/EV does not improve the performance of value portfolio, but on the
other hand, neither deteriorates it. Performance comparison between 3A and 3B value portfolios
reveals some advantage of using EBITDA/EV instead of E/P as earnings multiple.
The performance difference between value and glamour portfolios is significant for all 3-
composite measures examined; the greatest difference among all selection criteria is obtained when
division of stocks is based 3B criterion (Table 3). Substituting P/E with EBITDA/EV lowers the
corresponding alpha spread by two percentages, approximately, but the spread still maintains its
significance. The same holds also for the total risk –adjusted performance difference. The results are
also parallel for the third 3-composite measure (i.e., 3C) with the exception that the significance level
of the Sharpe ratio difference is 6.6 per cent in that case. Altogether, the evidence of the value premia
from using 3-composite measures is very strong and not explained by different degrees of asymmetry
in portfolio return distributions as indicated by significant performance differences based on adjusted
Sharpe ratios.
3.4. Performance of Portfolios Based on 4-Composite Value Measures
Finally, we add the fourth valuation ratio to the best 3-composite selection criterion (i.e., 3B). The
results show that including volume multiple besides book value multiple, earnings multiple and
dividend yield does not enhance the performance of value portfolio but rather vice versa. Instead,
including another earnings multiple (i.e., CF/P) besides EBITDA/EV seems to work somewhat better;
over the 15-period examined it results in 8.98% alpha that is significant at the 1% level, and in the
Sharpe ratio that is significantly better than that of the market at the 5% level (Table 2). In spite of that,
3B value portfolio outperforms comparable 4A portfolio on the basis of every performance metrics
employed.
The rank order of two 4-composite measures examined remains the same also when
considering performance difference between the value and glamour portfolios (Table 3). However,
value premiums are positive and highly significant for both criteria based on both Sharpe ratios and
alphas. Nevertheless, including S/P ratio besides B/P, D/P and EBITDA/EV lowers the alpha spread by
almost 4 percentages while adding P/CF to the same 3-composite measure narrows the corresponding
spread much less. Thus, the volume multiple does not seem to add any value to 3-composite measure
for the sample employed. Moreover, the added value of including the fourth valuation criterion in the
best 3-composite value measure is negative in both 4-composite cases being tested.
3.5. Comparison of All Portfolio Formation Criteria
For the sake of comparability, the risk-return characteristics of all value and glamour portfolios based
on 14 selection criteria over the full-length period are illustrated in Figure 1 where the separate clusters
of value and glamour portfolios are clearly distinguishable. All the value portfolios dominate the
market portfolio in the mean-variance framework, while the market portfolio dominates all the glamour
portfolios except those based on S/P and B/P multiples. In addition, all the value portfolios dominate
the B/P glamour portfolio, and also the S/P glamour portfolio is dominated by every value portfolio
other than that based on 3C composite measure. As a consequence, three portfolios forming the mean-
variance efficient set are all value portfolios; D/P value portfolio that has the lowest risk, C2A has the
highest return, and 3B has the best Sharpe ratio, respectively.
12.
Journal of Money, Investment and Banking - Issue 8 (2009) 16
Figure 1: Risk-Return Characteristics of Value (P1) and Glamour (P3) Portfolios
23 % 2A (P1)
3B (P1)
D/P (P1)
21 %
3A (P1) 4A (P1)
19 % EBITDA/EV (P1)
Average annual return
3C (P1)
E/P (P1) 2B (P1)
CF/P (P1) 4B (P1) 2C (P1)
17 %
S/P (P1)
B/P (P1)
15 %
S/P (P3) M B/P (P3)
13 % 2C (P3)
11 % 2B (P3)
3C (P3) EBITDA/EV (P3) E/P (P3)
3A (P3)
2A (P3) D/P (P3)
9% CF/P (P3) 4A (P3)
3B (P3)
4B (P3)
7%
15 % 17 % 19 % 21 % 23 % 25 % 27 % 29 %
Average annual volatility
Note: The figure illustrates the locations of every value (P1) and glamour (P3) portfolio formed on the basis of 14
different classification criteria in the risk-return space (Symbol M represents the position of market portfolio and
RF denotes the risk-free rate of return).
3.6. Decomposition of Portfolio Performance Based on Bull and Bear Market Periods
The previous results show that following value strategies would really have paid off in the Finnish
stock market during the sample period of 1993-2008 since outperformance of several value portfolios
is undisputable in spite of performance metric employed. In order to trace how the outperformance of
value strategies have attributed during the sample period we perform an additional test by dividing the
sample period into bear and bull market conditions according to the overall development of the Finnish
stock market. To separate bullish and bearish periods we use a simple filter rule according to which 25
% gain (loss) in value of the market portfolio from previous tough (peak) is required to determine the
ongoing period as bullish (bearish). For the full sample period we identify four bear market periods and
equal amount of bull market periods. Altogether, the total lengths of the stacked bear and bull market
periods are 37 and 143 months, respectively.
The division of the full sample period into bear and bull market periods reveals that the
outperformance of value strategies is mostly attributed to the fact that the value portfolios lose much
less of their values during bear markets than do stocks on average (see Appendix A). While average
monthly loss of all 14 value portfolios included in the examination is only 1.59% during bear market
periods it is 4.02% for the market portfolio and 4.76% for the corresponding glamour portfolios. In
addition, the same value strategies that outperformed the market most during the full sample period,
suffer least from bear market conditions. Based on the Sharpe ratio difference test the value premium is
significant for every of 14 selection criteria except for CF/P criterion 10 (Table 4). Instead, the
corresponding single-factor alpha spreads are significant only for EBITDA/EV and E/P criteria.
However, when the size effect on alphas is controlled, all the 2-factor alpha spreads except that based
on D/P criterion turn statistically significant mainly for two reasons; Firstly, the standard errors of
alphas are systematically smaller in 2-factor models than they are in corresponding single-factor
models. Secondly, adding the size premium factor into the single-factor model increases alphas of
10
Though there are limitations in comparing the negative Sharpe ratios (e.g., see Israelsen (2005)), the comparison is valid in this context when all the
value portfolios dominate the corresponding value portfolios in the mean-variance framework (i.e., value portfolios have both higher excess return
and lower volatility than comparable value portfolios).
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17 Journal of Money, Investment and Banking - Issue 8 (2009)
value portfolios remarkably during the bear market periods (see Appendix B). This is a consequence of
higher SMB betas of value portfolios which in case of negative average SMB return increases alphas.
Table 4: Performance Comparison of Value and Glamour Portfolios during Bear Market Periods
Adjusted SR 2 factor alpha 2-factor 2-factor
zJK(sign.) alpha spread
difference spread beta(Rm) beta(SMB)
P1 vs. P1 vs.
P1 vs. P3 (sign.) P1 vs. P3 (sign.) (sign.) P1 P3 P1 P3
P3 P3
EBITDA/EV 21,517 (0,031) 34,810 (0,000) 16,18 % (0,095) 22,27 % (0,011) 0,75 0,89 0,31 -0,01
E/P 24,606 (0,014) 30,462 (0,002) 18,67 % (0,078) 22,89 % (0,008) 0,79 1,03 0,32 0,08
CF/P 15,253 (0,127) 23,883 (0,017) 9,75 % (0,359) 14,00 % (0,054) 0,80 1,03 0,32 0,08
D/P 17,045 (0,088) 27,744 (0,006) 10,28 % (0,358) 15,40 % (0,124) 0,70 1,03 0,34 0,04
B/P 18,296 (0,067) 21,824 (0,029) 12,31 % (0,208) 17,26 % (0,009) 0,79 0,91 0,33 0,05
S/P 16,704 (0,095) 21,783 (0,029) 8,73 % (0,365) 13,99 % (0,028) 0,85 0,82 0,36 0,05
2A (D/P EBITDA/EV) 19,281 (0,054) 30,458 (0,002) 14,04 % (0,216) 20,55 % (0,062) 0,75 0,96 0,38 0,01
2B (B/P EBITDA/EV) 18,758 (0,061) 27,186 (0,007) 15,36 % (0,127) 21,83 % (0,005) 0,77 0,97 0,33 -0,03
2C (S/P EBITDA/EV) 19,243 (0,054) 28,081 (0,005) 11,60 % (0,235) 17,75 % (0,025) 0,79 0,92 0,36 0,00
3A (B/P D/P E/P) 21,833 (0,029) 27,233 (0,006) 17,00 % (0,117) 23,28 % (0,003) 0,77 1,00 0,35 -0,01
3B (B/P D/P EBITDA/EV) 18,215 (0,069) 25,758 (0,010) 14,94 % (0,170) 22,16 % (0,013) 0,71 0,94 0,34 -0,05
3C (S/P B/P EBITDA/EV) 18,149 (0,070) 25,614 (0,010) 13,54 % (0,183) 20,34 % (0,009) 0,78 0,91 0,34 -0,04
4A (CF/P B/P D/P EBITDA/EV) 17,105 (0,087) 25,400 (0,011) 13,13 % (0,240) 20,31 % (0,041) 0,75 0,99 0,37 -0,04
4B (S/P B/P D/P EBITDA/EV) 16,708 (0,095) 27,402 (0,006) 12,53 % (0,242) 19,20 % (0,017) 0,77 1,00 0,34 -0,05
Note: The table presents performance differences between value (P1) and glamour (P3) portfolios on the basis of several
performance metrics (i.e., the Sharpe ratio, the adjusted Sharpe ratio, the Jensen alpha, and size-adjusted 2-factor
alpha) over the stacked bear market period for each portfolio formation criterion (significance levels are in
parentheses). Both the Sharpe ratio difference and the adjusted Sharpe ratio difference are evaluated with the
Ledoit-Wolf test statistics. In addition, corresponding 2-factor betas are reported in the two last columns.
During bull periods both return differences and performance differences between value and
glamour portfolios are much smaller (see Appendix C and Table 5). However, the same value
strategies that outperform the market most during the full sample period do the same also during bull
market conditions. The same holds also for the relative performance between value and glamour
portfolios. The greatest alpha spread is generated by the best 3-composite measure 3B, followed by the
best 2-composite measure 2A. Both of these measures employ EBITDA/EV and D/P multiples in
portfolio formation. The sub-period analysis reveals also the reason for the usefulness of combining
dividend yield and earnings multiple in forming the value portfolios; the highest value premiums of
individual valuation ratios are gained by strategies based on earnings multiples during bear market
conditions while in bullish periods D/P selection criterion leads to the highest performance difference
between value and glamour portfolios that are based on individual valuation ratios (Tables 4 and 5).
3.7. Impact of Firm Size Effect on Results
Since many studies have documented the relationship between value and size anomalies we test what
happens to wide value premiums observed on the basis of the standard CAPM model when SMB factor
is added to the regression models as another explanatory variable. We compare single-factor alphas of
both value and glamour portfolios with corresponding 2-factor alphas. Generally and somewhat
surprisingly, “size-adjusted” 2-factor alphas calculated over the full sample period do not deviate
significantly from comparable single-factor alphas for either value or glamour portfolios. The alpha
differences are statistically significant neither in bull nor bear market periods though the difference is
distinctly greater during bear market conditions and large enough to turn many insignificant alpha
spreads to significant after including the SMB factor in the regression model (see Appendix B and
Table 4). Somewhat surprisingly, alpha spreads between extreme portfolios are generally more
significant based on 2-factor model than based on single-factor model (Tables 3-5). This finding that is
partially explained by systematically smaller standard errors of 2-factor alphas holds for the full sample
period, as well as for bull and bear market periods, and indicates that the value premium is not
explained by size anomaly in the Finnish stock market.
14.
Journal of Money, Investment and Banking - Issue 8 (2009) 18
Table 5: Performance Comparison of Value and Glamour Portfolios during Bull Market Periods
Adjusted SR 2 factor alpha 2-factor 2-factor
zJK (sign) alpha spread
difference spread beta(Rm) beta(SMB)
P1 vs. P1 vs.
P1 vs P3 P1 vs. P3 (sign.) (sign.) (sign.) P1 P3 P1 P3
P3 P3
EBITDA/EV 10,316 (0,302) -0,1113 (0,911) 5,21 % (0,362) 4,99 % (0,300) 0,88 1,07 0,34 0,24
E/P 17,186 (0,086) -0,5425 (0,588) 6,40 % (0,220) 6,34 % (0,137) 0,80 1,15 0,29 0,28
CF/P 17,640 (0,078) 0,4938 (0,621) 7,08 % (0,192) 7,02 % (0,009) 0,86 1,12 0,31 0,27
D/P 29,964 (0,003) 18,845 (0,059) 11,24 % (0,030) 10,94 % (0,018) 0,78 1,12 0,25 0,22
B/P -15,234 (0,128) -0,7916 (0,429) 0,08 % (0,988) -0,27 % (0,948) 0,88 1,03 0,42 0,15
S/P -12,612 (0,207) -11,859 (0,236) -0,70 % (0,898) -0,99 % (0,819) 0,94 0,94 0,42 0,19
2A (D/P EBITDA/EV) 29,654 (0,003) 14,510 (0,147) 11,52 % (0,033) 11,13 % (0,015) 0,78 1,11 0,25 0,23
2B (B/P EBITDA/EV) -0,2601 (0,795) -0,2091 (0,834) 4,98 % (0,373) 4,51 % (0,303) 0,87 1,07 0,41 0,11
2C (S/P EBITDA/EV) -0,8181 (0,413) -22,514 (0,024) 1,95 % (0,733) 1,61 % (0,706) 0,91 1,04 0,43 0,20
3A (B/P D/P E/P) 25,338 (0,011) 0,6294 (0,529) 10,80 % (0,041) 10,54 % (0,016) 0,78 1,19 0,30 0,24
3B (B/P D/P EBITDA/EV) 28,478 (0,004) 16,973 (0,090) 13,16 % (0,013) 12,63 % (0,004) 0,75 1,13 0,29 0,22
3C (S/P B/P EBITDA/EV) -0,9106 (0,362) 0,3871 (0,699) 1,48 % (0,794) 1,16 % (0,792) 0,97 0,95 0,42 0,19
4A (CF/P B/P D/P EBITDA/EV) 22,242 (0,026) 0,1136 (0,910) 9,22 % (0,091) 8,93 % (0,045) 0,81 1,12 0,29 0,23
4B (S/P B/P D/P EBITDA/EV) 0,7846 (0,433) -0,2394 (0,811) 7,65 % (0,160) 7,36 % (0,061) 0,85 1,11 0,39 0,19
Note: The table presents performance differences between value (P1) and glamour (P3) portfolios on the basis of several
performance metrics (i.e., the Sharpe ratio, the adjusted Sharpe ratio, the Jensen alpha, and size-adjusted 2-factor
alpha) over the stacked bull market period for each portfolio formation criterion (significance levels are in
parentheses). Both the Sharpe ratio difference and the adjusted Sharpe ratio difference are evaluated with the
Ledoit-Wolf test statistics. In addition, corresponding 2-factor betas are reported in the two last columns.
Since the changes in alphas based on different factor models are marginal for the most part we
do not analyze 2-factor alphas in detail. In spite of its marginal impact on alphas, SMB is a significant
explanatory factor for every value and glamour portfolios in the full sample period. Instead, during
bear market conditions SMB is not significant for glamour portfolios. In respect to size effect, our
results are parallel with Dhatt et al. (1999), Bird and Whitaker (2003) and Bird and Casavecchia
(2007b) who find that the average market-cap of value stocks is generally smaller than that of growth
stocks. Though the marginal contribution of SMB in this study is significant more often for value
portfolios than for glamour portfolios, the impact of size factor on alphas is not significant in any case
on the basis of statistical significance tests of difference between single-factor and 2-factor alphas (see
Appendix B). Therefore, the main conclusions drawn based on single-factor alphas remain unchanged.
4. Summary and concluding remarks
We test the performance of various value strategies in the Finnish stock market over several economic
cycles. For the sample employed, the dividend yield is the most successful selection criterion of the six
individual valuation ratios examined. Every performance metric employed in performance difference
tests agrees on the significant outperformance of D/P value portfolio over the market over the 15-year
sample period. Also P/E value portfolio outperforms the market based on standard performance
metrics. However, taking account of dimensions of skewness and kurtosis in risk metrics its superiority
to the market portfolio is no more statistically significant.
Consistently with the results of Dhatt et al. (1999 & 2004) and Leivo et al. (2009), our results
give some evidence that the performance of value strategies based on individual valuation multiples
could be somewhat enhanced by using the composite selection criteria. The greatest alpha is achieved
by combining D/P, EBITDA/EV and B/P selection criteria (i.e., composite value measure 3B).
Particularly beneficial is to include D/P in composite value measures while the added-value of
including S/P is negative for this particular sample. The performance comparison between value and
corresponding glamour portfolios highlights the advantages of using composite value measures. Their
performance difference is significant based on 7 out of 8 composite value measures no matter what
performance metrics is employed. The value premium is greatest when using composite value measure
3B as a selection criterion. Moreover, the 3-quantile portfolios could be used as a basis of long-short
strategies; for example 130/30 long-short strategy increases the average annual return of the best value
15.
19 Journal of Money, Investment and Banking - Issue 8 (2009)
portfolio (3B) by more than 3.5 percentages without any increase in volatility. 11 This is more than
enough to cover the transaction costs from short sales of glamour stocks worth 30% of the portfolio's
nominal value. In addition, the beta of 130/30 long-short portfolio decreases to 0.44 and as a
consequence, the average annual alpha increases by 4.6 percentages.
The value premium in the Finnish stock market is not explained by higher risk of value
portfolios since the case is just reverse. Both volatilities and betas are lower for the value portfolios
than they are for the corresponding glamour portfolios. The same holds also for skewness- and
kurtosis-adjusted risk metrics employed (i.e., SKAD). Somewhat surprisingly, including size factor as
another explanatory variable in the regression model employed in determining abnormal risk-adjusted
returns has only marginal impact on alphas in the full sample period.
The examination of the value premium on annual basis (not reported) reveals that there are
occasional time-periods when the value premium is negative. Therefore, it would be interesting topic
for further research to examine whether the long-term performance of active Finnish equity portfolio
could be further enhanced by means of such style timing strategies that have been proven to be
successful in major stock markets (e.g., see Asness et al., 2000; Bauer et al., 2004; Nalbantov et al.,
2006). Moreover, the recent results of Bird and Whitaker (2004) and those of Bird and Casavecchia
(2007a, 2007b) have shown that performance of value portfolio formed on the basis of individual
valuation ratios (B/M and S/P) can be further enhanced by combining them with price momentum.
Thus, it would be interesting to study if the same holds also for the combination of composite value
measures and price momentum. Altogether, the above-mentioned extensions provide interesting topics
for further research.
11
We choose 3B portfolios as a basis of the long/short strategy since the overall performance difference between the value portfolio and the corresponding
glamour portfolio is the biggest for this selection criterion. In our example, 130/30 long-short 3B portfolio is rebalanced once a year on the same day as
original 3-quantile portfolios (i.e., on the first trading day of May)
16.
Journal of Money, Investment and Banking - Issue 8 (2009) 20
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