Value Investing Anomalies

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The Quarterly Review of Economics and Finance 50 (2010) 527–537

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The Quarterly Review of Economics and Finance
journal homepage: www.elsevier.com/locate/qref

Value investing anomalies in the European stock market: Multiple Value,
Consistent Earner, and Recognized Value
Gregor Elze ∗
University of Graz, Department of Statistic and Operations Research, Universtaetsstr. 15, 8010 Graz, Austria

a r t i c l e

i n f o

Article history:
Received 11 March 2010
Accepted 21 June 2010
Available online 6 July 2010
JEL classification:
G11
G12
G14
G19

a b s t r a c t
Empirical academic studies have consistently found that value stocks outperform glamour stocks and
the market as a whole. This article extends prevailing research on existing value anomalies. It evaluates
simple value strategies for the European stock market (compared to many other studies that test market
data on a country-by-country basis) as well as sophisticated multi-dimensional value strategies that also
include capital return variables (Consistent Earner Strategy) and momentum factors (Recognized Value
Strategy), the latter reconciling intermediate horizon momentum and long-term reversals of behavioral
finance theories. It can be shown that these “enhanced” value strategies can produce superior returns
compared to returns of the whole market or “simple” value strategies without capturing higher risks
applying traditional risk measures.
© 2010 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.

Keywords:
Behavioural Finance
Market Anomalies
Value Investing

1. Introduction
In their 1934 book, Security Analysis1 , Benjamin Graham and
David Dodd argued that out-of-favor stocks are sometimes underpriced in the marketplace, and that investors cognizant of this
phenomenon could capture strong returns. This philosophy is now
widely known as value investing. Although value investing has
taken many forms since its inception, it generally involves buying shares which appear underpriced based on some form(s) of
fundamental analysis. Value shares typically feature low price-tobook, price-to-earnings, or price-to-cash flow ratios, while glamour
stocks generally are characterized by valuation metrics at the opposite end of the spectrum.
As early as 1977, academic studies have used share price and
earning per share data to classify stocks into the value or glamour
categories and compare historical performance. Stocks with low
price-to-earnings multiples (often called “value” stocks) appear
to provide higher rates of return than stocks with high priceto-earnings ratios as first shown by Nicholson (1960) and later
confirmed by Ball (1978), Basu (1977, 1983), and Fama and MacBeth

∗ Tel.: +49 17663198991.
E-mail address: r [email protected].
1
Graham and Dodd (2005).

(1973).2 De Bondt and Thaler (1985) obtain a similar result for their
contrarian strategy based on buying stocks with low past returns
because of the behavioral hypothesis of investor overreaction. A
stock’s price-to-book value ratio has also been found to be a useful predictor of future returns. Fama and French (1992) concluded
that size and price-to-book value together provide considerable
explanatory power for future returns in U.S. markets.
These results raised questions about the efficiency of the market if one accepts the capital asset pricing model, as Lakonishok,
Schleifer and Vishny pointed out. In 1994, they published “Contrarian Investment, Extrapolation, and Risk3 ”. Using data from
1968 to 1994, they grouped U.S. stocks into value and glamour
segments based on price-to-book, price-to-cash flow, and price-toearnings ratios, as well as sales growth. The researchers concluded
that, for a broad range of definitions of “value” and “glamour”,
value stocks consistently outperformed glamour stocks by wide
margins.
In their 1998 study, “Value versus Growth: The International
Evidence”, Fama and French tackled the question of whether value
stocks tended to outperform glamour stocks in markets outside
the U.S. The researchers found that, from 1975 to 1995, value

2
3

Papers are cited in detail at the end of this article.
An update was published in 2004: Chan, L., & Lakonishok, J. (2004).

1062-9769/$ – see front matter © 2010 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.qref.2010.06.005

528

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

stocks outperformed glamour stocks in 12 of 13 major national
equity markets. In their opinion, this laid to rest the possibility
that the value outperformance seen by Lakonishok, Schleifer and
Vishny was simply a sample-specific happenstance within the U.S.
market.4
While there is some agreement that value strategies have produced superior returns, the interpretation of why they have done so
is more controversial. “Behavioralists” believe that investors consistently tend to overpay for “growth” stocks that subsequently fail
to live up to expectations (for example, Kahneman & Riepe, 1998
and Gilovich, Griffin, and Kahneman (2002)). In their view value
strategies produce higher returns because they are contrarian to
“naive” strategies followed by other investors. These naive strategies might range from extrapolating past earnings growth too far
into the future, to assuming a trend in stock prices, to overreacting
to good or bad news, or to simply equating a good investment with
a well-run company irrespective of price. Regardless of the reason,
some investors tend to get overly excited about stocks that have
done very well in the past and buy them up, so that these “glamour” stocks become overpriced. Similarly, they overreact to stocks
that have done very poorly, oversell them, and these out-of-favor
“value” stocks become underpriced.
This article is based on prevailing research on existing value
anomalies.5 It evaluates simple value strategies for the European
stock market as well as sophisticated multi-dimensional value
strategies that also include capital return variables (Consistent
Earner Strategy) and momentum factors (Recognized Value Strategy).
In Section 2 of the article our methodology is briefly discussed.
Section 3 (a) examines a variety of simple classification schemes for
value and glamour stocks based on dividend yield, price-to-book
and price-to-earnings ratio. Contrary to many studies that test market data on a country-by-country basis, all strategies are applied
and modulated for the European stock market. The EuroStoxx index
has been selected as the market proxy. It can be shown that simple value strategies have produced superior returns motivating our
subsequent use of variable combinations.
Section 3.1 (b) evaluates strategies based on multi-dimensional
selection criteria. First, simple value measures are combined (Multi
Value Strategy). In a second step we combine more sophisticated
multi-dimensional value strategies that also include capital return
variables (Consistent Earner Strategy) and momentum factors
(Recognized Value Strategy). It can be shown that while multidimensional value strategies based on a combination of simple
value variables do not further improve investment performance
and statistical significance, strategies based on combinations
of value and capital return variables (e.g. Return on Equity)
improve the statistical significance of results (while generating
compatible investment returns). Strategies based on combinations of value and momentum variables improve both investment performance and significance compared to simple value
strategies.
Finally in Section 4 the question of whether strategies based
on our investment selection criteria are fundamentally riskier is
evaluated. Evidence is provided that, in general, value strategies
have outperformed glamour strategies quite consistently without
support for the hypothesis that value strategies are fundamentally
riskier than glamour strategies. Conclusions are drawn in Section
5.

4
Simliar results had been shown by Lakonishok, Hamao, and Chan (1991) for
Japan.
5
We widely follow Lakonishok et al. (1994) in the structure of our analysis.

2. Methodology
The sample period covered in this study starts on July 1, 19946
and ends June 30, 2009.7 For portfolio strategies that are tested over
2-year (3-year) performance horizons the last reformation date is
July 1, 2007 (July 1, 2006). As market proxy for the European stock
market the EuroStoxx index has been selected. Results are also verified for the EuroStoxx50 in order to verify that results still hold
if only large capitalization equities are examined. Results for stock
returns of indices containing only large firms are less contaminated
by significant look-ahead or survivorship bias.8,9
Based on the index we form our model portfolios using as a first
step one-dimensional (single) accounting ratios, such as dividend
yield (DY), price-to-book10 (P/B) and price-to-earnings11 (P/E). In
addition, corporate return (RoE) and momentum (Levy2712 , Relative Strength – 3 months) ratios13 are computed for a Consistent
Earner Strategy (trying to mimic investment styles of successful
and well-known value investors who focus on “outstanding companies at a sensible price”)14 and a Recognized Value Strategy (trying
to further improve performance by timing reversals better based
on the stock momentum life cycle hypothesis).15 Then ratios and
historical performance data are used to sort individual stocks into
portfolios.16 Based on the investment strategy chosen, deciles are
formed for which performance is measured for 1–3-year time horizons. Within each of our portfolios, we equally weight all stocks
and compute returns using a buy-and-hold strategy for years t + 1,
t + 2 and t + 3 relative to the time of formation. If a stock is delisted
from the stock exchange during a year, we continue with the same
portfolio using the return of that stock at the time it was last traded

6
Results for starting dates on January 1, April 1 and October 1 were also tested
and results are comparable to conclusions drawn from yearly starting dates on July
1.
7
If the 30th is not a weekday, then the last trading day of the month is used. Years
in tables and graphs refer to a time period from July 1 that year until June 30 of the
subsequent year. Formation and reformation occur based on publicly available price
and accounting data from the previous year (t−1). Results for current year estimates
as accessible at formation and reformation dates were comparable. Reformation at
the beginning of the second quarter was chosen in order to ensure that fundamental
company information for the entire previous year published in annual reports, SEC
filings or by the financial media was available to investors and incorporated into
valuation ratios. 1994 was chosen as the first formation year because the EuroStoxx
was created in 1999 and index constituents were recalculated back to this time.
8
Look-ahead and survivorship bias are common types of sample selection biases.
The first is created by the use of information or data in a study or simulation that
would not have been known or available during the period being analyzed. This
will usually lead to inaccurate results in the study or simulation. To avoid this bias
we calculated ratios based on data available at the time of portfolio formation and
reformation, not from revisions published thereafter. The second bias occurs, for
example, when backtesting an investment strategy on a large group of stocks. Then it
may be convenient to look for securities that have data for the entire sample period.
However, eliminating a stock that stopped trading, or shortly left the market, would
input a bias in data samples. To avoid this problem we used historical constituent
lists for the EuroStoxx when we constructed our quantile portfolios.
9
Banz and Breen (1986), Kothari, Shanken, and Sloan (1992). La Porta (1993) also
points out that the selection bias is less serious among larger firms.
10
Rosenberg, Reid and Lanstein (1984) and Lee and Swaminathan (2000) published the first cited study that evaluated the performance of the book-to- market
strategy.
11
Basu (1977) first looked at the relationship between common stocks and their
price-to-earnings ratios. He found that a low P/E ratio portfolio earned 6% more per
year than a high P/E portfolio in the 14-year sample.
12
Levy, R. (1967).
13
RoE = Return on Equity, Levy27 = a stock’s price divided by its price 27 weeks
earlier, Relative Strength – 3 months = performance of a stock compared to the index
during the last 3 months (MO3m).
14
Greenblatt (2006).
15
Oyefeso (2004), Lee, C., & Swaminathan, B. (2000). Price momentum & trading
value. Journal of Finance.
16
As Lakonishok et al. (1994) we consider only positive ratios.

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

529

Table 1
Yearly average decile returns for portfolio strategies based on one-dimensional classifications by various measures of valuea .
Panel A: DY (t0)
Return overview: Yearly

Value premium

Glamour

R1
R2
R3
ANN
CR3
AR

Value

1

2

3

4

5

6

7

8

9

10

10−1

5.07%
6.81%
7.20%
6.36%
20.30%
6.36%

9.89%
11.19%
5.56%
8.85%
28.97%
8.88%

8.01%
5.69%
7.19%
6.96%
22.36%
6.96%

10.42%
11.52%
9.56%
10.50%
34.91%
10.50%

9.07%
9.71%
8.14%
8.97%
29.41%
8.98%

9.42%
12.56%
9.39%
10.45%
34.74%
10.46%

11.16%
13.02%
10.41%
11.52%
38.71%
11.53%

12.86%
11.49%
7.67%
10.65%
35.48%
10.67%

8.28%
11.59%
15.75%
11.83%
39.85%
11.87%

13.66%
12.46%
15.48%
13.86%
47.61%
13.87%

8.60%
5.64%
8.28%
7.50%
27.31%
7.51%

Panel B: P/B (t0)
Return overview: Yearly

Value premium

Glamour

R1
R2
R3
ANN
CR3
AR

Value

1

2

3

4

5

6

7

8

9

10

10−1

7.23%
7.64%
5.09%
6.65%
21.29%
6.65%

7.32%
8.70%
7.71%
7.91%
25.65%
7.91%

8.04%
7.70%
6.20%
7.31%
23.57%
7.31%

9.88%
10.44%
8.45%
9.59%
31.60%
9.59%

10.39%
9.22%
7.64%
9.08%
29.78%
9.08%

9.45%
9.88%
11.18%
10.17%
33.72%
10.17%

8.60%
11.40%
10.81%
10.26%
34.05%
10.27%

9.22%
12.88%
10.39%
10.82%
36.09%
10.83%

9.01%
9.87%
12.95%
10.60%
35.29%
10.61%

12.64%
18.99%
15.75%
15.76%
55.14%
15.79%

5.40%
11.35%
10.67%
9.12%
33.84%
9.14%

Panel C: P/E (t0)
Return overview: Yearly

Value premium

Glamour

R1
R2
R3
ANN
CR3
AR

Value

1

2

3

4

5

6

7

8

9

10

10−1

1.11%
7.51%
6.60%
5.04%
15.88%
5.07%

5.87%
7.20%
9.26%
7.44%
24.01%
7.45%

9.46%
8.92%
5.78%
8.04%
26.11%
8.05%

11.03%
11.16%
8.72%
10.30%
34.18%
10.30%

10.00%
11.58%
7.69%
9.75%
32.18%
9.76%

9.49%
13.38%
5.94%
9.56%
31.51%
9.60%

12.74%
9.31%
11.22%
11.08%
37.06%
11.09%

9.04%
9.42%
10.12%
9.53%
31.39%
9.53%

11.12%
11.97%
12.95%
12.01%
40.54%
12.02%

13.76%
13.87%
13.39%
13.68%
46.89%
13.68%

12.66%
6.35%
6.79%
8.64%
31.01%
8.60%

Panels A–C present the average percentage decile returns for one-dimensional value strategies formed in ascending order based on P/E and P/B; descending order based on
dividend yield (DY). The value portfolio refers to the decile portfolio containing stocks ranking lowest on P/E or P/B, or highest on dividend yield (DY). The glamour portfolio
contains stocks with precisely the opposite set of rankings. Portfolio reformation occurs yearly at the beginning of July during the period from 1994/95 to 2008/09. The
right-most column contains the value premium based on the performance difference between decile 10 and 1.
Panel A: percentage decile returns as described above for a one-dimensional value strategy based on dividend yield (DY).
Panel B: percentage decile returns as described above for a one-dimensional value strategy based on P/E.
Panel C: percentage decile returns as described above for a one-dimensional value strategy based on P/B.
a
R1 = 1st year return, R2 = 2nd year return, R3 = 3rd year return, ANN = annualized 3-year return, CR3 = compounded 3 year return, AR = average 3 year return, (t0) = refer
to annotation 15.

until the end of the observation period.17 There is no reformation in
the portfolios during the performance measurement period. Portfolio formation occurs18 at the beginning of July each year and each
stock gets the same weight. Stock, dividend and index information
are derived from Thomson-Reuters Datastream and Factset based
on closing prices. Performance is measured by stock price changes
and dividend payouts. Transaction costs are not included.
In a second step we combine accounting ratios following
the selection procedure used by Lakonishok, Shleifer and Vishny
(1994). For two-dimensional value strategies, stocks are classified
into nine groups by independently sorting them in ascending/descending order into three arrays ((3) bottom 30%, (2) middle

17
The reasons for stocks to be delisted from the stock exchange during a reformation period were mostly acquisitions by other corporations. The last traded
price therefore was taken to approximate real performance of that stock in a given
period. Return on cash proceeds during the remaining period was not included in
the performance calculation.
18
Performance data for year 2 and year 3 (assuming no reformation) is also indicated.

40%, and (1) top 30%) based on each of two variables. The sorts are
12 pairs of variables: DY and P/E, DY and P/B, P/E and P/B, RoE and
DY, RoE and P/E, RoE and P/B, DY and Levy27, DY and MO3m, P/E and
Levy27, P/E and MO3m, P/B and Levy27, P/B and MO3m. Depending
on the two variables being used for classification, the value portfolio either refers to the portfolio containing stocks ranked in the top
group (1) on both variables from among P/E, P/B (sorted in ascending order), or else the portfolio in the top group on one of those
variables or/and in the top group (1) on reversely sorted DY, RoE,
Levy 27, and MO3m. The glamour portfolio19 contains stocks with
precisely the opposite set of rankings. Portfolio formation and reformation occur yearly at the beginning of July during the period
from 1994/95 to 2008/09. For each quartile, performance is measured using the same procedure as for one-dimensional (single)
accounting ratios.

19
In the case of momentum variables, it is usually inappropriate to talk of value
and glamour criteria but for simplicity we refer to the value quantile for the top
parameter characteristics and to glamour for the bottom.

530

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

Graph 1. Yearly and 3-year annualized decile returns. Graph 1 shows the yearly percentage decile portfolio returns for years 1–3 and annualized for the entire 3-year period
for a one-dimensional value strategy based on dividend yield (DY). Portfolio reformation occurs yearly at the beginning of July during the period from 1994/95 to 2008/09.

3. (a) Performance evaluation: one-dimensional value
strategies
Table 1, Panels A–C present the yearly average decile returns for
one-dimensional value strategies formed in ascending order based
on P/E and P/B;20 descending order based on dividend yield (DY).21
The value portfolio refers to the decile portfolio containing stocks
ranking lowest on P/E and P/B, or highest on dividend yield (DY).
The glamour portfolio contains stocks with precisely the opposite
set of rankings. Portfolio formation and reformation occur yearly at
the beginning of July during the period from 1994/95 to 2008/09.
In addition, year 2 and year 3 returns (assuming no reformation
after year 1), the annualized 3-year return, the compounded 3-year
return and the average 3-year return are indicated. The right-most
column contains the value premium based on the performance
difference between decile 10 and 1.
The yearly average (R1) return differences (value (decile 10)
minus glamour (decile 1)) presented above fall in a range between
5.40% and 12.66% depending on the value variable chosen. The outperformance is statistically significant based on a 5% significance
interval except for price-to-book (P/B).22,23 Consequently, the conclusion can be drawn that the value anomaly discovered for most
indexes worldwide also holds for the European market as proxied
by the EuroStoxx index.24

20
Ratios in this article are based on actual data available at formation and reformation. Ratios in graphs and tables are therefore (for better differentiation) assigned
a (t0). Results for ratios based on estimated data were also tested. Outcomes are
comparable.
21
Though RoE (Return in Equity), Levy27 (stock price divided by its price 27 weeks
before) and Relative Strength – 3 month (MO3m) constitute no value variable they
are used in combination with value variables for our two-dimensional Consistent
Earner Strategy and Recognized Value Strategy strategies. For their returns as standalone variable, refer to the appendix section.
22
Results of the t-statistic for the test of the hypothesis that the differences in
returns between value and glamour are equal to zero are presented in Table 3, page
14.
23
For simple return and momentum variables, return differences fall in a range
between 2.50% and 11.44%. Results are presented in the appendix section. Though it
was not part of the research question formulated in this study it nevertheless seems
worthwhile to mention that return differences for single capital and momentum
variables are not statistically significant (page 14).
24
Comparable results are obtained for the EuroStoxx50 index. Lakonishok et al.
(1994) divide the U.S. market into the 50% largest/smallest corporations instead

Graph 1 shows the yearly average percentage decile portfolio
returns for years 1–3 and annualized for the entire 3-year period
representatively for a one-dimensional value strategy based on dividend yield (DY).25 In addition to the results obtained earlier it can
be seen that returns rise considerably moving from glamour (1) to
value (10) deciles. It also can be observed that most of the value
difference (steeper curve) is captured in year 1 though differences
are still positive in years 2 and 3.

3.1. (b) Performance evaluation: two-dimensional value
strategies
Table 2 presents the average yearly percentage quantile returns
for two-dimensional value strategies (including our Consistent
Earner and Recognized Value Strategies) each classified into nine
groups of stocks by independently sorting in ascending/descending
order into three arrays ((1) bottom 30%, (2) middle 40%, and (3) top
30%) each of two variables. The sorts are 12 pairs of variables: DY
and P/E, DY and P/B, P/E and P/B, RoE and DY, RoE and P/E, RoE
and P/B, DY and Levy27, DY and MO3m, P/E and Levy27, P/E and
MO3m, P/B and Levy27, P/B and MO3m. Depending on the two variables being used for classification, the value portfolio either refers
to the portfolio containing stocks ranked in the bottom group (1)
on both variables from among P/E, P/B (sorted in ascending order),
or else the portfolio in the bottom group on one of those variables
or/and in the bottom group (1) on reversely sorted dividend yield
(DY), capital return (RoE), Levy27, and MO3m. The glamour portfolio contains stocks with precisely the opposite set of rankings.
Portfolios reformation occurs yearly at the beginning of July during
the period from 1994/95 to 2008/09. In addition, returns in year 2
and year 3 (assuming no reformation after year 1), the annualized
3-year return, the compounded 3-year return and the average 3year return are indicated. The right-most column contains the value
premium based on the performance difference between groups 1/1
and 3/3.

and likewise conclude (after eliminating the size-effect) that results still hold when
analyzing only large corporations.
25
For decile performance graphs of different one and two-dimensional ratios refer
to the appendix section. Outcomes are comparable.

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

531

Table 2
Yearly average quantile returns for portfolio strategies based on two-dimensional classifications by various value measures.
Return overview: Yearly

Value premium

Glamour
3/3
DY&P/E
DY&P/B
P/E&P/B
DY&RoE
P/E&RoE
P/B&RoE
DY&Levy27
DY&M03m
P/E&Levy27
P/E&M0m3
P/B&Levy27
P/B&M03m

Value
3/2

Multi value
6.50%
9.79%
8.91%
5.89%
6.77%
5.21%
Consistent
4.96%
8.68%
2.95%
12.18%
−3.29%
5.44%
Recognized value
2.42%
6.69%
2.51%
8.03%
−0.50%
8.25%
0.84%
8.25%
1.81%
5.29%
1.50%
7.32%

2/3

3/1

2/2

1/3

2/1

1/2

1/1

Total

1/1−3/3

8.33%
10.74%
10.09%

13.20%
8.00%
5.64%

10.81%
9.84%
10.60%

3.22%
10.64%
12.39%

10.37%
10.62%
12.21%

12.97%
14.08%
11.18%

12.38%
11.85%
10.98%

9.88%
9.89%
9.52%

5.89%
2.94%
4.20%

9.64%
7.75%
6.57%

10.62%
12.22%
9.28%

9.96%
10.85%
10.37%

11.10%
10.60%
9.73%

11.21%
10.72%
10.83%

12.12%
9.25%
11.59%

11.39%
11.46%
11.50%

9.89%
9.50%
9.19%

6.43%
8.51%
14.78%

5.85%
7.50%
5.92%
4.32%
6.40%
6.75%

13.12%
12.24%
7.16%
8.27%
8.96%
9.26%

8.91%
9.48%
8.96%
10.45%
8.86%
7.79%

8.19%
9.57%
10.39%
11.78%
13.43%
14.72%

14.79%
12.83%
11.39%
10.66%
10.88%
11.23%

11.72%
10.67%
15.59%
14.57%
13.99%
13.63%

18.86%
18.54%
16.54%
14.97%
12.88%
13.71%

9.79%
9.78%
9.33%
9.35%
9.22%
9.20%

16.44%
16.03%
17.05%
14.14%
11.08%
12.21%

Presents the yearly average percentage quantile returns for two-dimensional value strategies classified into nine groups of stocks by independently sorting in ascending/descending order into three arrays ((1) bottom 30%, (2) middle 40%, and (1) top 30%) based on each of two variables. The sorts are 12 pairs of variables: DY and P/E, DY
and P/B, P/E and P/B, RoE and DY, RoE and P/E, RoE and P/B, DY and Levy27, DY and MO3m, P/E and Levy27, P/E and MO3m, P/B and Levy27, P/B and MO3m. Depending on the
two variables being used for classification, the value portfolio either refers to the portfolio containing stocks ranked in the bottom group (1) on both variables from among
P/E, P/B (sorted in ascending order), or else the portfolio in the bottom group on one of those variables or/and in the bottom group (1) on reversely sorted dividend yield
(DY), capital return (RoE), Levy27, and MO3m. The glamour portfolio contains stocks with precisely the opposite set of rankings. Portfolios reformation occurs yearly at the
beginning of July during the period from 1994/95 to 2008/09. The right-most column contains the value premium based on the performance difference between group 1/1
and 3/3.

For two-dimensional value strategies based on two value variables (Multi Value Strategy) the yearly average return differences
(quantile 1/1 minus quantile 3/3) presented above fall in a range
between 2.94% and 5.89% depending on the variable combination
chosen. These strategies do not improve investment performance
compared to simple value strategies. Furthermore statistical significance is reduced26 (in fact, investment results get worse and are
statistically insignificant).27
Strategies based on combinations of value and capital return
variables28 (Consistent Earner Strategy) seem to result in investment returns comparable to single variable value strategies ranging
from 6.43% to 14.78%. Statistical significance however improves.29
The Consistent Earner Strategy mimics investment styles of wellknow investors like Warren Buffett or Joel Greenblatt who further
developed the value investing concept by focusing on “finding an
outstanding company at a sensible price” or buying “cheap and
good companies with competitive advantages indicated by a high
return on capital” rather than generic companies at a bargain price
as originally promoted by Graham and Dodd.
Strategies based on combinations of value and momentum
variables (Recognized Value Strategy) do improve both investment performance and significance compared to single variable
value strategies. Investment returns are in a range from 11.08%
to 17.05% annually. The Recognized Value Strategy is based on the
stock momentum life cycle hypothesis30 stating that stocks move
alternately through periods of relative “glamour” and “neglect”
attempting to reconcile intermediate horizon momentum and long
horizon-reversals of behavioral finance theories.

26

Refer to Table 3, page 14 for a statistical result summary.
An explanation may be that selection based on mixed value criteria does reduce
the variable’s indication for value because of accounting features. For instance low
P/E’s are a value indicator for pharmaceutical companies, low P/B’s are not, since
these companies have high amounts of intangible assets increasing this ratio artificially.
28
e.g. Return on Equity.
29
Refer to Table 3, page 14 for a statistical result summary.
30
Lee & Swaminathan (2000). Price momentum & trading value. Journal of Finance.
27

4. Risk evaluation
Two competing theories have been proposed to explain why
various investment strategies have produced higher returns in
the past. The capital asset pricing model (CAPM) relating risk and
expected return is grounded on the theory that rational investors
demand higher returns for higher risks. Serving as a common model
for the pricing of risky securities in the financial industry, it takes
into account an asset’s sensitivity to non-diversifiable risk (also
known as systematic risk or market risk), often referred to by the
measure beta (␤), as well as the expected return of the market
and the expected return of a theoretical risk-free asset. In contrast,
behavioral finance theories recognize a psychological element in
financial decision-making, thus challenging traditional models that
assume investors will always weigh risk/return factors rationally
and act without bias.31 For example, the human tendency to avoid
admitting error, called “fear of regret” by psychologists, can cause
an investor to hold a losing stock too long or sell a winner too soon.
Similarly, investment choices are influenced positively or negatively by attitudes toward wealth, by investor attention, mimicry
(herding instinct), etc. The premise of behavioral finance is that
taking psychological factors into account can enhance the effectiveness of investment strategies and explain the success of contrarian
and value strategies (described as anomalies or inefficiencies in
standard financial literature).
In this section it will be examined whether superior returns by
value portfolios constructed based on one- and two-dimensional
variables (Multiple Value, Consistent Earner, Recognized Value
strategies) formed using the EuroStoxx benchmark as the sample index (EuroStoxx Value Anomaly) can be explained by a higher
exposure to systematic risk. In the subsequent analysis we widely
follow the methodology used by Lakonishok et al. (1994).
Value stocks would be fundamentally riskier than glamour
stocks if they underperform glamour stocks in periods of severe
market declines in which the marginal utility of wealth is high,

31

Kahneman and Tversky (1979).

532

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

Graph 2. Yearly return differences (value minus glamour): EuroStoxx. Graph 2 shows the yearly percentage portfolio return differences (value quantile (1/1) minus glamour
quantile (3/3)) based on the EuroStoxx index for a two-dimensional value classification scheme based on DY (t0) and P/E (t0). In addition, years in which the European market
index (EuroStoxx) declined are marked by “N”, years in which it rose are indicated by a “+”. Yearly data in the 1994/95–2008/09 period refers to the 12 months between the
beginning of July and the end of June the following year. Portfolio reformation occurs yearly.

making value stocks unattractive to risk-averse investors. To begin,
we look at the consistency of performance of each selected strategy
over time and then we ask how each performs in different market
environments. In addition, we assess some traditional measures of
risk, such as beta and the standard deviation of returns, to compare
value and glamour strategies.32
Graph 2 shows the year-by-year performance differences (value
minus glamour) of a two-dimensional value strategy based on DY
and P/E for the EuroStoxx index over the period from 1995/95 to
2008/09 (July 1, 1994 and June 30, 2009).33 The strategy has quite
consistently generated a positive value difference. Using a 1-year
horizon, value outperformed glamour in 11 out of 15 years using a
strategy based on DY& P/E.
While the number of years (N = 15) is too small to draw significant conclusions a more detailed examination of quarterly results
(N = 60) still reveals a comparable – while slightly less spectacular
– picture.
Table 3 presents the quarter-by-quarter performance differences (value minus glamour) of one- and two-dimensional value
strategies for the EuroStoxx index over the period from July 1, 1994
to June 30, 2009. The quarterly average return differences for each
strategy are reported at the bottom of each column along with tstatistic for the test of the hypothesis that the difference in returns
between value and glamour is equal to zero. The corresponding pvalue, the standard deviation (quarterly and annualized) and the
Sharp Ratio34 are also presented at the bottom lines.
Over a 1-year time horizon, one-dimensional value strategies
based on DY, P/B, P/E generated negative performance differences
(value minus glamour) in only 21, 26, and 18 instances respec-

32
Common portfolio measures are also indicated, however only to provide further
useful information.
33
Refer to the appendix section for a complete graphical overview of value difference evolutions based on all variable combinations.
34
The Sharp Ratio (SR) is a risk-adjusted measure developed by William F. Sharpe,
calculated using standard deviation and excess return to determine reward per unit
of risk. The higher the Sharpe ratio, the better a portfolio’s historical risk-adjusted
performance.

tively (35.0%, 43.3%, 30.0%). Results for single capital return and
momentum variables are comparable.35
Multi Value Strategy: Two-dimensional value strategies based
on simple value variable combinations (e.g. DY & P/E, DY & P/B, P/E
& P/B) generated negative performance differences over a 1-year
time horizon in 22, 22, and 23 instances respectively (36.7%, 36.7%,
38.3%). Performance differences for these value variable combinations however are not statistically significant.
Consistent Earner Strategy: Two-dimensional value strategies
based on simple value and capital return variable combinations
(e.g. RoE & DY, RoE & P/E, RoE & P/B) generated negative performance differences over a 1-year time horizon in 23, 20, and 20
instances respectively (38.3%, 33.3%, 33.3%). Statistical significance
increases compared to single value variables.
Recognized Value Strategy: Two-dimensional value strategies
based on simple value and momentum variable combinations (e.g.
DY & Levy27, DY & MO3m, P/E & Levy27, P/E & MO3m, P/B & Levy27,
P/B & MO3m) generated negative performance differences over a
1-year time horizon in 21, 19, 19, 25, 18, and 18 instances respectively (35.0%, 31.7%, 31.7%, 41.7%, 30.0%, 30.0%). Return differences
and statistical significance both increase compared to single value
variables.
In most cases presented above, the magnitude of underperformance of value versus glamour was mostly small relative to the
mean outperformance, and return differences were negative during market declines in even fewer instances. Thus we can conclude
that downside risk is relatively low.
We proceed and examine the performance of one- and twodimensional strategies in extreme down markets on a monthly
basis thereby comparing the performance difference (value minus
glamour) in the worst months for the stock market as a whole.
Table 4, Panels A–D, lists the performance of one- and twodimensional strategies based on DY and on combinations of DY

35
Note that value differences for single value variables are statistically significant
at the 5.0% confidence interval (exception: P/B). Results for single variables based
on capital return and momentum ratios are generally not statistically significant.

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

533

Table 3
Quarterly average portfolio return differences (value minus glamour) based on various value portfolio strategies for one- and two-dimensional classification schemes
(EuroStoxx).

Q3 94
Q4 94
Q1 95
Q2 95
Q3 95
Q4 95
Q1 96
Q2 96
Q3 96
Q4 96
Q1 97
Q2 97
Q3 97
Q4 97
Q1 98
Q2 98
Q3 98
Q4 98
Q1 99
Q2 99
Q3 99
Q4 99
Q1 00
Q2 00
Q3 00
Q4 00
Q1 01
Q2 01
Q3 01
Q4 01
Q1 02
Q2 02
Q3 02
Q4 02
Q1 03
Q2 03
Q3 03
Q4 03
Q1 04
Q2 04
Q3 04
Q4 04
Q1 05
Q2 05
Q3 05
Q4 05
Q1 06
Q2 06
Q3 06
Q4 06
Q1 07
Q2 07
Q3 07
Q4 07
Q1 08
Q2 08
Q3 08
Q4 08
Q1 09
Q2 09

Value premiums 1/1−3/3

DY (tO)

PB (tO)

PE (tO)

RoE (tO)

Levy27

M03m

DY&PE

DY&PB

PE&PB

+
+
N
+
+
+
+
+
+
+
+
+
+
N
+
+
N
+
+
+
N
+
+
N
N
N
N
+
N
+
N
N
N
+
N
+
N
+
+
+
N
+
+
+
+
+
+
N
+
+
+
+
N
+
N
N
N
N
N
+

−4.22%
3.11%
−8.86%
10.63%
−6.15%
11.56%
−1.65%
3.05%
0.32%
0.25%
−2.88%
1.70%
2.53%
0.16%
10.92%
−14.15%
1.69%
−8.11%
14.73%
8.14%
−7.09%
−18.40%
−6.69%
7.47%
7.88%
27.42%
17.79%
7.12%
8.88%
−1.28%
4.19%
11.89%
8.13%
2.11%
10.57%
−5.13%
5.66%
2.52%
5.68%
−4.46%
5.29%
7.02%
0.48%
2.74%
−1.08%
1.50%
−1.32%
3.21%
3.44%
−0.56%
−0.14%
0.43%
−0.77%
0.18%
−1.04%
2.15%
5.35%
−1.51%
−7.37%
6.64%

−7.40%
0.09%
−12.32%
2.42%
−10.58%
−5.01%
−16.11%
−0.58%
−3.58%
−6.65%
35.86%
3.30%
16.38%
−0.56%
13.30%
−8.86%
−11.30%
−4.27%
5.33%
1.85%
0.78%
−32.52%
−9.90%
10.72%
4.79%
36.22%
26.65%
3.30%
8.01%
−9.22%
3.60%
13.80%
0.67%
7.56%
2.92%
−0.45%
12.90%
−1.14%
−3.40%
3.94%
−0.38%
3.90%
7.78%
0.63%
6.74%
1.49%
10.41%
−1.51%
6.36%
2.57%
3.32%
0.66%
−5.95%
−6.67%
−2.75%
−4.71%
1.44%
−10.37%
−15.04%
9.53%

5.40%
4.70%
−3.63%
9.94%
4.00%
9.23%
3.25%
4.94%
0.05%
−2.72%
8.70%
10.96%
1.72%
−1.53%
6.03%
−4.04%
−8.97%
−7.42%
12.45%
6.72%
−10.13%
−17.44%
−11.61%
5.12%
6.92%
25.93%
25.98%
8.23%
7.58%
7.72%
14.64%
16.84%
2.57%
−3.79%
−4.06%
3.36%
10.86%
−4.43%
3.98%
2.27%
6.94%
1.98%
−0.10%
7.11%
3.90%
4.71%
4.65%
4.40%
4.22%
2.46%
2.30%
9.94%
−3.40%
−7.57%
4.37%
−3.41%
0.86%
−4.79%
−6.27%
10.08%

6.25%
5.61%
0.67%
5.98%
8.90%
9.43%
12.70%
5.64%
8.38%
−0.96%
−29.91%
11.35%
−8.90%
3.68%
−5.56%
4.29%
0.98%
−4.71%
−0.04%
−0.31%
−3.00%
13.48%
2.91%
−2.67%
4.58%
3.85%
−5.68%
3.83%
9.14%
−2.26%
9.72%
8.28%
5.11%
−10.72%
−7.85%
−7.21%
−4.89%
−1.17%
−1.18%
5.25%
12.77%
1.68%
−1.24%
6.56%
−4.70%
−5.33%
−7.29%
6.45%
−1.57%
−1.07%
−3.00%
2.28%
3.74%
8.53%
2.53%
2.47%
4.73%
15.61%
9.71%
−8.18%

1.50%
−2.88%
1.07%
0.08%
5.19%
3.45%
12.16%
6.58%
0.35%
8.62%
−6.26%
2.77%
10.95%
−4.15%
12.35%
2.74%
5.92%
16.19%
3.22%
0.53%
41.98%
1.31%
−11.49%
−11.11%
3.75%
−6.57%
−15.84%
2.19%
18.30%
−33.65%
5.55%
21.09%
28.21%
−16.23%
0.21%
−18.58%
0.94%
8.49%
6.17%
−0.30%
10.47%
−0.62%
−1.47%
4.89%
3.28%
−3.50%
7.95%
−0.91%
2.71%
4.49%
0.92%
2.67%
5.11%
7.86%
0.39%
13.28%
−12.80%
−0.11%
14.24%
−5.51%

1.53%
−2.39%
1.15%
1.15%
5.23%
6.88%
−2.53%
−2.02%
2.20%
1.45%
−5.93%
6.43%
3.20%
−2.78%
8.57%
−4.12%
5.77%
2.28%
6.08%
8.46%
45.66%
−1.89%
−16.24%
−13.66%
7.95%
30.22%
7.21%
9.59%
15.42%
−20.98%
−4.77%
14.46%
34.00%
−20.66%
2.23%
−22.71%
−1.29%
4.10%
1.98%
−3.08%
10.02%
2.66%
−3.00%
2.11%
8.04%
2.44%
3.30%
−4.23%
−0.29%
0.01%
−4.88%
4.88%
5.78%
9.18%
−5.22%
16.21%
−12.29%
7.00%
7.47%
−9.73%

−1.75%
−0.02%
−8.38%
8.77%
−2.52%
7.53%
−3.03%
0.50%
0.22%
0.58%
1.90%
2.08%
4.04%
2.21%
3.35%
−4.53%
−8.39%
−12.20%
11.52%
3.11%
−5.15%
−15.85%
−4.92%
5.25%
5.51%
19.06%
15.63%
4.14%
6.47%
2.40%
6.18%
11.29%
4.17%
−1.44%
−0.87%
3.44%
3.84%
−0.72%
4.60%
2.52%
6.09%
3.61%
1.11%
4.09%
−0.88%
4.53%
1.49%
2.76%
1.45%
−0.32%
0.34%
1.75%
−1.47%
−1.84%
−1.13%
−3.68%
2.37%
−5.91%
−9.16%
10.98%

−7.33%
1.82%
−5.85%
2.26%
−5.60%
8.33%
−12.44%
−4.85%
−1.76%
−1.30%
2.62%
1.08%
10.44%
5.21%
8.38%
−10.53%
−8.30%
−12.03%
9.34%
1.85%
0.61%
−20.65%
−3.01%
8.20%
1.39%
20.60%
15.13%
2.70%
5.20%
−2.96%
4.15%
9.84%
7.37%
0.28%
2.77%
−0.17%
3.17%
−1.17%
3.46%
1.09%
2.70%
4.73%
2.17%
0.98%
0.04%
3.31%
5.29%
0.60%
2.68%
−2.44%
1.89%
0.44%
−4.51%
−5.54%
−1.95%
−9.06%
4.21%
−7.19%
−13.18%
9.38%

−5.94%
2.49%
−6.07%
0.75%
0.11%
3.78%
−0.21%
1.94%
−4.39%
−4.01%
6.69%
1.16%
11.45%
5.03%
5.75%
−6.93%
−13.76%
−10.73%
8.59%
1.82%
2.75%
−30.90%
−12.15%
10.71%
3.03%
25.50%
18.92%
4.23%
4.30%
0.21%
6.34%
12.55%
2.68%
−5.66%
−1.32%
5.66%
4.88%
−0.31%
3.14%
3.70%
4.05%
4.30%
2.87%
2.87%
1.27%
7.84%
5.09%
−0.82%
1.13%
−0.70%
−1.11%
3.14%
−7.87%
−8.20%
−0.04%
−5.58%
−0.52%
−8.23%
−10.56%
12.55%

Mean
t-statistic
p-value
st. dev.
Number
st. dev. (ann.)
SR

2.20%
2.2875
0.0258
7.43%
60
14.87%
0.59

1.30%
0.9034
0.3700
11.15%
60
22.30%
0.23

3.21%,
3.1401
0.0026
7.92%
60
15.85%
0.81

1.63%
1.6815
0.0979
7.50%
60
15.00%
0.43

2.64%
1.8406
0.0707
11.09%
60
22.19%
0.48

2.46%
1.6572
0.1028
11.50%
60
23.00%
0.43

1.45%
1.8494
0.0694
6.05%
60
12.11%
0.48

0.56%
0.6185
0.5386
7.07%
60
14.15%
0.16

0.95%
0.8990
0.3723
8.22%
60
16.45%
0.23

534

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

Table 3 (Continued)
Value
premiums
1/1−3/3
Q3 94
Q4 94
Q1 95
Q2 95
Q3 95
Q4 95
Q1 96
Q2 96
Q3 96
Q4 96
Q1 97
Q2 97
Q3 97
Q4 97
Q1 98
Q2 98
Q3 98
Q4 98
Q1 99
Q2 99
Q3 99
Q4 99
Q1 00
Q2 00
Q3 00
Q4 00
Q1 01
Q2 01
Q3 01
Q4 01
Q1 02
Q2 02
Q3 02
Q4 02
Q1 03
Q2 03
Q3 03
Q4 03
Q1 04
Q2 04
Q3 04
Q4 04
Q1 05
Q2 05
Q3 05
Q4 05
Q1 06
Q2 06
Q3 06
Q4 06
Q1 07
Q2 07
Q3 07
Q4 07
Q1 08
Q2 08
Q3 08
Q4 08
Q1 09
Q2 09

+
+
N
+
+
+
+
+
+
+
+
+
+
N
+
+
N
+
+
+
N
+
+
N
N
N
N
+
N
+
N
N
N
+
N
+
N
+
+
+
N
+
+
+
+
+
+
N
+
+
+
+
N
+
N
N
N
N
N
+
Mean
t-statistic
p-value
st. dev.
Number
st. dev. (ann.)
SR

RoE (tO) &
DY (tO)
4.77%
4.64%
4.38%
5.49%
2.84%
13.72%
−0.02%
6.98%
5.73%
8.00%
−6.45%
7.97%
−4.90%
0.31%
−5.97%
−0.58%
−6.80%
−0.93%
3.62%
0.51%
−4.03%
−16.14%
−9.74%
5.50%
8.90%
12.91%
6.27%
7.35%
6.21%
2.93%
6.32%
9.53%
6.66%
−1.45%
−0.65%
0.98%
−4.86%
−3.46%
6.52%
−0.65%
5.56%
3.32%
−2.82%
4.60%
−2.99%
−1.35%
−4.39%
4.36%
−4.73%
1.25%
−2.18%
1.89%
0.09%
−0.55%
0.67%
1.47%
−3.07%
8.47%
4.46%
0.22%
1.61%
2.2553
0.0278
5.53%
60
11.07%
0.58

RoE (tO) &
PB(tO)
12.97%
11.27%
−2.93%
0.56%
9.09%
16.95%
11.06%
5.40%
−8.52%
−7.37%
1.23%
6.63%
−8.55%
7.40%
−3.66%
8.48%
−24.04%
−16.49%
−3.37%
7.06%
13.30%
−31.88%
−3.10%
11.11%
19.21%
47.52%
18.49%
22.31%
17.50%
−6.83%
10.69%
32.12%
−8.93%
−7.21%
8.06%
3.16%
2.89%
−22.91%
6.34%
3.40%
21.48%
−14.47%
19.78%
13.84%
1.44%
−19.27%
14.44%
12.98%
14.61%
6.43%
−24.32%
14.13%
2.21%
−5.82%
11.04%
1.25%
−16.21%
−4.80%
10.97%
7.39%
3.76%
2.0388
0.0460
14.28%
60
28.56%
0.53

RoE (tO) &
PE (tO)
3.22%
2.78%
−0.82%
2.81%
8.76%
8.03%
4.02%
2.82%
3.46%
1.80%
1.48%
8.32%
−3.98%
−0.62%
−3.51%
2.76%
−11.70%
−1.52%
6.09%
2.84%
−4.44%
−5.22%
−1.00%
−3.18%
8.02%
12.13%
9.16%
5.27%
5.71%
5.29%
9.76%
13.81%
6.18%
−6.51%
−2.02%
1.09%
2.36%
−3.63%
−0.14%
2.55%
6.67%
1.63%
0.91%
5.81%
−1.03%
0.94%
−0.18%
3.39%
−0.88%
0.69%
0.40%
8.24%
3.15%
−2.83%
0.95%
7.72%
−4.37%
−4.22%
1.96%
12.03%
2.22%
3.4445
0.0011
4.99%
60
9.99%
0.89

DY (tO) &
Levy27
−11.85%
0.48%
−10.59%
12.70%
−2.33%
21.29%
−7.43%
5.62%
−2.91%
11.17%
−2.87%
−4.75%
4.47%
−0.38%
26.67%
−9.28%
0.66%
14.37%
18.95%
−0.79%
1.64%
3.96%
−1.30%
−9.76%
6.54%
24.74%
14.88%
9.53%
16.31%
−18.90%
9.53%
25.04%
26.31%
−9.27%
9.98%
−12.87%
−2.17%
−0.27%
2.32%
3.64%
10.01%
6.85%
2.88%
6.96%
2.79%
−5.46%
5.24%
4.31%
0.68%
7.26%
7.63%
6.04%
1.34%
2.94%
7.17%
−5.49%
−12.87%
4.37%
10.00%
−9.97%
3.60%
2.7318
0.0083
10.20%
60
20.39%
0.71

DY (tO) &
M03m
−0.69%
7.50%
2.73%
10.10%
−1.65%
14.61%
−2.66%
4.84%
7.28%
13.35%
−7.85%
13.05%
6.66%
0.20%
16.22%
−13.24%
7.69%
−3.92%
14.72%
1.15%
0.57%
5.77%
−11.57%
−9.79%
2.68%
27.31%
20.30%
8.96%
14.99%
−22.13%
5.67%
18.76%
29.98%
−13.45%
9.51%
−13.07%
−0.19%
9.85%
−1.26%
−1.75%
6.66%
5.28%
4.35%
2.26%
0.27%
−4.76%
5.41%
2.65%
0.54%
0.47%
1.75%
2.09%
−0.31%
−1.87%
−0.76%
8.22%
1.99%
13.97%
8.89%
−21.10%
3.45%
2.6653
0.0099
10.04%
60
20.07%
0.69

PB (tO) &
Levy27
−11.99%
−1.64%
−18.27%
10.56%
1.98%
16.99%
-10.71%
10.20%
−9.71%
5.78%
0.89%
−26.72%
18.03%
−2.98%
29.21%
−5.83%
−15.47%
21.38%
1.16%
0.20%
5.60%
0.66%
9.99%
0.33%
11.17%
35.09%
10.56%
−2.40%
13.16%
−18.33%
4.74%
20.03%
18.30%
−10.42%
7.04%
−8.67%
7.15%
1.11%
0.78%
0.61%
13.13%
6.07%
3.06%
5.55%
4.89%
1.97%
9.91%
−6.46%
3.63%
8.77%
4.86%
5.61%
2.45%
−5.82%
−2.01%
−7.93%
−7.27%
−12.61%
3.91%
1.01%
2.54%
1.7398
0.0871
11.30%
60
22.60%
0.45

PB (tO) &
M03m
−4.82%
−3.51%
−7.96%
0.26%
3.26%
4.53%
−9.53%
4.58%
−3.00%
−7.72%
52.23%
7.75%
16.98%
−6.86%
26.69%
−5.80%
−12.76%
4.73%
−0.80%
1.24%
7.77%
4.61%
−11.53%
−4.72%
2.56%
35.95%
18.68%
−0.26%
9.82%
−13.59%
3.60%
17.53%
26.14%
−14.57%
4.67%
−12.45%
9.70%
4.96%
−2.40%
−2.58%
8.20%
3.90%
6.13%
4.46%
4.02%
3.20%
5.16%
−0.92%
3.59%
5.08%
−4.50%
0.61%
1.83%
−3.01%
−2.51%
7.82%
−12.16%
−15.35%
2.74%
−2.40%
2.65%
1.7271
0.0894
11.91%
60
23.81%
0.45

PE (tO) &
Levy27
−4.44%
−0.39%
−6.84%
1.67%
5.66%
16.35%
−3.47%
7.76%
1.62%
7.08%
−4.65%
−3.70%
11.60%
−8.67%
25.44%
1.93%
−13.22%
17.45%
5.37%
−9.09%
0.96%
1.88%
−2.91%
−3.28%
11.86%
12.51%
16.07%
8.23%
13.83%
−13.28%
10.03%
22.49%
17.09%
−13.54%
5.76%
−7.31%
8.72%
1.34%
8.01%
8.29%
9.87%
4.72%
5.88%
11.07%
6.20%
7.48%
12.34%
−2.14%
−0.50%
10.27%
8.25%
6.33%
4.78%
−7.10%
2.98%
2.68%
−20.98%
3.08%
8.09%
5.46%
3.88%
3.3196
0.0015
9.06%
60
18.13%
0.86

PE (t0)
& M03m
7.83%
−7.93%
9.71%
0.26%
9.02%
0.64%
9.60%
2.86%
6.10%
7.39%
−2.50%
13.83%
6.40%
−4.18%
20.01%
−4.09%
−9.19%
−9.10%
8.96%
−1.55%
−5.22%
7.20%
−7.79%
−5.40%
1.40%
28.28%
18.72%
6.50%
9.39%
−7.57%
9.04%
18.99%
20.57%
−14.92%
5.18%
−10.79%
13.43%
0.31%
2.06%
1.84%
7.56%
1.91%
1.41%
6.53%
−0.45%
4.80%
5.01%
0.89%
−3.05%
5.94%
2.26%
2.03%
0.43%
−8.27%
0.82%
12.75%
−14.62%
9.23%
7.84%
−3.90%
3.24%
2.8646
0.0058
8.76%
60
17.52%
0.74

Quarterly percentage portfolio return differences (value minus glamour) based on portfolios formed from EuroStoxx constituents using different one- and two-dimensional
value classification schemes. Portfolios reformation occurs yearly at the beginning of July during the period from 1994/95 to 2008/09. In addition, years in which the market
indexes declined are marked by “N”, years in which it rose are indicated by a “+”. The t-statistic for the test of the hypothesis that the differences in returns between value
and glamour are equal to zero, the corresponding p-value and the quarterly standard deviation of returns are indicated in the bottom lines. The return differences’ standard
deviation (annualized) and the Sharp Ratio (SR) are indicated for convenience as further useful information.

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

535

Table 4
Average monthly returns in different market environments: worst 25 down months, next 50 down months, positive 80 up months, best 25 up months.
Panel A: Dividend yield

W25
N53
P80
B25

1

2

3

4

5

6

7

8

9

10

Eq.-w.

10−1

t-statistic

p-Value

−0.0954
−0.0247
0.0209
0.0874

−0.0891
−0.0202
0.0278
0.0803

−0.0939
−0.0198
0.0251
0.0840

−0.0851
−0.0150
0.0254
0.0790

−0.0852
−0.0200
0.0247
0.0813

−0.0848
−0.0188
0.0256
0.0788

−0.0793
−0.0119
0.0261
0.0682

−0.0842
−0.0108
0.0271
0.0766

−0.0844
−0.0159
0.0240
0.0716

−0.0720
−0.0127
0.0279
0.0721

−0.0852
−0.0168
0.0255
0.0778

0.0234
0.0120
0.0070
−0.0153

3.0424
2.0129
2.3613
−1.5309

0.0056
0.0506
0.0205
0.1389

Panel B: DY (t0) & RoE (t0)

W25
N50
P80
B25

3/3

3/2

2/3

3/1

2/2

1/3

2/1

1/2

1/1

Eq.-w.

Index

1/1−3/3

t-statistic

p-Value

−0.0946
−0.0268
0.0194
0.0972

−0.0900
−0.0209
0.0269
0.0791

−0.0937
−0.0202
0.0253
0.0909

−0.0921
−0.0203
0.0287
0.0831

−0.0822
−0.0141
0.0262
0.0695

−0.0784
−0.0164
0.0237
0.0815

−0.0773
−0.0164
0.0255
0.0779

−0.0811
−0.0132
0.0288
0.0691

−0.0784
−0.0105
0.0261
0.0687

−0.0855
−0.0173
0.0257
0.0786

0.0000
0.0000
0.0000
0.0000

0.0162
0.0163
0.0067
−0.0285

2.6067
3.3483
2.4413
−3.6663

0.0155
0.0017
0.0167
0.0012

Panel C: DY (t0) & Levy27

W25
N50
P80
B25

3/3

3/2

2/3

3/1

2/2

1/3

2/1

1/2

1/1

Eq.-w.

1/1−3/3

t-statistic

p-value

−0.1071
−0.0309
0.0209
0.1026

−0.0872
−0.0184
0.0224
0.0782

−0.1021
−0.0182
0.0225
0.0887

−0.0837
−0.0155
0.0293
0.0775

−0.0804
−0.0177
0.0254
0.0729

−0.0959
−0.0180
0.0265
0.0817

−0.0743
−0.0109
0.0285
0.0723

−0.0727
−0.0117
0.0256
0.0677

−0.0692
−0.0038
0.0290
0.0769

−0.0853
−0.0164
0.0257
0.0781

0.0379
0.0270
0.0080
−0.0257

2.5519
3.3124
2.1662
−1.7922

0.0175
0.0019
0.0331
0.0857

Panel D: DY (t0) & MO3m

W25
N50
P80
B25

3/3

3/2

2/3

3/1

2/2

1/3

2/1

1/2

1/1

Eq.-w.

1/1−3/3

t-statistic

p-Value

−0.1112
−0.0300
0.0221
0.1042

−0.0895
−0.0193
0.0261
0.0785

−0.1005
−0.0182
0.0257
0.0874

−0.0773
−0.0157
0.0268
0.0778

−0.0833
−0.0142
0.0256
0.0730

−0.0972
−0.0155
0.0276
0.0843

−0.0734
−0.0151
0.0274
0.0746

−0.0758
−0.0113
0.0257
0.0657

−0.0581
−0.0072
0.0289
0.0719

−0.0853
−0.0161
0.0261
0.0779

0.0531
0.0229
0.0068
−0.0323

4.0470
3.5922
1.7503
−1.9776

0.0005
0.0008
0.0837
0.0596

Panels A–D present the average percentage decile portfolio returns, the average total performance, the return differences (value minus glamour) on a monthly basis
representatively for one-dimensional value strategies based on DY; for two-dimensional strategies based on DY and RoE, DY and Levy27, DY and MO3m. The performance
of our portfolios are divided into four states of general market environments; the 25 worst stock return months in the sample based on the equally weighted index, the
remaining 50 negative months other than the 25 worst, the 80 positive up months other than the 25 best, and the 25 best up months in the sample. The average difference
in returns between value and glamour for each state is also reported along with t-statistics for the test that the differences of returns are equal to zero and its corresponding
p-value.

and RoE, Levy27 and MO3m during four states of the global market; the 25 worst stock return months36 in the sample based on the
equally weighted index, the remaining 50 negative months other
than the 25 worst, the 80 positive months other than the 25 best,
and the 25 best months in the sample. The average difference in
returns between the value and glamour portfolio for each state is
also reported along with t-statistics for the test that the difference
of returns is equal to zero and its corresponding p-value.37
The results in this table are ambiguous (due to low t-statistics).
While for most classification schemes, the value portfolio outperformed glamour in the market’s worst 25 months and in the next
50 negative months, results in all cases are only statistically significant for the category “Next negative 50 months”.38 For example,
using DY, the value portfolio lost an average of 7.2% of its value
in the worst 25 months, whereas glamour lost 9.5% of its value.
Similarly, the value portfolio outperformed glamour in the next
worst 50 months in which the index declined. It lost 1.5% in these
months while glamour experienced a 2.5% decline. Similar results
can be observed for other one- and two-dimensional value strategies as well (refer to the appendix section). The value strategy did
generally better when the market fell. In the 80 positive months
other than the best 25, one- and two-dimensional value strategies

outperformed glamour slightly. However, results lack statistical
significance in some cases. In the very best 25 months, one- and
two-dimensional value strategies underperformed glamour39 substantially. Return results however also lack statistical significance.
If anything, the superior performance of the value strategy is
skewed toward negative market return months rather than positive market return months. Overall, the value strategy performed
better than the glamour in all states other then extreme upward
movements. Table 4 thus indicates that the value strategy does not
expose investors to greater downside risk.
Finally, for completeness, Table 5, Panels A–D present some
more traditional risk measures for portfolios using our classification schemes. These risk measures are calculated using quarterly
return measurement intervals over the sample period. For each
of our strategies, we have 60 quarterly return observations in the
year following the first formation and hence we can compute the
standard deviation of quantile returns. We also have calculated
corresponding returns on the total return of the EuroStoxx index
(equally weighted) and can calculate a beta for each quantile portfolio.
First, the beta of the equally weighted index is lower then for
the market-capitalized index. Secondly betas for value portfolios
are similar or much smaller than glamour portfolios.40 Only for
some single value strategies are betas of the value portfolios slightly

36

We widely follow Lakonishok et al. (1994) in the structure of this analysis.
Slight differences in returns and risk measures are due to the fact that negative
parameter characteristics are not in included in this analysis. Refer also to Section
2.
38
Illustrations are presented for dividend yield (DY). For a complete list including
all variable examined refer to the appendix section. Similar results can be observed.
37

39
These results are consisted with earlier observations. For a complete list of all
variables examined refer to the appendix section.
40
Refer also to the appendix section for variable combinations other than with
dividend yield.

536

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

Table 5
Traditional risk measures: beta, standard deviation.
Panel A: Dividend yield

Beta ann.
St. dev. (ann)

1

2

3

4

5

6

7

8

9

10

Eq.-w.

1.0836
0.2098

1.0481
0.1994

1.0962
0.2050

0.9987
0.1860

1.0077
0.1870

1.0012
0.1862

0.8911
0.1671

0.9784
0.1835

0.9907
0.1852

0.9265
0.1801

0.9079
0.1796

Panel B: DY (t0) & RoE (t0)

Beta ann.
St. dev. (ann)

3/3

3/2

2/3

3/1

2/2

1/3

2/1

1/2

1/1

Eq.-w.

1.1358
0.2187

1.0360
0.1947

1.1008
0.2059

1.0642
0.2068

0.9211
0.1723

0.9837
0.1926

0.9364
0.1819

0.9544
0.1793

0.9199
0.1776

0.9122
0.1742

3/3

3/2

2/3

3/1

2/2

1/3

2/1

1/2

1/1

Eq.-w.

1.2331
0.2477

1.0090
0.1899

1.1042
0.2119

0.9885
0.1927

0.9497
0.1773

1.1074
0.2105

0.9047
0.1735

0.8805
0.1668

0.9046
0.2030

0.9093
0.1914

3/3

3/2

2/3

3/1

2/2

1/3

2/1

1/2

1/1

Eq.-w.

1.2773
0.2550

1.0326
0.1930

1.1206
0.2141

0.9496
0.1872

0.9590
0.1774

1.1341
0.2181

0.8966
0.1745

0.8920
0.1691

0.7953
0.1743

0.9088
0.1920

Panel C: DY (t0) & Levy27

Beta ann.
St. dev. (ann)

Panel D: DY (t0) & MO3m

Beta ann.
St. dev. (ann)

Panel A shows the beta with respect to the European market index EuroStoxx, the decile return standard deviations and the return value premium’s standard deviations
(annualized = ann.) for various one-dimensional value strategies (DY) based on quarterly performance data. Portfolio reformation occurs yearly at the beginning of July
during the period from 1994/95 to 2008/09. Panels B–D show the beta with respect to the European market index EuroStoxx, the quantile return standard deviations and
the return value premium’s standard deviations (annualized = ann.) for various two-dimensional value strategies (DY and RoE, DY and Levy27, DY and MO3m) based on
quarterly performance data. Portfolio reformation occurs yearly at the beginning of July during the period from 1994/95 to 2008/09.

higher than for the glamour portfolios. In addition, it seems that
strategy combinations based on dividend yield (DY) exhibit the
smallest betas in the value quantile portfolios. If anything, the
superior performance of the strategies occurs disproportionally
during “bad” performances of the stock market (refer to the previous pages: performance in different market environments). The
difference in betas of less then 0.1 can explain only a small portion
of return difference41 and surely not the roughly 5–17% difference
in returns that we find.
Additionally, Table 5, Panels A–D present the annualized standard deviations of one- and two-dimensional strategy portfolio
quantile returns based on DY. It shows that value quantile portfolios
do not generally have higher standard deviations of returns then
glamour portfolios.42 For example, using the DY, RoE classification,
the value quantile portfolio has an average standard deviation of
returns of 17.8% compared to 21.9% for the glamour quantile. Some
remarks about these results are necessary. First, because of its much
higher mean return, the value strategy’s higher standard deviation
does not necessarily translate into greater downside risk. Second,
the small differences in standard deviations of returns between
value and glamour portfolios are quite small in comparison to the
difference in average return (around 5–17% per year). A risk model
based on differences in standard deviation cannot explain the superior returns on these strategies.
5. Conclusion
Value investing is an investment paradigm that derives from
the ideas on investment and speculation that Ben Graham & David
Dodd began teaching at Columbia Business School in 1928. Since

41
This conclusion is not valid for value strategies based on P/B where betas for the
value quantile portfolios seem to be substantially higher than for glamour portfolios.
42
It seems however that glamour and value strategies generally exhibit somewhat higher standard deviations than other portfolio quantiles or the total equally
weighted index.

then numerous empirical academic studies have consistently found
that value stocks outperform glamour stocks and the market as a
whole. This article extended prevailing research on existing value
anomalies. It evaluated simple value strategies for the European
stock market (compared to many other studies that test market data on a country-by-country basis) as well as sophisticated
multi-dimensional value strategies that also include capital return
variables (Consistent Earner Strategy) and momentum factors (Recognized Value Strategy).
In Section 3 (a) of this analysis it was shown that a variety of simple classification schemes sorting value and glamour stocks based
on dividend yield (DY), price-to-book ratio (P/B) and price-toearnings ratio (P/E) produced superior returns for value portfolios
compared to glamour. As market proxy for the European market
the EuroStoxx index was selected. Return differentials (premiums)
between value and glamour varied between 5.40% and 12.66% per
annum on average depending on the selection criteria chosen during the period from July 15, 1994/95 to June 30, 2008/09.
Motivated by these results we subsequently examined portfolio
strategies based on two-dimensional selection criteria in Section
3.1 (b). First simple value measures (as evaluated in Section 3
(a)) were combined. It was shown that two-dimensional value
strategies (Multiple Value) based on a combination of simple value
strategies do not further improve investment performance and statistical significance (in fact, investment returns were smaller and
statistically not significant).
Subsequently more sophisticated two-dimensional value
strategies were evaluated. The Consistent Earner Strategy including capital return variables (e.g. RoE) resulted in investment
returns similar to simple value strategies but much better than
for single capital return variables. Return differences (premiums)
fall in a range between 6.43% and 14.78%. Statistical significance
improved substantially.43 The Consistent Earner Strategy mimics
investment styles of well-know investors like Warren Buffett or

43

Refer to Table 3, page 14 to compare statistical significances.

G. Elze / The Quarterly Review of Economics and Finance 50 (2010) 527–537

Joel Greenblatt who further developed the value investing concept
by focusing on “finding an outstanding company at a sensible
price” or buying “cheap and good companies with competitive
advantages indicated by a high return on capital” rather than
generic companies at a bargain price as originally promoted by
Graham and Dodd.
With regard to strategies combining momentum and value variables (Recognized Value Strategy), both investment performance
differences (premiums) and statistical significance improved compared to simple value and/or simple momentum variables.
Investment returns fell in a range between 11.08% and 17.05% per
annum on average.44 The Recognized Value Strategy is based on the
stock momentum life cycle hypothesis45 stating that stocks move
alternately through periods of relative “glamour” and “neglect”
attempting to reconcile intermediate horizon momentum and long
horizon-reversals of behavioral finance theories.
Finally in Section 4 the question of whether strategies based
on investment criteria previously chosen are fundamentally riskier
was evaluated. Evidence could be provided that, in general, value
strategies based both on one- and two-dimensional simple value
criteria as well as “sophisticated” strategies including capital return
or momentum variables have outperformed glamour strategies
quite consistently without support for the hypothesis that value
strategies are fundamentally riskier.
References
Ball, R. (1978). Anomalies in relationships between securities’ yields and yieldsurrogates. Journal of Financial Economics, 6, 103–126.

44
Momentum criteria here included representatively Levy27 and relative strength
– 3 month (MO3m).
45
Lee & Swaminathan (2000). Price momentum & trading value. Journal of Finance.

537

Basu, S. (1977). Investment performance of common stocks in relation to their price
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of Political Economy, 81, 607–636.
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