Directory UMM :Data Elmu:jurnal:J-a:Journal Of Banking And Finance:Vol24.Issue6.2000:

Journal of Banking & Finance 24 (2000) 893±919
www.elsevier.com/locate/econbase

``The ®rst shall be last''. Size and value strategy
premia at the London Stock Exchange
Michele Bagella *, Leonardo Becchetti, Andrea Carpentieri
Dipartimento di Economia e Istituzioni, Facolt
a di Economia, Universit
a Tor Vergata, Roma,
Via di Tor Vergata snc, 00133 Roma, Italy

Abstract
The paper analyses the determinants of cross-sectional stock returns at the London
Stock Exchange in the last 26 years. It ®nds that portfolio strategies based on low values
of earning per share (EPS), market to book value (MTBV), market value (MV) and
return on equity (ROE) signi®cantly outperform the index. Do size and value (S&V)
strategy premia disappear when risk-adjusted or do they reveal gains from trading
against noise, near rational, liquidity or ``weak-hearted'' traders? We ®nd that the signi®cance of cross-sectional determinants of these strategies is not absorbed by ex post
betas. They are not riskier in terms of monthly return standard deviations, covariation
with GDP growth and their premia do not disappear when survivorship bias is taken
into account. Portfolio mean monthly returns (MMRs), regressed on several risk factors

in 3-CAPM models, con®rm that S&V strategy premia persist when risk adjusted.
Empirical results also mark the di€erence between ROE and MTBV portfolios, on the
one side, and MV and EPS portfolios, on the other. Descriptive statistics on preformation and postformation returns, average balance sheet values and preformation
standard deviations clearly show that ROE and MTBV portfolios have a common ®nancial distress factor and are then more exposed to systematic risk. Ó 2000 Elsevier
Science B.V. All rights reserved.
JEL classi®cation: G11
Keywords: Size and value strategies; CAPM models; Cross-sectional stock returns

*

Corresponding author. Tel.: +39-6-2025-361; fax: +39-6-2020-500.
E-mail address: Bagella@uniroma2.it (M. Bagella).

0378-4266/00/$ - see front matter Ó 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 4 2 6 6 ( 9 9 ) 0 0 1 1 1 - 9

894

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

One of the basic principles of statistical prediction, which is also one
of the least intuitive, is that the extremeness of predictions must be
moderated by considerations of predictability. ¼the prediction should
be regressive; that is, it should fall between class average and the value that best represents oneÕs impression of the case at hand. The lower
the predictability the closer the prediction should be to class average.
Intuitive predictions are often nonregressive: people often make extreme predictions on the basis of information whose reliability and
predictive validity are known to be low (Kahneman and Tversky,
1982).

1. Introduction
1.1. The literature on S&V strategies
The literature investigating explanatory variables of cross-sectional stock
returns has generated in the last decade a large amount of empirical papers which make use of comparable methodologies and focus most of their
attention on the US stock market. Results from this literature seem to
confute the empirical relevance of the traditional CAPM model of Sharpe
(1964), Linter (1965) and Black (1972) where b is the only signi®cant
explanatory variable of cross-sectional variations in stock returns. In
particular, past returns (De Bondt and Thaler, 1985; Jegadeesh and Titman, 1993), book to market equity (Stattman, 1980; Rosenberg et al.,
1985; Fama and French, 1992; Barber and Lyon, 1997) and size (Reinganum, 1990; Fama and French, 1992; Barber and Lyon, 1997; Campbell
et al., 1997) are also shown to have a signi®cant impact which does not

seem to be sample- or country-speci®c. Chan et al. (1991) analyse the
e€ect of market to book, size, earning yield and cash ¯ow yield on Japanese stock returns in a data set running from 1971 to 1988. They demonstrate that all these variables have a signi®cant explanatory power. Clare
et al. (1997) show that strategies based on size earn signi®cant premia on
the UK stock market between 1978 and 1993. They do not test, though,
the sensitivity of their results to risk factors di€erent from the traditional
beta. Heston et al. (1995) show that premia from size and beta-sorted
portfolios in 12 European countries cannot be explained by market risk
and exposure to the excess return of small over large stocks. Brouwer et
al. (1996), ®nd that value portfolios formed on earnings to price, cash ¯ow
to price, book to market and dividend yield outperform glamour strategies
in four European countries (France, Germany, the Netherlands and the

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

895

UK). Haugen and Baker (1996) evaluate 12 return factors in ®ve industrialised countries. 1
This branch of empirical research has recently become the battle®eld of
advocates and opponents of the Ecient Market Hypothesis (EMH). Fama
and French (1995, 1996) defend the EMH by testing an alternative three-factor

model where excess returns from size and value (S&V) strategies are explained
by the traditional excess returns on broad market portfolio, the return di€erential between two portfolios respectively containing high and low book-tomarket stocks and the return di€erential between a small ®rm and a large ®rm
portfolio. This econometric speci®cation captures the idea that average stock
returns are in¯uenced by three risk factors which are proxied by the above
described explanatory variables. This hypothesis is not rejected by data as
excess returns, corrected by the three risk factors (intercepts of the threevariable regression), are shown to be not signi®cantly di€erent from zero. A
strong criticism of Fama±French (FF) results comes from Daniel and Titman
(1997) who claim the rejection of the three-factor model, showing that return
premia on small size and high book to market ratios disappear once ®rm
characteristics are taken into account. 2
Interpretations of empirical ®ndings on S&V strategies then focus on three
main explanations. The ®rst is consistent with the Ecient Market Hypothesis
and argues that size and book to market variables proxy for multidimensional
risk factors not captured by market bs (Fama and French, 1992, 1993, 1995,
1996). The second explanation maintains that return premia on small size and
low market to book stocks are too high and that they may be partly explained
by the irrational behaviour of noise- (De Long et al., 1990), near rational(Wang, 1993), liquidity- or ``weakhearted''- traders overreacting to shocks and

1
Emerging stock markets, which until recently have been characterised by high segmentation

and relatively low correlation with global risk factors, are another interesting ®eld to test whether
S&V premia are related to a common behaviour of investors across all ®nancial markets. Results
which support the existence of these premia come from Fama and French (1998); Claessens et al.
(1995) and Rouwenhorst (1998) showing that lower liquidity is not sucient to explain the
outperformance of small versus large, and low market to book versus high market to book stocks in
these markets.
2
Studying the behaviour of preformation portfolios average returns and standard deviations
they show that small ®rms and high book to market ®rms tend to have strong covariance in returns
before they become distressed. They then argue that these ®rms do not covariate due to a common
relative distress factor, but because of their common characteristics. They also provide additional
evidence in favour of the common characteristics model showing that: (i) portfolios formed by
higher factor loading stocks, net of size and book to market factors, do not earn higher returns than
portfolios formed by lower factor loading stocks and that (ii) intercepts of the three-factor CAPM
FF estimates are positive (negative) when using low (high) factor loading portfolios.

896

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

extrapolating past stock price behaviour (Lakonishok et al., 1994, from now on
LSV). 3 The third explanation (Fama and French, 1992, 1993, 1995, 1996)
considers that premia from S&V strategies may be a spurious e€ect for at least
two reasons: (i) they may be the result of data-snooping and the relevance of
book to market and size factors may be sample-speci®c; (ii) they may be affected by a survivorship-bias given that the Compustat database gives excessive
weight to distressed ®rms that survived than to distressed ®rms that ®eld.
1.2. The aim of the paper
Which of the three above mentioned interpretations best ®ts results on returns from S&V strategies at the London Stock Exchange? Are empirical
regularities on determinants of cross-sectional variations in average stock returns country-speci®c or do they extend also to European stock markets such
as the LSE ? This paper explores the issue showing that UK stocks have similar
patterns but also some distinctive features when compared to US stocks.
The empirical analysis on 541 stocks between July 1971 and June 1997
shows (Section 2) that S&V portfolio strategies based on earning per share
(EPS), return on equity (ROE), MVs and market to book values (MTBVs)
signi®cantly outperform the index in terms of 26-year average monthly returns.
We try to discriminate between two alternative explanations for these premia,
the latent risk factor hypothesis, which would reconcile these anomalies with the
EMH (Fama and French, 1992, 1995) and the LSV hypothesis, which implies
that S&V strategies are not any riskier and that their premia are earned in
transactions with irrational agents erroneously extrapolating market trends.

We investigate (Section 3) the issue looking at di€erent risk measures and ®nd
that S&V strategies do not covariate more than other strategies with GDP,
they are still signi®cantly more pro®table when corrected for their standard
deviations, they are not explained by ex post systematic nondiversi®able risk or
by latent risk factors. Empirical ®ndings also show a di€erence between ROE
and MTBV strategies, on the one side, and MV and EPS strategies, on the
other side. There are at least four pieces of evidence suggesting that the former
strategies bet on ®rms which are more ®nancially distressed and are then ®nancially riskier. Firms included in lowest ROE and MTBV portfolios have on
average far higher debt±equity ratios and ROE values than ®rms in lowest MV
and EPS portfolios. MTBV and ROE preformation performance present at
least one long period of negative cross-sectional mean returns leading to a
cumulative four±®ve month loss of at least 10% prior to portfolio formation.
This is an indication of the presence of a common distress factor and of an
3
An alternative agency cost explanation suggests that fund managers choice of past winners may
be easier to explain to sponsors (Lakonishok et al., 1994).

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

897

overexposure to systematic risk which is also evidenced by a higher beta and by
an overreaction to upturns and downturns after portfolio formation. On the
contrary, EPS and MV portfolios have none of the above characteristics. Their
preformation cross-sectional mean returns do not present any sign of ®nancial
distress and their postformation performance evidences lower exposure to
systematic risk.
The paper also investigates the impact of survivorship bias on these results.
Empirical results are compared with the benchmark of the total sample buyand hold portfolio which is 20% higher in terms of mean monthly returns
(MMRs) than the Financial Times All Share Index Benchmark. In addition, a
speci®c analysis on the universe of stocks delisted from the LSE shows that the
share of delisted ®rms on which investors would have lost all their money and
that belong to our successful portfolios is quite small. S&V strategies when
corrected for risk of delisting and loss of shareholders capital still outperform
stock market index and our total sample index.
Final conclusions and further directions for research are addressed in
Section 4.

2. Premia from S&V strategies on the UK stock market
We select from Datastream a sample of 541 stocks listed on the London

Stock Exchange for which we gather daily stock exchange prices, and other
indicators such as the ratio of MV to book value, ROE, leverage (LEV),
earnings per share, price earnings and pre-portfolio ranking betas. 4
For all these stocks we collect data from 1970 to 1997. Every year we rank
all stocks on ascending values of the selected indicator and we form 11 portfolios using all deciles and the ®rst ventile as breakpoints in the distribution of
the selected indicator. For all indicators that include balance sheet values
(price earning, EPS, MTBV, ROE and LEV) we use the end of December
value in the year t ) 1 to build portfolios and calculate average monthly stock
returns of the portfolios in the period running from July of the year t to June
of the year t + 1. 5 When we rank portfolios on size we use MV as a proxy and
we consider the end-of-June value of the year t. Pre-ranking betas are calculated by using two-year monthly returns of stocks and of the Financial Times

4
MTBV is equal to the percentage value of (MV)/(equity capital and reserves minus total
intangibles). LEV is equal to (subordinated debt plus total loan capital plus short-term (one-year)
borrowings)/(total capital employed plus short-term (one-year) borrowings minus total intangibles
minus future income tax bene®ts). ROE is equal to (net pro®t after tax, minority interests and
preference dividends)/(equity capital and reserves minus intangibles plus total deferred tax).
5
This lag is necessary to allow end-of-year balance sheet statistics to be fully known by investors.

The methodology is the same followed by FF (1992, 1993, 1996) and LSV (1997).

898

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

All Share Index minus the risk-free interest rate, proxied by the monthly
equivalent of the yield on the three month UK average bank deposit rates. The
estimation period runs from June of the year t ) 2 to June of the t. Tables 1
and 2 present descriptive features of the 11 equally weighted portfolios built on
ranked values of the four selected indicators. Descriptive results from these
tables show that the distribution of MMRs obtained from our 11 portfolios in
the July 1971±June 1997 period has similar features to those found by FF
(1992) in the US. The portfolio formed every year with the 5% lowest MTBV
stocks (Table l) has a performance of 2.54% MMRs for the 26-year period,
which is signi®cantly higher than 1.02% value which represents the return for
the 26-year buy-and-hold strategy on the Financial Times All Share Index, and
also higher than the 1.29 which is the MMR for the 26-year buy-and-hold
strategy on our total sample portfolio. T-statistics indicate that fund managers
following the two strongest S&V strategies (selection of the 5%, or of the 10%,

stocks with lowest MTBVs) would have signi®cantly outperformed both these
two passive strategies. 6 If we look at other portfolio characteristics we ®nd
that, apart from the highest market to book portfolio, lower market to book
portfolios earn higher returns and are composed of ®rms which also have
lower returns on investments and are smaller in size. Stocks in the lower
market to book portfolios behave quite similarly to LSV (1994) value stocks 7
and the market seems to understand it. In fact, following LSV (1994) and
Gordon and Shapiro (1956), a high price earning should indicate, holding
constant discount rates and payout ratios, a high expected growth rate of
earnings which is typical of value stocks. Portfolios containing stocks with
extremely low MTBV and ROE values have, in fact, higher average price
earnings. This implies that ®rms included in these portfolios are expected to
grow more in the future. If the empirical ®ndings will demonstrate that S&V
strategy premia persist even when risk adjusted, we should conclude from these
price earnings that the market understood, but underestimated, the growth
potential of these stocks.
MV portfolios (Table 1) provide a similar and even clearer ranking as the
two smallest size strategies (0±5 and 0±10 portfolios) signi®cantly outperform

6
In absolute terms, MMRs are higher than those found by FF (1992) as 1963±1990 average for
the US stock market on the top 10% book to market portfolio (1.92%) and presumably higher than
the Lakonishok et al. (1994) and Brouwer et al. (1996) results (respectively, average yearly returns
on the highest book to market portfolio of 17% in the US between 1968 and 1989 and of 14% in
four European Stock Exchanges between 1983 and 1992).
7
LSV (1997) argue from their empirical analysis that S&V strategy premia are generated by
trading with agents erroneously extrapolating trends in stock returns. According to their
hypothesis, glamour stocks are those which performed well in the past and are (erroneously)
expected to perform well also in the future. Value stocks are instead those which performed badly
in the past and are (erroneously) expected to perform badly also in the future.

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

899

Table 1
Mean monthly returns and economic fundamentals of portfolios formed on market-to-book values and market
values (percent values, LSE: July 1971±June 1997)a
MTBV

a

PE

EPS

LEV

ROE

MTBV portfolios
0±5
)0.93
0±10
)0.27
10±20
0.59
20±30
0.73
30±40
0.87
40±50
1.04
50±60
1.22
60±70
1.45
70±80
1.76
80±90
2.30
90±100
7.01

28.09
29.59
23.68
17.09
17.61
20.87
16.87
14.61
19.30
18.74
44.15

20.94
15.09
10.76
25.46
12.99
13.92
11.96
19.18
17.20
50.67
20.36

0.66
0.48
0.40
0.28
0.57
0.42
0.41
0.43
0.42
0.41
0.87

)2.67
4.23
6.02
7.69
8.43
9.88
11.29
12.30
14.98
17.00
23.11

MV portfolios
0±5
0.45
0±10
0.87
10±20
0.94
20±30
1.04
30±40
1.11
40±50
1.55
50±60
1.54
60±70
1.54
70±80
2.02
80±90
2.23
90±100
2.41

30.89
29.74
34.39
22.71
18.22
19.14
23.20
15.78
17.20
17.56
14.75

8.46
8.76
11.15
11.94
25.22
18.48
15.32
26.54
19.52
40.47
14.50

0.31
0.37
0.51
0.35
0.39
0.40
0.37
0.40
0.53
0.51
0.76

3.10
4.19
2.43
12.93
9.92
10.97
12.87
13.06
15.39
13.18
17.34

MV

MMR

t
(mean)b

t
(mean)c

t
(mean)d

157.58
111.78
74.44
157.24
200.44
295.11
401.82
364.46
340.62
444.26
698.15

2.54
2.35
1.69
1.36
1.34
1.32
1.04
1.17
1.12
0.97
0.97

6.75
6.93
5.28
4.61
4.26
4.06
3.36
3.60
3.44
2.94
2.85

4.04
3.92
1.72
0.29
0.20
0.11
)1.05
)0.49
)0.72
)1.29
)1.38

4.20
4.13
2.09
1.15
1.02
0.92
0.06
0.46
0.31
)0.15
)0.15

0.82
1.23
3.11
6.25
11.53
20.87
37.09
71.37
159.05
411.32
2239.26

2.65
2.17
1.60
1.29
1.30
1.18
1.19
1.20
1.23
1.13
0.99

8.21
7.73
5.37
4.10
4.13
3.70
3.57
3.34
3.22
2.79
2.73

4.96
3.76
1.18
)0.01
0.02
)0.39
)0.35
)0.28
)0.18
)0.45
)0.93

5.05
4.09
1.95
0.86
0.89
0.50
0.51
0.50
0.55
0.27
)0.08

We consider a dataset of 541 stocks listed in the UK stock exchange. For each of these stocks we gather balance
sheet and stock price data from July 1971 to June 1997. We select six variables which may help in stock selection:
MTBV (market to book value); MV (market value), EPS (earnings per share), PE (price earnings), ROE (return
on equity and LEV (leverage). Market to book value (MTBV) is equal to the percentage value of the ratio
(market value)/(equity capital and reserves minus total intangibles). Leverage (LEV) is equal to (subordinated
debt plus total loan capital plus short term (one-year) borrowings)/(total capital employed plus short term (oneyear) borrowings minus total intangibles minus future income tax bene®ts). ROE is equal to (net pro®t after tax,
minority interests and preference dividends)/(equity capital and reserves minus intangibles plus total deferred
tax). 11 Portfolios are formed according to ascending values of return on equity (ROE) and of earning per share
(EPS) values using as breakpoints percentile values of this indicator (i.e. the ®rst portfolio includes stocks with
the lowest ®ve percent return on equity values on all considered stocks). Portfolios are formed at the end of June
of any year t on values that the ranking variable assumes at the end of December of year t ) 1 (June of year t for
MV portfolios) and are held for one year (until June of year t + 1). MMRs are mean monthly returns for each of
the 11 portfolios calculated from July of period t to June of period t + 1 and averaged across all portfolio formation years.
b
t (mean) is a t-statistics testing whether mean monthly returns of the selected portfolio are signi®cantly di€erent
from zero.
c
t (mean) is a t-statistics testing whether mean monthly returns of the selected portfolio are signi®cantly di€erent
from the July 1971±June 1997 total sample mean monthly returns (1.293).
d
t (mean) a t-statistics testing whether mean monthly returns of the selected portfolio are signi®cantly di€erent
from the July 1971±June 1997 mean monthly returns of the Financial Times All Share Index (1.02).
*
MMRs are signi®cantly di€erent from the considered mean at 99%.
**
MMRs are signi®cantly di€erent from the considered mean at 95%.

900

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

Table 2
Mean monthly returns and economic fundamentals of portfolios formed on return on equity and on earning per
share values (percent values, UK: July 1971±June 1997)a
MTBV

a

PE

EPS

LEV

ROE

MV

MMR

t
(mean)b

t
(mean)c

t
(mean)d

ROE portfolios
0±5
1.03
0±10
0.91
10±20
0.95
20±30
0.91
30±40
1.20
40±50
1.39
50±60
1.49
60±70
1.61
70±80
2.09
80±90
2.12
90±100 3.83

31.22
51.57
52.57
21.48
16.94
20.94
14.20
12.09
15.37
12.82
13.80

16.93
14.49
10.58
9.39
12.25
11.49
14.78
24.50
18.65
52.69
21.85

1.23
0.85
0.46
0.39
0.44
0.41
0.37
0.44
0.37
0.41
0.57

)38.07
)19.79
3.53
6.29
8.33
10.17
12.03
13.97
16.34
19.79
44.23

120.33
97.57
110.40
174.07
301.85
284.19
297.02
346.97
402.05
415.54
651.75

2.04
1.86
1.47
1.33
1.46
1.20
1.30
1.13
1.16
0.91
1.15

5.23
5.29
4.66
4.21
4.43
3.85
4.16
3.65
3.56
2.90
3.17

2.04
1.77
0.62
0.13
0.60
)0.34
0.02
)0.56
)0.44
)1.29
)0.45

2.62
2.39
1.43
0.98
1.33
0.58
0.90
0.36
0.43
)0.35
0.36

EPS portfolios
0±5
1.69
0±10
1.44
10±20
1.43
20±30
1.54
30±40
1.86
40±50
1.60
50±60
1.88
60±70
2.01
70±80
1.45
80±90
1.51
90±100 1.99

137.26
89.36
20.01
17.68
17.42
14.01
16.98
15.32
12.46
11.80
12.08

0.46
0.80
2.04
3.21
4.43
5.85
7.52
9.55
12.30
16.64
136.29

0.49
0.50
0.35
0.41
0.36
0.42
0.42
0.43
0.60
0.38
0.31

7.52
9.05
10.77
12.36
12.96
13.35
13.06
13.69
15.77
12.23
16.31

26.80
36.00
63.54
104.93
130.87
258.05
350.92
381.48
549.34
661.70
725.10

2.21
2.03
1.76
1.54
1.33
1.33
1.28
1.11
0.89
0.73
0.65

6.44
6.05
5.63
4.84
4.25
4.41
4.08
3.42
2.74
2.28
2.10

3.02
2.48
1.64
0.85
0.13
0.14
)0.05
)0.63
)1.38
)1.93
)2.34

3.47
3.01
2.37
1.63
0.99
1.03
0.83
0.28
)0.40
)0.91
)1.19

We consider a dataset of 541 stocks listed in the UK stock exchange. For each of these stocks we gather balance
sheet and stock price data from July 1971 to June 1997. We select six variables which may help in stock selection:
MTBV (market to book value); MV (market value), EPS (earnings per share), PE (price earnings), ROE (return
on equity and LEV (leverage). Market to book value (MTBV) is equal to the percentage value of the ratio
(market value)/(equity capital and reserves minus total intangibles). Leverage (LEV) is equal to (subordinated
debt plus total loan capital plus short term (one-year) borrowings)/(total capital employed plus short term (oneyear) borrowings minus total intangibles minus future income tax bene®ts). ROE is equal to (net pro®t after tax,
minority interests and preference dividends)/(equity capital and reserves minus intangibles plus total deferred
tax). 11 Portfolios are formed according to ascending values of return on equity (ROE) and of earning per share
(EPS) values using as breakpoints percentile values of this indicator (i.e. the ®rst portfolio includes stocks with
the lowest 5% return on equity values on all considered stocks). Portfolios are formed at the end of June of any
year t on values that the ranking variable assumes at the end of December of year t ) 1 (June of year t for MV
portfolios) and are held for one year (until June of year t + 1). MMRs are mean monthly returns for each of the
11 portfolios calculated from July of period t to June of period t + 1 and averaged across all portfolio formation
years.
b
t (mean) is a t-statistics testing whether mean monthly returns of the selected portfolio are signi®cantly di€erent
from zero.
c
t (mean) is a t-statistics testing whether mean monthly returns of the selected portfolio are signi®cantly di€erent
from the July 1971±June 1997 total sample buy-and-hold mean monthly returns (1.293).
d
t (mean) is a t-statistics testing whether mean monthly returns of the selected portfolio are signi®cantly di€erent
from the July 1971±June 1997 mean monthly returns of the Financial Times All Share Index (1.02).
*
MMRs are signi®cantly di€erent from the considered mean at 99%.
**
MMRs are signi®cantly di€erent from the considered mean at 95%.

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

901

average total sample returns. Looking at other ®rm characteristics we ®nd that
portfolios formed by stocks of poorly capitalised ®rms have relatively higher
MMRs and price earnings, relatively lower ROE and LEV. Here again, small
stocks seem to be value stocks in the LSV (1994) sense. Their earnings are
expected to grow more than those of other stocks, yet consistent return premia
from holding them in portfolios are earned for many years. An interesting
di€erence between MTBV and MV portfolios is that, in the second case, also
LEV seems to be positively and monotonically related to portfolio size values.
This may highlight the diculty of ®nancial markets to provide equity ®nancing to small ®rms although expectations on the growth of their earnings
are high.
ROE portfolios (Table 2) con®rm that value strategies earn large premia on
index performance. Portfolio rankings seem to show that both LEV and
MMRs are lower for groups of ®rms with higher returns on equity. Apart from
the obvious relationship with LEV where, holding constant the ®rmÕs performance, a higher debt/equity ratio reduces returns on investment and increases
returns on equity, no other variables exhibits a distribution which is correlated
with descending values of the ranking variable. Price earnings and LEV are the
two portfolio rankings which yield the lowest premia. 8 The ®rst result is
consistent with the surveyed literature on cross-sectional determinants of stock
returns in the US where the impact of price earnings on future stock performance is overshadowed by that of size and book to MVs.
Descriptive evidence on EPS portfolio rankings (Table 2) seems to con®rm
at ®rst glance that S&V strategies earn large return premia on buy-and-hold
index strategies. Lower EPS portfolios have on average higher price earnings
and then a higher ratio between expected future and current earnings. 9
When we look at yearly values of the di€erences between MMRs of 0±10
and 90±10 portfolios and the Financial Times All Share Index we can see that
value strategies signi®cantly outperform glamour strategies even when we look
at their relative success year by year. 0±10 size and MTBV portfolios have a
lower performance than relative 90±100 portfolios only in four years and the
0±10 EPS portfolio only in two years out of 26. 10

8

Estimates are omitted for lack of space and are available from the authors upon request.
We estimated also S&V premia for value weighted portfolios. Results yield, as expected,
slightly lower MMRs than equally weighted portfolios given that large ®rms are generally less
pro®table than small ®rms in our sample. S&V strategies are obviously more successful if we follow
a dividend reinvestment strategy. Estimates with value weighted portfolios and with dividend
reinvestment are omitted for lack of space and are available from the authors upon request.
10
The performance of value and size strategies in our sample is higher than that reported by
Brouwer et al. (1996) in the UK and other European countries both in terms of MMRs and relative
success year by year. This may be partly due to the inclusion of the seventies in the sample period
and to the use of 0±10 and not 0±20 portfolios.
9

902

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

Tables 1 and 2 show the success of S&V strategies, but are of no help in
identifying the relative strength of the variables used building our portfolios.
The correlation between most of them (positive of MTBV with ROE and MV;
negative of EPS with price earnings, positive of EPS with ROE and MV;
negative of ROE with LEV) indicates that a multivariate and econometric
analysis is needed in order to identify the relative contributions of these factors
to return premia. 11

3. Do premia from S&V strategies disappear when risk adjusted?
The simple inspection of MMR rankings across di€erent portfolios in
Tables 1 and 2 seems to con®rm that S&V strategies are successful in the 1971±
1997 period. A general question arising from the inspection of these tables is
why these return opportunities, which are partially incorporated in expectations expressed by price earnings, are not rapidly exploited by market agents
and why S&V strategy premia persist for so long (up to ®ve-year postformation
portfolio returns). Do these strategies reveal a failure of the EMH hypothesis
or do they re¯ect a latent relative risk factor? To answer this question we try to
measure the exposure to di€erent risk sources of S&V strategy premia: (i)
MMRs standard deviation of individual stocks being part of S&V strategy
portfolios; (ii) MMRs standard deviation of successful portfolios; (iii) covariation of S&V strategy returns with GDP growth; (iv) sensitivity of portfolio
returns to downturns in total sample returns; (v) exposure to systematic nondiversi®able risk; (vi) exposure to additional risk factors (small ®rm risk factor
and ®nancially distressed ®rm risk factor) which are usually not considered in
single factor CAPM models; (vii) bankruptcy risk of ®rms selected in S&V
portfolios.

11
For a preliminary assessment of the marginal contribution of each factor we performed a
bivariate descriptive analysis in the following way. We break our stocks into six groups according
to each of the four variables (EPS, ROE, MTBV, MV) which seem to a€ect most stock returns.
Five portfolios were created using as breakpoints the quintiles of the distribution, the sixth
portfolio is created using the lowest decile. We use the six variables two by two and we create sixby-six intersections of our portfolios. Any two-variable analysis measures MMRs from portfolios
created every year which are delimited by these intersections. After one year stocks are sold and
portfolios are formed again following the same method. Bivariate portfolio tables show that left
corner strategies earn the largest premia and that sometimes they are more successful than
strategies described in Tables 1 and 2. This is the case of the MV/EPS (2.84% average monthly
returns) and of the EPS/MTBV (2.68% average monthly returns) value strategy combinations. A
general result from this bivariate analysis is that most of the variables maintain their ranking
properties net of the control factor (tables illustrating results from bivariate strategies are omitted
for lack of space and are available from authors upon request).

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

903

Only in the case of the lowest (0±5) ROE and of the lowest (0±5) MTBV
strategies preliminary descriptive statistics present clear evidence that these
portfolios include stocks of ®rms which are ®nancially distressed given that
they have signi®cantly lower returns on equity and relatively higher borrowing ratios. If instead we look at the ®rst MV and at the ®rst EPS portfolios we do not ®nd evidence of higher ®nancial distress in spite of large
MMR premia. 12
To con®rm intuitions originating from descriptive statistics we propose an
econometric estimate to identify the net impact of selected variables respectively on one year and two-year MMRs from buy-and-hold strategies. We
adopt a mean group estimator approach by performing 25 cross-sectional estimates for any observational year and then compute 25-year average coecient for each regressor and its time series standard error. 13
As estimation period we consider one-year (or two-year) MMRs running
from July of the year t to June of the year t + 1 on the set of explanatory
variables measured at the end of December of the year t ) 1 (end of June of the
year t) when they do (do not) contain balance sheet data. To evaluate the
impact of our variables, net of exposure to systematic risk, we propose two
speci®cations (Tables 3a and b) in which we alternatively add preformation
and postformation betas to the set of explanatory variables. Preformation
betas are calculated on the basis of two-year monthly returns running from
July t ) 2 to June t, while postformation betas run from July t to June t + 2.
Results from the two di€erent speci®cations show that the negative impact of
EPS on MMRs is quite stable both with preformation and postformation
betas. The negative impact of MTBV is weak in the speci®cation with preformation betas and is eliminated with postformation betas. The relevance of
the MTBV variable then seems to be much smaller than in the FF (1995) paper.
This may be explained by the use of some variables (EPS and ROE) which are
not considered in the FF experiment and by the fact that FF use postformation
portfolio betas and not postformation betas of individual stocks. But our
multicollinearity tests reject the hypothesis of a strong correlation between
these two variables and book to MVs. It then seems that e€ective ex post

12
Returns on equity of the 0±5 MTBV and ROE portfolios are in fact negative and borrowing
ratios are respectively 0.66% and 1.23%. Returns of equity of the 0±5 MV and EPS portfolios are
small but positive and borrowing ratios are respectively 0.31% and 0.49%.
13
We check whether the multicollinearity problem is serious by regressing each of the
independent variables of the ®nal equation on other regressors. These tests evidence that the
multicollinearity problem between LEV and ROE is serious for some years (1983, 1986, 1989, 1993
and 1994). We alternatively perform estimates with both and with only one of the two variables for
the above mentioned years and ®nd that 25-year average coecients of other variables are virtually
unaltered in the two di€erent speci®cations. We therefore omit results from the second procedure.

904

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

Table 3
Mean group estimators of determinants of cross-sectional variations in stock returnsa
Dep. variable

MMR from 1-year buy-andhold strategy

Dep. variable

MMR from 2-year buy-andhold strategy

Regressors

Coecient

Regressors

Coecient

t-statistics

(a) Preformation betas used as regressors
C
1.57
23.31
PE
)0.02
1.35
MTBV
)0.11
)1.98
MV
0.002
)0.71
EPS
)0.01
)7.89
ROE
)0.01
)1.82
LEV
0.12
2.74
ABETA
)0.13
)1.86

C
PE
MTBV
MV
EPS
ROE
LEV
ABETA

1.38
)0.02
)0.02
)0.003
)0.003
)0.01
0.13
)0.20

21.93
)2.83
)1.50
)1.38
)4.85
)1.71
1.97
)2.92

(b) Postformation betas used as regressors
C
1.573
26.200
PE
)0.024
0.198
MTBV
)0.002
)1.354
MV
0.115
3.259
EPS
)0.009
)7.967
ROE
)0.006
)2.874
LEV
)0.089
)2.207
PBETA
0.578
6.567

C
PE
MTBV
MV
EPS
ROE
LEV
PBETA

1.28
)0.02
)0.02
)0.002
)0.003
)0.01
0.14
0.48

20.75
)2.75
)1.66
)1.06
)5.19
)1.58
2.10
6.21

t-statistics

a
We consider a dataset of 541 stocks listed in the UK stock exchange. For each of these stocks we
gather balance sheet and stock price data from July 1971 to June 1997. We perform 26 crosssectional estimates of mean monthly returns according to the two alternative speci®cations:

MMRt ˆ a0 ‡ a1 ROEtÿ1 ‡ a2 EPStÿ1 ‡ a3 LEVtÿ1 ‡ a4 MVtÿ1 ‡ a5 MTBVtÿ1 ‡ a6 PEtÿ1
‡ a7 ABETAtÿ1 ‡ e
or
MMRt ˆ a0 ‡ a1 ROEtÿ1 ‡ a2 EPStÿ1 ‡ a3 LEVtÿ1 ‡ a4 MVtÿ1 ‡ a5 MTBVtÿ1 ‡ a6 PEtÿ1
‡ a7 PBETAtÿ1 ‡ e;
where MMR are mean monthly returns from July (t) to June (t + 1). For all indicators that include
balance sheet values (price earning, earning per share, market-to-book value, ROE and leverage)
we compute the December value in the year t ) 1 market to book value (MTBV) is equal to the
percentage value of the ratio (market value)/(equity capital and reserves minus total intangibles).
Leverage (LEV) is equal to (subordinated debt plus total loan capital plus short-term (one-year)
borrowings)/(total capital employed plus short-term (one-year) borrowings minus total intangibles
minus future income tax bene®ts). ROE is equal to (net pro®t after tax, minority interests and
preference dividends)/(equity capital and reserves minus intangibles plus total deferred tax). MV is
the ®rmÕs market value at the end of June of the year t. Pre-ranking betas (ABETA) are calculated
using two year monthly returns of stock and Financial Times All Share index minus the risk free
interest rate proxied by the monthly equivalent of the three month UK average bank deposit rates
from July of year t ÿ 2 to June of year t. Post-ranking betas (PBETA) are calculated using monthly
returns of stock and Financial Times All Share Index minus the risk free interest rate proxied by the
monthly equivalent of the three month UK average Bank deposit rates from July of year t to June
of year t ‡ 2. The table reports 26-year average coecients of the cross-sectional estimates performed for each year. t-statistics are computed on the basis of time series standard deviations.

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

905

exposure to systematic nondiversi®able risk of individual stocks kills the explanatory power of book to MVs. 14
Another variable whose impact changes between the ®rst and the second
speci®cation is MV. Size is negative and weakly signi®cant in the speci®cation
with preformation betas and it becomes positive and highly signi®cant when we
include postformation betas. Here again the MTBV argument applies. Small
size seems here to be a proxy for ex post e€ective systematic nondiversi®able
risk and not for an additional relative distress component when individual
stock betas are considered. In our extended set of regressors, the two additional
variables of EPS and ROE are those which most absorb the relative distress
factor or, according to the alternative hypothesis, more clearly discriminate
between value and glamour stocks. They are in fact a direct indication of ®rmsÕ
underperformance.
Premia from S&V strategies then seem not to be entirely explained by traditional risk factors. The explanatory power of our regressors persists after
controlling ex post systematic nondiversi®able risk. In addition, S&V strategies
do not generally underperform the market in the most negative years when
looking at their annual performances. If we consider risk as covariance with
GDP or consumption growth, according to a consumption-CAPM approach
(Breeden et al., 1989), we may see that betting on value stocks is often less risky
than betting on glamour stocks. This because quarterly returns of value stock
portfolios covariate less with GDP rates of growth than those of glamour stock
portfolios (see Table 4). The only evidence that S&V strategies may be slightly
riskier is that standard deviations of their portfolio MMRs are somewhat
higher than those of glamour stock portfolios (the di€erence between the top
and the bottom portfolios ranges from 15% to 37% and is negative only in the
case of MV portfolios). Portfolio diversi®cation though helps much more value
than glamour strategies given that the top-bottom di€erence among standard
deviations is reduced when we pass from average individual stock standard
deviations to portfolio average standard deviation. This is particularly evident
for size portfolios (Table 4) where average standard deviation of individual
stocks in the top value portfolio is far higher than that in the top glamour
portfolio (+62.95%), while standard deviations of the top value (0±5) portfolio

14
FF may then be right in thinking that book to MVs proxy for risk factors, but not ± as they
believe ± for risk components orthogonal to traditional beta risk. It is just that portfolio betas are
something di€erent than individual stock betas being the former (the latter) to exposure to
systematic nondiversi®able risk when holding the entire portfolio (the individual stock). A
fundamental point is then that MTBV contrarian strategy premia may then disappear when
adjusting for the risk run by holding the single stock but may be positive when adjusting for the risk
run by holding the portfolio. Our result is consistent with Loughran (1997) showing in crosssectional regression of individual stocks the MTBV variable is not signi®cant once January and size
e€ects are considered.

Percentiles

MV portfolios

Average S.D. of
monthly returns
for individual
stocks included
in the portfolio

Covariance
Average S.D. of
portfolio monthly between portfolio
monthly returns
returns
and GDP rate of
growth

Average S.D. of
monthly returns
for individual
stocks included
in the portfolio

12.60
11.33
9.22
9.03
39.51

6.65
5.99
5.74
6.00
10.72

1.90
1.75
1.82
2.69

13.44
12.49
10.06
8.25
62.95

6.88
6.22
5.50
6.40
7.58

1.67
1.58
2.02
3.29

ROE portfolios
0±5
0±10
40±50
90±100
Percent increase in S.D.
between ®rst and last
portfolio
S.D. of the FT all share
a

14.69
13.25
9.52
10.05
46.22

Covariance
Average S.D. of
portfolio monthly between portfolio
monthly returns
returns
and GDP rate
of growth
5.70
4.96
5.64
6.42
)11.17

)0.13
0.17
1.65
1.38

6.06
5.93
5.32
5.47
10.69

1.32
1.37
0.86
2.19

EPS portfolios
12.38
12.02
9.39
9.15
35.19

6.33

We consider a dataset of 541 stocks listed in the UK stock exchange. For each of these stocks we gather balance sheet and stock price data from July
1971 to June 1997. Market to book value (MTBV) is equal to the percentage value of the ratio (market value)/(equity capital and reserves minus total
intangibles). Leverage (LEV) is equal to (subordinated debt plus total loan capital plus short term (one-year) borrowings)/(total capital employed plus
short term (one-year) borrowings minus total intangibles minus future income tax bene®ts). ROE is equal to (net pro®t after tax, minority interests and
preference dividends)/(equity capital and reserves minus intangibles plus total deferred tax). Portfolios are formed according to ascending values of the
book value to market value ratio using percentile values of the same indicator as breakpoints (i.e. the ®rst portfolio includes stock with the highest ®ve
percent book to market values on all sample stocks). Portfolios are formed at the end of June of any year t on values that the ranking variable assumes
at the end of December of year t ÿ 1 (June of year t for MV portfolios). For each portfolio we measure the average standard deviation of monthly
returns for individual stocks included in the portfolio, the average S.D. of portfolio monthly returns and the covariance between portfolio monthly
returns and GDP rate of growth.

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

0±5
0±10
40±50
90±100
Percent increase in S.D.
between ®rst and last
portfolio

MTBV portfolios

906

Table 4
A comparison of simple risk indicators for size and value strategiesa

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

907

are lower than that of the top glamour (90±100) portfolio ()11%). Our 0±10
value portfolios cannot be considered riskier even when we compare their
standard deviations (6.22 for ROE, 5.93 for EPS, 5.99 for MTBV and 4.96 for
MV portfolios) with that of a 25-year buy-and-hold strategy replicating the
Financial Times All Share Index (6.33).
The inspection of the dynamics of cross-sectional averages of monthly returns for the same type of portfolios 15 ®ve years after and before portfolio
formation gives us further information on exposure to risk for value and
glamour portfolios and marks the di€erences between ROE and MTBV
strategies, on one side, and MV and EPS strategies, on the other (Fig. 1a±d).
Strategies based on the ®rst two factors seem relatively riskier than those
based on the last two factors for two reasons. Firstly, there is clear evidence of
preformation underperformance of the 0±10 portfolios with 2±3 series of
consecutive negative cross-sectional averages of monthly returns before the
formation year. Secondly, postformation returns of 0±10 portfolios seem to
perform better. Their returns grow more in upturns of both value and glamour
portfolios (when the entire stock market is presumably growing), while their
performance is not worse than that of glamour stocks in downturns of both
types of portfolios. These ®ndings are consistent with average balance sheet
values of 0±10 MTBV and ROE portfolios (Tables 1 and 2) which reveal signs
of ®nancial distress for these ®rms.
On the contrary, 0±10% MV and EPS portfolios do not present any evidence
of preformation underperformance and have the nice feature of outperforming
90±100 portfolios after portfolio formation both in downturns and in upturns
of cross-sectional mean returns calculated before and after the formation period (Fig. 1a±d). Downturn smoothing in these portfolios cuts almost all
negative tails in cross-sectional averages of monthly returns which are instead
very frequent in the 90±100 portfolios.
Fig. 1a±d also show the presence of a strong seasonality e€ect generated by
high average January returns. The signi®cance of the January e€ect disappears
when correction for exposure to systematic nondiversi®able risk is considered
in FF 3-factor CAPM estimates. 16
When inspecting preformation and postformation standard deviations of
monthly returns in our factor portfolios we replicate the Daniel and Titman
(1997) test on the alternative between common distress factors and common
characteristics as determinants of S&V strategy premia. According to the
15
These are not time series averages of monthly returns for the same strategy across sample years
as in Tables 1 and 2 but cross-sectional averages of monthly returns for the same type of portfolios
(i.e. 0±10 size portfolio) formed in di€erent years. All these are centered around the portfolio
formation date.
16
3 and 4 factor CAPM estimates with the January dummy are not presented here and are
available from the authors upon request.

908

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

authors, when common distress factors generate these premia, common variations of portfolio constituents (and consequently portfolio standard deviations) should decline in the years before and after portfolio formation, but they
should remain unchanged in case of persisting common characteristics. Results
from this test seem consistent with our interpretation of evidence from preformation and postformation MMRs. MTBV portfolios (and, in part, also
ROE portfolios), which seemed more exposed to ®nancial distress by graphical
inspection (Fig. 1a±d), exhibit a clear declining pattern in yearly standard
deviation from the formation year. The pattern of EPS and MV portfolio

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

909

Fig. 1. (a) Preformation and postformation cross-sectional mean returns of MTBV 0±10 and
90±100 portfolios. (b) Preformation and postformation cross-sectional mean returns of 0±10 and
90±100 MV portfolios. (c) Preformation and postformation cross-sectional mean returns of 0±10
and 90±100 ROE portfolios. (d) Preformation and postformation cross-sectional mean returns of
0±10 and 90±100 EPS portfolios.
Note: 0±10 and 90±100 portfolios are formed according to ascending values of the selected indicator
using percentile values of the same indicator as breakpoints (see Table 1). For each portfolio
formed at June t we calculate postformation (from July t to July t + 5) and preformation (from July
t ) 5 to July t) returns and average them with those of the same type of portfolio formed in di€erent
years.

910

M. Bagella et al. / Journal of Banking & Finance 24 (2000) 893±919

standard deviations is instead much ¯atter and seems more consistent with the
common characteristics hypothesis. 17
A test on the signi®cance of intercepts in speci®cations in which monthly
returns are regressed on various risk factors is another fundamental step to
check whether S&V strategies persist even when risk-adjusted. FF (1995)
propose on this point the extension of the original one-factor CAPM to a
three-factor CAPM where two additional risk factors, related to size and book
to MVs, are added to the traditional speci®cation. The rationale for adopting a
multifactor capital asset pricing model is that some risk factors are not captured by sensitivity to stock market index. Sensitivity to stock market index
measures exposure to macro economic or to political news and is likely to be
higher for ``blue chips'' than for small ®rms. There are other risk factors
though to which small ®rms or ®nancially distressed ®rms are particularly
exposed. Shocks in asset values may, for instance, reduce the value of collateral
a€ecting both solvency of ®nancially distressed ®rms and the capacity to obtain
credit of sm