International Journal of Economics and Financial Issues   
Vol. 4, No. 1,  2014, pp.196-216  
ISSN: 2146-4138 
www.econjournals.com 

196 
 

 
Stock Prices and Implied Abnormal Earnings Growth 

 
Hafiz Imtiaz AHMAD 

NYIT, Abu Dhabi, UAE. 
Email: hahmad02@nyit.edu 

 
Pascal ALPHONSE 

University of Lille North of France, F-59000, 
Lille, France-LSMRC. Email: palphonse@univ-lille2.fr 

 
Michel LEVASSEUR 

University of Lille North of France, F-59000, 
Lille, France-LSMRC. Email: michel.gabriel.levasseur@gmail.com

 
 

ABSTRACT: In terms of corporate valuation, the frequently used heuristics are Price Earnings or 
Price Earnings to Growth ratios. The development of a valuation model of type Abnormal Earnings 
Growth Model including modeling of expected rents evolution, conditions compatible with perfect 
competition, allows us to propose a testable relationship between market value of share, expected 
earnings per share in a year, its rate of growth in short term and a set of accounting variables 
composing a synthetic indicator of growth of company. Our results show that (1) expected increase in 
earnings per share are significantly associated with stock prices for developed countries, (2) but, the 
persistence of its effects is limited for emerging countries, (3) when the dynamics of growth are more 
complex, inclusion of synthetic variable of can make a significant correction term (4) and the implied 
cost of capital is significantly higher for emerging countries than for developed countries. 
 
Keywords: equity valuation; abnormal earnings; Emerging markets 
JEL Classifications: G12; G14; M41 
 

1. Introduction 
Our study examines the relationship between the market price of a share, expected earnings and its 
expected growth for the next two years because they are the very value drivers, followed by the 
financial community through the P/E ratio and PEG ratio, for example. Consistent with the current 
accounting literature called, the association. We take the proposal put forward by Barth et al. 
(2001): “an accounting amount is defined as value relevant if it has a predicted association with equity 
market values” (p.79) and their following remark; “accounting information can be value relevant but 
not decision relevant if it is superseded by more timely information”. We make no assumption 
regarding the efficiency of stock markets. Our study fits in the course of all those interested to price 
levels and not their changes. We raise this by a double question: knowing that the form of association 
between stock price and expected earnings per share depends on the type of growth of the company, (i) 
that brings short term increase in expected earnings by financial analysts to explain differences in stock 
market value (ii) can an indicator of growth built on historical accounting data correct the bias 
introduced by previous measure? 

The interest in this subject is primarily motivated by practical considerations. Investments in the 
international equity markets have become significant for fund managers worldwide. The use of methods 
based on comparison of basic observed ratios, for listed companies, between stock prices and expected 
earnings per share is often considered the most powerful, (Liu et al, 2007) reports that “EPS forecasts 
represented substantially better summary measures of value than did OCF forecasts in all five countries 
examined, and this relative superiority was observed in most industries”. Understanding the link 
between market value and expected earnings is likely to illuminate the investment process in countries 
where information is more difficult to collect for foreign investors. 



Stock Prices And Implied Abnormal Earnings Growth 
 

197 
 

The second motivation is of theoretical nature. It focuses on the relationship between book values 
and market values. The valuation models based on abnormal earnings growth (A.E.G.) provide support 
to the link between expected future earnings, expected dividends and market values. The pioneering 
model of Ohlson and Juettner-Nauroth (2005) claim that only the expected earnings for the next two 
years and expected dividend are sufficient. The empirical evidence is not conducive to this hypothesis 
(Gode and Mohanram, 2003), (Penman, 2005). The question is whether an extension of the model 
A.E.G.(Abnormal Earnings Growth) proposing a more fine decomposition of the abnormal earnings 
growth in volume and intensity provides a better  estimate of the link between expected earnings and 
stock price of a share. 

We begin our study with a theoretical extension of the model A.E.G. Aware of the fact that the 
models of type AEG are complex in their inner mechanics (Brief, 2007), we want to make development 
of the profitability in the form of a progressive realization of a set of growth opportunities. To do this, 
we take an idea developed by Walker and Wang (2003) in a different context, that of R.I.M. (Residual 
Income Models). As Walker and Wang, we bring together the microeconomic analysis and modeling of 
accounting earnings. But we do so as a part of valuation based on taking into account expected earnings 
and especially their growth. 

The second part of the study is empirical. Three samples are formed over the period 1998-
2008.They include American companies, firms from other developed countries (Germany, Australia, 
Canada, France, Japan, and the United Kingdom) and a set from emerging countries (China, Korea, 
Hong Kong, India, Malaysia, Singapore, Taiwan and Thailand). Our objective is to provide an 
international comparison. From historical accounting data, we build a synthetic indicator of growth by 
company. We, then, proceed to estimate our model by incorporating the variables of expected earnings 
(in level and in variation), this synthetic variable of growth and other control variables. The objective is 
to verify (1) that the anticipated effects of abnormal earnings growth are limited in time, (2) that the 
inclusion of the synthetic variable for growth makes a significant correction when the variable of 
growth in the short-term alone is insufficient,(3) that the values implicit of cost of capital are  
acceptable from an economic stand point. 

Our empirical study allows to establish the following results: 
(i) Whatever the geographical zone, expected earnings per share remains the variable most 

strongly associated with the stock market values. But, the coefficients are higher in 
developed countries than in emerging countries. The valuation of profits is affected by 
different levels of their persistence and more generally of risk. 

The expected change in earning per share is significantly associated with the market value of a share 
(especially for developed countries) but its persistence is limited (especially in emerging countries). 
This last result contrary to the intuition which would like the expected growth being greater in 
emerging countries, the PEG is a better tool of valuation in these countries. The PER and PEG ratio 
combine in valuation essentially, within developed countries. 

(ii) These two indicators must be supplemented to avoid either over-valuation or under-
valuation. Taking into account the intensity of the growth through historical accounting 
indicators provides a part of the missing information. The corrections are mostly positive 
(insufficient to take into account the growth potential by the increase of expected earnings, 
especially in emerging countries) and more rarely negative (low persistence of the intensity 
of the expected pension, rather in part of developed countries). 

(iii) At the international level, the expected implied rates of return are significantly higher in 
emerging countries than in developed countries. 

The rest of the paper is organized as follows. In Section 2, we develop our model; Section 3 presents 
our data and some descriptive statistics. Section 4 describes the methods of calculation of the variable 
of growth. Our results are presented in Section 5 and Section 6 concludes. 

 
2. The Model 

2.1  The sources of model: 
We take an idea developed by Walker and Wang (2003) in a different framework. Walker and Wang 

approach to microeconomic analysis and modeling of company’s accounting earnings particularly the 
R.I.M. (Residual Income Model). They studied several forms of competition and provide, among other, 
a representation of the dynamic followed by the residual income in a world of perfect competition. We 



International Journal of Economics and Financial Issues, Vol. 4, No. 1, 2014, pp.196-216 
 

198 
 

propose a similar extension but applied to the model AEG (Abnormal Earning Growth) proposed by 
Ohlson and Juettner-Neuroth (2005). 

We preferred to place our study in the current A.E.G. model because its point of departure is linked 
to an empirical observation. The accounting variable best associated with market value is expected 
earnings (Ohlson & Gao, 2006). Unlike the R.I.M. model that bases valuation on the book value of 
equity, the A.E.G. model anchor valuation in the capitalization of expected earnings (Ohlson  J.A., 
2005). The progress in the modeling requires a description of the dynamics of this earnings. Ohlson and 
Juettner Neuroth postulate that the annual variation in the expected abnormal earnings (income in 
excess of the remuneration of reinvested cost of capital) follows an autoregressive process of order 1. 
Not only, no theoretical justification is advanced to support this hypothesis, but this is certainly very 
restrictive, as it gives only expected incomes very close role in valuation. 

The purpose of this article is to extend the analysis of Walker and Wang to the model of Ohlson and 
Juettner Neuroth in the framework of a pure and perfect competition and unbiased accounting. The 
originality of this paper is inspired by a measure of growth, already used in accounting literature by 
Hribar and Yehuda (Hribar & Yehuda, 2008). Thus indirectly taking into account the expected rents, 
we, partly, believe to avoid some of the shortcomings highlighted by Holthausen and Watts 
(Holthausen and Watts, 2001). 

2.2 The valuation model from abnormal earnings growth and growth opportunities 
First we assume that the price of a share  is equal to the sum of free cash flow received by 
shareholders  discounted at a required rate: 

P = ∑
( )

∞     (1) 
A second hypothesis, the variation in earnings has two sources: the variation in the value of a rent 

and reinvestment of undistributed profits. The complementary hypothesis of the reinvestment of the 
latter at the rate r guarantees the neutrality of the dividend policy. By designating, intensity of expected 
rent by a  and q  its extent, we put: 

EPS − EPS = a ∙ q − a ∙ q + (EPS − FPS ) ∙ r   (2) 
This particular set of assumptions used to express the price of share based on the expected 

income, the required rate of return and expected values of the parameters defining the future rent: 
= + ∙ ∑

( [ ∙ ] [ ∙ ])
( )

   (3) 
To complete the model, we adopt a third hypothesis that the variables a  and q  follow linear 

informational dynamics described in (4). The intensity of the rent  a  is decomposed into a part 
depending on its past value  δ ∙ a  and a white noise 	ε , . 

Its persistence is measured by the parameter δ (with the condition 0 < < 1 to take into 
account the effects of competition). The extent of the rent q  is a function of its trajectory q  and a 
gap which itself decomposes into a corrective movement back toward the track 	γ ∙ (1 + c) ∙ (q − q ) 
and a white noise ε , . The coefficient γ measures the intensity of the restoring force to the track  q  . 
The trajectory q  of the extent of the rent grows at a rate c to take account of the growth. Finally, the 
two white noises embedded in these movements are assumed to be independent: there is no link 
between variations of intensity and variations of the extent of the rent. 

= ∙ + ,  
− = ∙ ( + ) ∙ ( − ) + ,    (4) 

= ∙ ( + ) 
, , , = 			∀ ,  

 
This set of assumptions allows to write the following relationship (Proof available) 

= − ( + ) ∙ ∙ ∙ + ∙ [ ] ∙ ∙    (5) 
 
with : 

g = (1 + c) ∙ δ ∙ γ − 1 
ℎ = (1 + c) ∙ δ ∙ (1 − γ) ∙ [δ ∙ (1 + c) − 1] 

CEPS = EPS + r ∙ FPS  



Stock Prices And Implied Abnormal Earnings Growth 
 

199 
 

The primary interest of this model is to retain the general form of popular valuation models, taking 
as anchoring the expected earnings per share. For example, if δ = γ = 1, it reduces to the model of 
Ohlson Juettner-Nauroth which is only a special case. Assuming again that E EPS = (1 + c) ∙
E EPS  , we find the standard model of Gordon and Shapiro. 

The second interest of this model is mainly to clarify the value of the coefficient included in the 
autoregressive dynamics of abnormal earnings growth. It is not solely equal to the expected rate of 
growth in the long run, as in Ohlson and Juettner-Nauroth. It takes into account the value creation 
potential of the firm, the speed with which the latter will be realized (γ) and its ability to persist (δ). 

The third interest is to show that under what conditions a valuation based only on expected earnings 
EPS  and EPS  may suffice. It is necessary that the term h is near to zero or that δ ∙ (1 + c) = 1. 
Conversely, when the ability to generate value is not persistent (δ < (1 + c) ), a model of type AEG 
overestimates the share. When the enterprise is only at the beginning of growth ( q high), its 
implementation very progressive (γ low) and its ability to create value very persistent  (δ > (1 + c) ), 
then a model of type AEG is very incomplete. Its explanatory power is weak and suffers from the 
absence of key variables. 

2.3 The specification of the model tested 
From an empirical point of view, the measures selected  for  and  are the 

median forecasts of earnings per share retained by IBES, noted  and .The measure chosen for 
[ ] is the median forecast  adopted by IBES for dividend per share, noted .We do not have any 

direct forecast for  ∙ [ ].  The objective of this study is to test the explanatory power of several 
approximations: 

∙ [ ] = ∑ ∙ ∙    (6) 
Where k is one of the N variables potentially correlated with the expected abnormal earnings 

growth, Y  knowing that α  is a measure of its expected impact on the evolution of the earnings and 
TAPS  total assets per share. P  is the share price in the beginning of the year. The 
variables	P ,	EPS ,	EPS  and DPS  were divided by TAPS  , to be normalized. Finally, the model was 
completed by the inclusion of a control variable for size measured by log of market capitalization in 
U.S. dollars. The following specification was chosen 
 

= + ∙ + ∙ ∙ + ∑ ∙ + ∙ ( ) +    (7) 
 
One of the main limits of this specification is that it only takes the average values for r and g with in 
each country. Note that according to the theoratical model we should have  

 r =
β
∙β

+
β
−

β
∙β

 and 	g = − . 

 
3. Data and Descriptive Statistics 
3.1  Constitution of the samples 

Our sample was compiled from the information available in early July 2009 in the data base 
Thomson Financial Accounting Research data and covering 18 countries for which the number of firms 
represented in this database was the highest, it is possible some information has been modified ex post 
by data provider. It contains both the developed countries (Germany, Australia, Canada, France, Italy, 
Japan, United Kingdom, Sweden and USA) and emerging countries (Brazil, China, Korea, Hong Kong, 
India, Malaysia, Singapore, Taiwan, and Thailand). South Africa and India were eliminated from 
sample due to too few and limited forecast data. In order to study the period 2001-2008 between the 
two crises, it was necessary to collect the data over the period 1998-2008. In effect some variables 
appear in the form of annual variations, other as average of past performance. Missing information, 
especially for forecast of earning per share, reduced the sample size. 

In order to constitute homogenous sample with in each of the country as regards of accounting year, 
we selected only the companies with year-end corresponding to the date most widely used in the 
country. Generally, it is the 31 December, with the exception of Australia (end of June) and Japan (end 
of March). This requirement generally seems not very constraining. 



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Vol. 4, No. 1,  2014, pp.196-216  
ISSN: 2146-4138 
www.econjournals.com 

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Table 1. Selection of Sample 
This table presents the modalities of selection of companies studied. The period of selection extends from 1998 to 2008.The data comes from Worldscope and IBES databases 
provided by Thomson Financial. The securities initially selected for all concerned countries are those considered by Thomson Financial as active or inactive, in order to limit the 
“survivorship” bias. Numbers of these securities correspond to firms effectively disappeared, to not listed companies or yet to particular categories of securities issued. The 
selection process consisted of a search of market values year after year of these companies and to retain only the firms years for which this information was available. In order to 
have uniform accounting periods by country, we have selected only those companies that adopted the most usual year end date for each country. By following the sector 
classification proposed by Fama and French (49), we have eliminated all societies of financial sectors and real estate (45-49) and the companies from which the sector was not 
identified. The following selection consisted of to retain only the firms for which accounting data and earnings per share forecast, necessary for the study was available 

 

Active 
and 

inactive in 
the 

database 
Thomson 
Financial 

Number of 
firms whose 
fiscal year 
end date is 
known 

The most 
frequent end 
of year for the 
country 

 

Number of 
firms having 
this year end 
date 

Percentage of 
firms with 
this year end 
date 

Number of 
firms with a 
code FF 
sector less 
than 45 

 

Number of 
companies with 
market 
capitalizations 
available at least 
for one year 

Number of firms 
/ year with 
known market 
capitalizations  b
etween 1998 and 
2008 

Number of firms 
/ year with the 
known book 
values used 
between 1998 
and 2008 

Number of 
firms/ year 
with equity 
&capitalizatio
n in excess of 
1 million $ 
between 1998 
&2008 

Number of firms / 
year with positive 

net income between 
1998 and 2008 

Number of firms 
/ year with 
positive net 
income between 
2001 and 2008 

Number of 
firms/  year 
with EPS 
forecasts 
available 
between 2001 
and 2008 

USA 28 013 8 574 December 6 086 70.98% 4 531 4 217 32190 30 888 25 127 15 910 12 078 5 940 
              

Germany 29 096 7 075 December 6 739 95,3% 6 066 546 4 624 2 457 2 386 1 807 1 424 705 
Australia 17 369 2 733 June 1 975 72,3% 1 660 1 376 8 163 6 668 5 831 2 613 2 287 851 
Canada 20 176 5 665 December 5 076 89,6% 4 282 937 6 342 3 962 3 790 2 168 1 778 840 
France 27 856 5 750 December 4 781 83,1% 4 131 470 4 099 2 534 2 417 1 924 1 603 812 
Italy 13 825 1 705 December 1 640 96,2% 1 422 210 1 648 1 287 1 280 967 762 356 
Japan 36 774 5 604 March 2 969 53,0% 2 652 2 564 24 453 10 979 10 876 9 176 8 167 3 818 
United Kindom 38 141 7 201 December 3 976 55,2% 3 454 702 4 869 4 771 4 316 2 650 2 107 985 
Sweden 11 050 1 772 December 1 633 92,2% 1 441 309 2 276 1 054 1 048 776 599 409 
Other developed countries 194 287 37 505  28 789  25 108 7 114 56 474 33 712 31 944 22 081 18 727 8 776 
 

             

Brazil 21 722 7 335 December 7 318 99,8% 6 615 250 1 957 1 008 974 787 647 252 
China 23 521 4 437 December 4 381 98,7% 4 081 1 768 10 682 2 493 2 421 2 047 1 672 381 
Korea 1 804 1 091 December 998 91,5% 956 948 7 691 5 603 5 482 4 235 3 570 376 
Hong Kong 7 155 1 240 December 805 64,9% 624 469 3 787 3 565 3 390 2 378 2 020 675 
Indonesia 888 716 December 716 100,0% 570 274 2 228 2 049 1 781 1 362 1 139 232 
Malaysia 1 938 1 450 December 918 63,3% 794 510 3 859 3 188 3 073 2 338 1 962 519 
Singapore 6 053 1 610 December 1 146 71,2% 1 014 354 2 564 2 128 2 066 1 581 1 319 340 
Taiwan 3 754 1 894 December 1 891 99,8% 1 795 1 418 9 725 4 605 4 589 3 630 3 071 628 
Thailand 1 084 800 December 755 94,4% 641 413 3 191 2 618 2 444 1 944 1 606 424 
Emerging countries 67 919 20 573  18 928  17 090 6 404 45 684 27 257 26 220 20 302 17 006 3 827 

 



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The percentage of companies respecting this practice is most often above 90%.However, there 

are two major exceptions among the developed countries (Japan and United Kingdom, where the 
percentage is around 50%). Similarly, Hong Kong and Malaysia have smaller proportions (about 60%). 
The financial and real estate companies whose accounting standards are often specific and not 
comparable were eliminated. We could raise within the Thomson Financial database only the market 
capitalization for  
7 114 companies of the other developed countries and 6 404 companies of emerging countries, for a 
total firms-year respectively equal to 56 474 and 45 684. Companies are not, therefore, present for all 
years. If we compare these figures to theoretical value of firms-year with a continuous presence over 11 
years, we obtain a frequency of occurrence of 72% for other developed countries and 65% for emerging 
countries. This last sample is, therefore, somewhat less dense. 

The availability of accounting data required to estimate the variables used in the study further 
reduced the sample size. The loss of the number of observation is equivalent for the two sub 
populations (other developed countries and emerging countries), or about 40%. For the rest of the study, 
we selected only profitable companies. They are more numerous in emerging countries (77%) than 
among other developed countries (69%). Finally, the greatest loss of observation comes from the 
limited number of forecasts for earning per share available on IBES during this period. The coverage 
rate is 47% for other developed countries and only 23% for the emerging countries. In total, we have  
12 603 firm years distributed for 8 776 to other developed countries and 3 827 for emerging countries. 
The number of observation is increasing over the period: 802 in 2001 and 1809 in 2008 but relatively 
stable from 2004 to 2008.The maximum is 2175 in 2007, just before the last financial crisis. 
3.2 Descriptive statistics 

The average stock market values normalized by total assets (measured by the item 
WS.YrEndMarketCap divided by the item WS.TotalAssets of Worldscope database from Thomson 
Reuters) are substantially similar for emerging countries (1.09) and other developed countries 
(1.10).The medians are lower because of the asymmetry of the distributions associated with positive 
sign of this measure. With in groups, the averages are significantly different: the highest for Australia 
(1.47) and Indonesia (1.36) and the lowest for Italy and Japan (0.84) and Korea (0.77).The mean and 
median are higher in the case of USA (1.55 and 1.13 respectively), reflecting  a higher capitalization 
and /or greater indebtedness over this period. 

   The return (measured  by the item IBH.EPSMedianFYR1 divided by (WS.TotalAssets/ 
WS.Common Shares Outstanding) of  the databases Worldscope and  IBES from Thomson Reuters) 
appear higher for the emerging countries (0.103) and USA (0.01) than for other developed countries 
(0.075) if we consider expected earnings per share normalized by total assets per share. Brazil emerges 
as the best performing country (0.14) and Japan as the least (0.04).The ratio of the expected change in 
earnings per share normalized by total assets per share (measured by the difference of 
IBH.EPSMedianFYR2 and IBH.EPSMedianFYR1, divided by (WS.TotalAssets/ 
WS.CommonSharesOutstanding) of the databases Worldscope and IBES from Thomson Reuters) 
reinforces this impression. It is higher for the USA (0.018) and emerging (0.014) than for other 
developed countries (0.10), Brazil and Japan still occupying the same places. 

  The sample firms belonging to other developed countries are sized (measured by the logarithm of 
market capitalization in USD: WS.YrEndMarketCapUSD of Worldscope database from Thomson 
Reuters) a little larger than those of emerging countries, but smaller than the American ones. The 
companies are significantly smaller for Malaysia, Thailand and Singapore. 
 
 
 
 
 
 
 
 
 



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202 
 

Table 2. Descriptive Statistics 
This table presents the synthesis of the values taken in the sample by the 3 basic selected variable used in the 
chosen model, i.e. market capitalization at year end, expected earnings per share for the coming year and expected 
earnings growth for the following year .All these variables are normalized by total assets for the first, by total 
assets divided by number of shares for the following two. The table also presents a measure of the size of 
companies selected through the natural logarithm of the market capitalization. The sample contains for all the 
countries only the companies whose year end is 31 December (30 June for Australia and 31 March for Japan). The 
study period extends from 2001-2008.The data come from Worldscope and IBES databases provided by Thomson 
Financial 
Panel A : 
 Market capitalization / Total 

assets 
Expected EPS / Total Assets 

per share 
Eaxpected EPS Variation  / Total Assets 

per sahre 
 Mean Median S.D Mean Median S.D Mean Median S.D 
USA 1.55 1.13 1.37 0.10 0.08 0.09 0.018 0.012 0.026 
          
Germany 1,11 0,72 1,19 0,07 0,06 0,06 0,012 0,008 0,015 
Australia 1,47 1,06 1,36 0,11 0,08 0,10 0,017 0,010 0,036 
Canada 1,11 0,90 0,80 0,08 0,06 0,06 0,009 0,005 0,027 
France 0,99 0,70 0,93 0,07 0,05 0,04 0,009 0,007 0,012 
Italy 0,84 0,67 0,66 0,05 0,05 0,03 0,007 0,006 0,008 
Japan 0,84 0,64 0,68 0,04 0,04 0,03 0,006 0,004 0,007 
United 
Kingdom 

1,23 0,96 0,96 0,09 0,07 0,07 0,009 0,007 0,023 

Sweden 1,22 0,98 1,03 0,09 0,08 0,05 0,012 0,010 0,018 
Mean 1,10 0,83 0,95 0,075 0,061 0,055 0,010 0,007 0,018 

          
Brazil 0,96 0,77 0,72 0,14 0,09 0,37 0,021 0,015 0,031 
China 1,11 0,76 1,14 0,08 0,07 0,06 0,012 0,007 0,022 
Korea 0,77 0,55 0,80 0,08 0,07 0,06 0,012 0,008 0,021 
Hong-Kong 1,24 0,90 1,06 0,09 0,08 0,07 0,014 0,009 0,027 
Indonesia 1,36 0,82 1,58 0,13 0,11 0,10 0,015 0,013 0,028 
Malaysia 1,09 0,75 1,11 0,10 0,08 0,07 0,011 0,009 0,016 
Singapore 1,01 0,81 0,73 0,10 0,09 0,06 0,017 0,013 0,021 
Taiwan 1,27 0,97 1,02 0,11 0,10 0,08 0,012 0,008 0,031 
Thaïland 0,98 0,77 0,79 0,10 0,08 0,06 0,011 0,009 0,021 

Mean 1,09 0,79 0,99 0,103 0,086 0,103 0,014 0,010 0,024 
Panel B : 
 

Size Variation of  sales over  2 years in % 

Variation over 2 year of book 
value of equity in excess of net 

income in % 

Ratio of invetsment over 2 
years compared to 

depreciation allowances 
 Mean Mean Median S.D Mean Median S.D Mean Median S.D 
USA 7.72 0.39 0.25 0.51 0.10 -0.02 0.68 1.35 1.10 0.87 
 

          
Germany 6,91 0.22 0.16 0.31 -0.02 -0.08 0.33 1.14 1.02 0.63 
Australia 6,05 0.69 0.33 1.26 0.28 -0.06 1.27 2.04 1.30 2.70 
Canada 7,14 0.56 0.29 0.95 0.15 -0.05 0.67 1.88 1.42 1.72 
France 7,00 0.25 0.16 0.34 0.02 -0.08 0.41 1.22 1.12 0.69 
Italy 7,37 0.25 0.17 0.34 -0.08 -0.12 0.25 1.23 1.00 0.81 
Japan 7,21 0.13 0.10 0.17 0.01 -0.02 0.13 1.20 1.10 0.56 
United 
Kingdom 6,96 0.35 0.21 0.62 0.03 -0.11 0.73 1.26 1.02 0.93 

Sweden 6,77 0.31 0.20 0.47 -0.03 -0.13 0.52 0.99 0.90 0.58 
Mean 6,93 0.34 0.20 0.56 0.04 -0.08 0.54 1.37 1.11 1.08 

 
          

Brazil 7,65 0.43 0.35 0.35 -0.09 -0.16 0.55 1.71 1.50 0.93 
China 6,97 0.61 0.48 0.53 0.03 -0.04 0.23 2.48 2.19 1.58 
Korea 7,37 0.27 0.23 0.29 -0.02 -0.04 0.24 1.64 1.39 1.00 
Hong-Kong 6,93 0.51 0.34 0.69 0.13 -0.05 0.71 2.40 1.68 2.07 
Indonesia 6,32 0.51 0.41 0.41 -0.03 -0.09 0.56 1.88 1.63 1.16 
Malaysia 5,44 0.40 0.28 0.46 -0.01 -0.05 0.23 1.85 1.49 1.30 



Stock Prices And Implied Abnormal Earnings Growth 
 

203 
 

Singapore 5,83 0.45 0.34 0.50 -0.01 -0.07 0.35 1.90 1.51 1.25 
Taiwan 6,95 0.48 0.40 0.44 -0.05 -0.07 0.23 1.79 1.57 1.13 
Thaïland 5,63 0.34 0.25 0.36 -0.09 -0.14 0.32 1.66 1.38 1.25 

Mean 6,57 0.45 0.34 0.45 -0.02 -0.08 0.38 1.93 1.59 1.30 
 
   The accounting measures of past growth were selected based on the methodology inspired by Hribar 
and Yehuda (Hribar & Yehuda, 2008). Three basic variables were measured: the variation of sales over 
2 years in %, variation of book value of equity in excess of net income in%, and the ratio of investment 
over 2 years compared to past depreciation during these past years (measured by the items WS.Sales, 
WS.TotalCommonEquity, WS.NetIncome, and WS.CapitalExpendituresCFStmt 
WS.DepreciationDeplAmortExpense of Worldscope database from Thomson Reuters). According to 
the first and the third indicator, the emerging countries have experienced the sharpest growth. 
These variables measuring the past growth have been combined into a synthetic indicator which varies 
from 0 (lowest growth) to 1 (highest growth). The detailed calculation of this indicator is given in 
Annex. 
 
4. The Empirical Results 

We comment, in the first paragraph, the different level of association between market values, 
expected earnings and their expected variation while omitting the supposed impact of dividends. We, 
then, discuss the possible effects of the bias associated with used forecasts. Finally, we propose a series 
of estimates of the expected implicit rates of return derived from these association relations. 
4.1  Association between market values and expected earnings without taking into account dividends 

The estimation of the equation (7) requires a preliminary measurement of the rate r to calculate the 
abnormal earnings growth. Since this rate is not directly observable and that it intervenes in the 
calculation of expected earnings per share cum dividend, we initially ignore the impact of r ∙ DPS 	. 
Table 3 provides an estimate for 18 countries studied. Expected earnings per share for the next year are 
significantly associated with stock prices in all countries. The primary role of expected earnings in 
valuation is therefore general, even if the intensity of the association varies considerably (8.77 on 
average for emerging countries against 6.81 for the USA and 12.10 for other developed countries. 

The increase in earnings per share is significantly associated with market value in the case of 
developed countries but this is not always true in case of emerging countries (the coefficients are not 
significant for Brazil and Malaysia).The average of these coefficients is 15.63 for USA, 19.79 for other 
developed countries and 26.7 for emerging countries. 

The coefficient associated with the composite measure of growth are mostly negative and non-
significant in developed countries (-0.047 for the USA and on average -0.006 for others),with a notable 
exception of Japan (0.188). This coefficient is positive on average in emerging markets (0.200) but 
significant only for Hong Kong, Indonesia, Malaysia and Thailand. Note that according to the equation 
(5), the expected sign for this variable depends on that of the term h. It can be positive and negative 
according to the degree of persistence and depending on the rate of growth (c), speed (γ)and the ability 
to persist (δ) which characterize the value creation potential of the firm. When it is negative (positive), 
only the capitalization of the expected increase in the short-term earnings tends to over value (under 
value) the share and this factor has made the necessary correction. The empirical results suggest that 
during this period, growth in short terms earnings was not sustainable over a long period (except Japan, 
which displays very poor performance). In contrast, on average, in the emerging countries, the short-
term variation of earnings does not fully realize long-term growth potential. 

The coefficients of the variable size are significant in all countries. But it is negative in the USA  
(-0.022) and in Korea and positive in emerging countries (0.124) or other developed countries (0.079). 
The American sample is large and one that offers the greatest variety of business sizes. 
 
 
 
 
 
 
 



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Table 3. Association between market values, expected earnings and growth 
This table presents the estimated values of the coefficients and their T for a regression model whose dependent 
variable is market capitalization at year end normalized by total assets, and the independent variables are expected 
earnings per share for the coming year and expected earnings growth for the following year normalized by total 
assets per share and a synthetic accounting variable measuring the past growth. The size was introduced as a 
control variable. The regressions were carried out by country with dummies by period. The coefficients T were 
calculated from “heteroskedasticity consistent standard errors”. The study period extends from 2001 to 2008.The 
data come from Worldscope and IBES databases provided by Thomson Financial. The observations belonging to 
extreme percentiles for the dependent variable and the first two independent variables have been eliminated. 
Finally, we have conserved companies appearing at least three times during the period. 

  EPS1 EPS2-EPS1 Growth Rank Size   Number of 
Observations   b1 T b2 T b3 T b4 T R2 F 

USA 6.810 21.356 15.629 14.187 -0.047 -1.014 -0.022 -3.423 0.423 354.609 5 333 
             
Germany 12.922 15.080 32.073 5.353 0.040 0.416 0.092 6.495 0.751 158.052 588 
Australia 8.916 10.496 12.206 3.717 0.273 2.423 0.114 6.775 0.642 111.390 695 
Canada 8.085 15.259 8.533 6.033 -0.349 -3.772 0.073 6.599 0.545 71.331 667 
France 14.564 17.328 21.376 6.792 0.028 0.341 0.068 7.762 0.704 148.086 698 
Italy 13.253 17.161 23.849 5.985 0.071 0.931 0.054 4.579 0.760 84.716 307 
Japan 15.635 50.469 21.149 13.787 0.188 9.095 0.056 12.805 0.745 900.015 3 400 
United Kingdom 9.975 11.951 17.493 7.509 -0.102 -1.038 0.119 10.035 0.577 104.262 852 
Sweden 13.479 23.884 21.653 5.786 -0.196 -1.494 0.058 4.253 0.750 96.495 365 
Other developed 
countries 12.104  19.792  -0.006  0.079    7 572 

             
Brazil 4.729 4.475 1.384 0.695 0.114 0.836 0.162 5.514 0.436 13.862 209 
China 6.136 4.962 11.447 2.025 0.160 0.907 0.106 3.719 0.313 11.049 279 
Korea 9.325 8.367 6.084 2.828 0.147 1.105 -0.036 -1.826 0.601 33.479 256 
Hong-Kong 8.865 14.432 9.473 5.853 0.454 3.972 0.181 10.894 0.568 64.672 552 
Indonesia 10.333 9.111 9.736 3.336 0.326 2.280 0.158 4.835 0.801 70.107 203 
Malaysia 11.706 23.695 -0.412 -0.183 0.331 4.116 0.108 4.326 0.772 120.188 402 
Singapore 9.595 13.413 12.575 4.776 0.003 0.022 0.202 11.016 0.691 47.254 244 
Taïwan 10.048 27.407 8.152 6.129 0.042 0.649 0.099 7.136 0.821 173.904 430 
Thaïland 8.204 10.124 6.868 2.858 0.224 2.612 0.134 7.656 0.657 56.446 336 
Emerging 
countries 8.771  7.256  0.200  0.124    2 911 

 
4.2 Quality of forecasts and association of variables. 

The coverage of various stocks by financial analysts is certainly uneven in quantity and quality 
according to the countries concerned. It is not, therefore, clear that the EPS forecast reported by IBES 
constitute a measure of market expectations, endowed with a homogeneous quality. Table 4 provides a 
series of measures of forecast errors characterizing each country at the end of the period. The average 
absolute error represents 4.76% of average score in USA, 12.01% in other developed countries and 
14.42% in emerging countries. The quality of forecasts is significantly higher in the USA. The 
disparities among countries are strong: Italy and Brazil have the highest values, while Australia and 
Taiwan have the lowest. The average error is positive, suggesting that analysts are pessimistic before 
publication of earnings, either because they have been conducted by the management (“earning 
guidance”) or because they are encouraged not to displease the firms: 0.93% of average score in USA, 
2.95 % for other developed countries and 0.57% for emerging countries. However, disparities are very 
large among countries. The averages are thus negative for Australia and Japan and for more than half of 
emerging countries. It is possible that analysts’ behaviors are very heterogeneous. If during this period 
FD regulation has, for example, prompted financial analysts to no longer express an unfounded 
optimism to USA, the situation had been different in other countries. Therefore, it is possible that the 
market holds expectations for the coming earnings per share, in some cases exceed the forecast reported 
by IBES, and in other lower. The quality of estimates of association links between expected earnings 
and market value is affected. 



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205 
 

 Table 4. Forecast errors and initial optimism 
This table presents the forecast errors for earnings per share for the year studied. The errors are estimated from the 
available year end forecast. The values were normalized by total assets per share. The mean values provide an 
estimate of bias, that of absolute values a measure of precision. These mean values were divided by the ratio of 
expected EPS divided by total assets per share to obtain a measure of earnings in %. This estimate was preferred 
to the mean of relative errors, given the presence of low values for certain earnings per share. The initial optimism 
is measured by the ratio: difference between earnings per share forecast at the beginning of the year and EPS 
realized in the previous year, divided by total assets per share at the beginning of the year. The study period 
extends from 2001 to 2008.The data come from Worldscope and IBES databases provided by Thomson Financial. 
The sample is that used previously, except for the measurement of initial optimism which lack certain 
observations because of the lag of a year. 

 
Error = (EPS real- EPS expected) / Total 

assets per share  

EPS 
expected / 

Total assets 
per share 

Ratios compared to mean 
expected EPS  Initial optimism 

 Value Absolute value Value 
Mean Error 

/ Mean 
value 
Mean 

 
S.D 

Value 

 
Mean S.D Mean S.D Mean Mean S.D 

USA 0.09% 1.55% 0.46% 1.48% 9.68% 0.93% 4.76% 17.22% 35.23% 

          Germany 0.28% 1.50% 0.89% 1.24% 6.97% 4.05% 12.69% 20.05% 83.92% 
Australia -0.04% 1.97% 0.88% 1.77% 10.50% -0.39% 8.37% 20.46% 54.34% 
Canada 0.01% 1.24% 0.67% 1.05% 7.23% 0.18% 9.28% 14.44% 41.55% 
France 0.35% 1.74% 0.87% 1.55% 6.30% 5.57% 13.79% 10.53% 40.20% 
Italy 0.47% 2.55% 1.00% 2.40% 5.45% 8.63% 18.27% 5.56% 54.94% 
Japan -0.03% 0.77% 0.44% 0.63% 4.36% -0.75% 10.14% 20.47% 47.92% 
United Kingdom 0.21% 1.84% 0.96% 1.59% 7.91% 2.61% 12.09% 12.02% 30.50% 
Sweden 0.31% 1.76% 0.96% 1.50% 8.36% 3.72% 11.47% 16.79% 57.87% 
Other developed 
countries 0.20% 1.67% 0.83% 1.47% 7.13% 2.95% 12.01% 15.04% 51.40% 

          Brazil 0.24% 3.76% 1.88% 3.27% 10.57% 2.24% 17.82% 39.33% 267.16% 
China -0.11% 1.51% 0.86% 1.25% 7.44% -1.49% 11.60% 14.24% 34.24% 
Korea -0.01% 1.53% 1.00% 1.16% 7.32% -0.13% 13.68% 15.96% 38.80% 
Hong Kong 0.00% 2.91% 1.37% 2.57% 8.95% -0.05% 15.31% 14.35% 41.79% 
Indonesia -0.57% 4.23% 2.10% 3.71% 12.25% -4.63% 17.17% 16.97% 42.54% 
Malaysia 0.43% 4.00% 1.50% 3.73% 9.16% 4.68% 16.34% 13.91% 50.46% 
Singapore 0.51% 4.46% 1.48% 4.23% 9.38% 5.47% 15.84% 11.18% 41.84% 
Taiwan -0.15% 1.76% 1.05% 1.42% 10.76% -1.43% 9.75% 15.40% 29.62% 
Thailand 0.04% 1.87% 1.13% 1.50% 9.20% 0.45% 12.26% 16.80% 50.44% 
Emerging countries 0.04% 2.89% 1.38% 2.54% 9.45% 0.57% 14.42% 17.57% 66.32% 
        

The analysts’ behavior can vary according to the forecast horizon, with in the same country. 
More it is distant, more it is difficult to verify the acuteness and more it is easy to be optimistic. Bartov 
et al. (2002) suggest that analysts have an interest in optimism at the beginning of the year and then to 
revise gradually their forecasts to end the year in pessimistic situation. They accumulate the advantage 
of revealing flattering long term forecasts without exposing business leaders to announce disappointing 
realized results. To characterize a possible initial optimism, we have calculated the gap in the beginning 
of the year between the forecast earnings and last known earning per share, which is to say that of the 
past year. All these measured have been normalized by total assets per share. The averages shown in 
table 4 reflect a general optimism: the expected evolution expressed in % of average earnings for 
concerned countries is of 17.22% in USA, 15.4% in other developed countries and 17.57% in emerging 
countries. 

The presence of a bias in the beginning of a period and a possibly different bias at the end of the 
period doubly affect the measurement of the expected variation of earnings per share. If the forecast for 
one year is optimistic and the short-term pessimistic, the variation between the two overestimates the 
progression really expected by the market. If the short-term forecast is infected with a sense of 
optimism, but that of one year is little concerned the same variation under estimate the actually 



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206 
 

anticipated growth. Finally, if only the forecast in the short term is biased, the impact is identical on 
both variables: expected earnings and anticipated growth and these variables are found correlated. To 
isolate the most severe effects of these manipulations of forecasts, we are inspired by the method used 
by Tian (2009). We isolated in each country the forecast likely to be most affected by manipulation. To 
do this, we have used two criteria. First, the forecast (firm-year) must be initially optimistic (the 
expected earnings early in the year is higher than the earnings per share published last year). Second, 
the revision of the forecast during the period must be abnormally pessimistic. To determine this second 
point, we have regressed, for each country, the variation of the forecasts during the period (normalized 
by total assets per share) on the stock return over the same period in order to eliminate the impact of the 
information taken into account by the market. We, then, calculated the forecasting residuals and we 
considered that if these residuals were negative and positive initial optimism, then we were faced with a 
case which could be suspected of strong manipulation. Table 5 resumed the regression carried out in 
table 3 but by combining a dummy variable taking the value 1 in a suspected case of manipulation and 
variables related to earnings and variation of earnings. 

The results obtained in the American market are as per expectations. The suspected cases of 
manipulation of the forecasts are associated with a coefficient of valuation of expected earnings 
significantly higher (a difference of 1.634). The market “would correct” the under estimation by the 
analysts. The coefficients associated to expected variations of earnings is negative but nonsignificant  
(-0.025). The correction coefficients related to growth is negative (-0.177) but becomes significant. In 
contrast, the effects are negligible for other developed countries (with the exception of Germany). The 
lack of results may be due to the small size of samples or less elaborated forecasts management by 
analysts. 
4.3 Estimation of expected implied rate of return  and implied absnormal growth by country 
Taking into account the dividends per share in the estimation of equation (7) requires knowledge of the 
expected rate of return r. Moreover, if the theoretical model is verified; the same rate r should be equal 

to 
∙

+ −
∙

. To avoid having to assume zero dividends and thereby introducing a bias in the 
estimation of the expected implicit rate of return, we proceed iteratively until this implicit rate for the 
country concerned is equal to that which we used to calculate the abnormal earnings growth. The 
estimates of the rate r and g were obtained from the coefficients of β1 and β2, only. This allows avoiding 
taking into account the effects related to the manipulation of forecasts. It is likely that in these cases, the 
market “corrects” the analysts’ forecasts and the coefficient obtained would be affected by this 
correction (see (Easton & Sommers, 2007)). 

 The results obtained in paragraph 4.1 are confirmed in Table 6. In all countries expected earnings 
by the analysts is strongly associated with market value. The coefficients vary across geographic zones 
(7.27 in USA, 11.39 for other developed countries and 7.90 for emerging countries).The increase in 
earnings per share is strongly associated with market value in the case of other developed countries  but 
this is not always the case in emerging countries. In the case of developed countries, using a PEG1 
based heuristics helps to improve the analysis of the market value of securities, beyond the information 
provided by the forward PE ratio. These two determinants can lead to overvaluation and require 
correction (case of USA and Canada where the coefficients associated with the composite variable of 
growth is significantly negative) and more rarely to an undervaluation (Japan).The results are mixed for 
emerging countries. The information content of the expected abnormal increase in earnings per share 
appears more limited. The coefficients associated are much lower (not meaningful for Brazil). The links 
between market value and earnings are more difficult to identify solely from the next two years 
earnings per share forecast. The reason can come from lower quality financial analysis. But also, the 
values are certainly dependent on other factors describing the growth opportunities in long term. The 
historical measurement of the past growth is of little use (coefficients significant in 3 cases out of 9). 
The traditional valuation heuristics should therefore be handled with much more prudence in these 
environments. 

 
 

  

                                                   
1 It is not, here, expected earnings per share but a measure of abnormal growth. 



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207 
 

Table 5. Association between market values, expected earnings, growth and manipulation of forecasts 

This tables table presents the estimated values of the coefficients and their T for a regression model whose 
dependent variable is market capitalization at year end normalized by total assets, and independent variables are 
expected earnings per share for the coming year and expected earnings growth for the following year normalized 
by total assets per share and a synthetic variable measuring the past growth. The size was introduced as a control 
variable. The dummy variable Dm takes the value 1 if a manipulation index has been estimated. The regressions 
were carried out by country with dummies by period. The coefficients T were calculated from “heteroskedasticity 
consistent standard errors”. The study period extends from 2001 to 2008.The data come from Worldscope and 
IBES databases provided by Thomson Financial. The observations belonging to extreme percentiles for the 
dependent variables and the first two independent variables were eliminated. Finally, we have conserved 
companies appearing at least three times during the period. 

  EPS1 EPS1*Dm EPS2-EPS1 EPS2-EPS1*Dm Growth Rank Size   Number  
of Obs.   b1 T B1m T B2 T B2m T b3 T b4 T R2 F 

USA 7.466 21.679 1.634 2.859 17.299 13.712 -0.025 -0.009 -0.117 -2.279 0.028 3.521 0.463 433.489 5 533 

 
               

Germany 12.409 13.778 5.618 1.594 36.372 5.322 -27.435 -2.920 0.062 0.632 0.090 6.564 0.751 158.052 588 
Australia 9.320 10.590 -1.520 -1.092 12.076 4.345 -0.155 -0.013 0.251 2.234 0.113 6.831 0.642 111.390 695 
Canada 8.056 14.982 0.573 0.759 7.784 4.824 2.266 0.671 -0.333 -3.559 0.073 6.646 0.545 71.331 667 
France 14.431 16.952 -0.340 -0.304 22.804 6.355 -7.080 -1.317 0.034 0.422 0.065 7.556 0.704 148.086 698 
Italy 12.949 16.314 1.658 1.285 25.930 5.640 -8.797 -1.255 0.062 0.791 0.056 4.563 0.760 84.716 307 
Japan 15.510 47.160 0.694 1.293 22.000 13.032 -3.115 -0.930 0.187 9.076 0.057 12.252 0.745 900.015 3 400 
United Kingdom 10.070 11.782 0.072 0.075 16.733 5.910 3.082 0.701 -0.103 -1.059 0.120 10.163 0.577 104.262 852 
Sweden 13.431 23.827 0.118 0.099 21.988 5.282 -1.788 -0.297 -0.190 -1.511 0.057 4.233 0.750 96.495 365 
Other developed  
countries 12.022  0.859  20.711  -5.378  -0.004  0.079    7 572 

 
               

Brazil 4.210 3.481 0.929 0.837 -1.138 -0.235 3.661 0.683 0.121 0.880 0.151 5.332 0.436 13.862 209 
China 6.088 4.836 -0.426 -0.233 8.651 2.541 8.448 0.533 0.160 0.904 0.108 3.629 0.313 11.049 279 
Korea 9.549 8.959 -2.615 -2.061 7.916 2.855 -2.347 -0.754 0.150 1.163 -0.036 -1.839 0.601 33.479 256 
Hong Kong 8.447 14.082 2.908 2.256 9.213 5.516 -2.716 -0.535 0.467 4.172 0.187 11.351 0.568 64.672 552 
Indonesia 9.474 10.728 2.380 1.376 7.647 4.402 1.798 0.228 0.331 2.286 0.164 4.977 0.801 70.107 203 
Malaysia 11.734 20.009 -0.114 -0.151 -0.648 -0.255 0.717 0.173 0.330 4.006 0.108 4.292 0.772 120.188 402 
Singapore 9.590 14.592 2.080 1.165 12.042 5.283 -1.830 -0.230 0.039 0.335 0.209 11.209 0.691 47.254 244 
Taiwan 9.984 27.565 -0.152 -0.269 6.428 6.004 8.716 2.758 0.056 0.876 0.098 7.447 0.821 173.904 430 
Thailand 8.207 10.109 0.325 0.276 6.853 2.736 0.706 0.116 0.225 2.607 0.135 7.520 0.657 56.446 336 
Emerging countries 8.587  0.591  6.329  1.906  0.209  0.125    2 911 

 
 
The model appears to capture a hierarchy of expected rates of return, although estimates for 

emerging markets remain very imprecise, country by country. The estimates of expected rates of return 
are respectively of 10.9% for USA, 8% for other developed countries and 12.3% for the emerging 
countries. Within the last two zones, the estimates vary across countries. For developed countries, the 
expected returns are lowest in Japan (6.0%) and in the Eurozone (6.5% for France and 7% for 
Germany) and the highest in Canada (11.4%) and Australia (10.1%) Among emerging countries, Brazil 
(24.7%) and China (14.8%) topped. Malaysia (8.8%) , Taiwan(9.7%), Singapore (9.8%) and Korea 
(9.9%)  are in the tail. The implicit values of the parameter g which governs the abnormal earnings 
growth are strongly negative (-0.406 for USA, on average of -0.595 for developed countries and 1.013 
for emerging countries2 (-0.083 if we limit the extreme value to -1). It is interesting to note that no 
estimates approach the hypothesis advanced by Ohlson and Juettner –Nauroth, namely a positive value 
close to a long-term rate of growth. 

  

                                                   
2 This factor cannot be below -1, according to our model. No value appears significantly lower, except the case 
of Malaysia. 



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208 
 

5. Robustness Tests 
The valuation of assets depends in the model used on the discount rate required by the market. 

Initially, we study the effects of two factors associated in the literature to the discount rate, the book to 
market ratio and the size. Then, we take into account the differences in precision in the earnings per 
share forecast. On the one hand, we can assume that more the forecasts are imprecise, the higher the 
risk. On the other hand, the more forecasts are precise, the more consensuses of analysts is close to 
market expectations. In both cases the measures of association should be affected. We, then, assume 
that the coefficients of persistence (δ) and speed ( γ) that characterize this model may differ if the 
abnormal growth is positive , or if it is negative. We replicate the test on a sub-sample composed solely 
of positive expected variations. Finally, we conduct a direct estimate of the coefficient g which governs 
the dynamics of the abnormal growth in earnings per share and compare with the implicit estimates 
derived from the model. 
5.1 Implied rate of return and risk factors 

We classified the companies of each country into two subcategories, those whose studied factor is 
low and those with high studied factor. The same method was used for the Book-to-Market ratio and for 
the size. As these ratios vary country by country and year by year, we chose to classify by companies 
and not by firm-year to avoid introducing the bias related to the period. The classification is carried out 
according to the following protocol. For each country, firms in the sample 2008 were divided into two 
groups around the median of used indicator (BM ratio or size). The same companies were taken in 
2007. For those contained therein; the average ratio was performed for each of the sub-groups. If a 
company appears in 2007 and does not exist in the sample in 2008, it is classified in the sub-population 
to whom it is the nearest (the smallest distance from its indicator compared to the two averages). The 
classification is retained for the following. The same approach is repeated in 2006 and beyond. Thus, 
for each of the indicator (BM ratio or size), once a company is classified in her country as big or small. 
The classification has the advantage of being independent of years and the inconvenience of not taking 
into account a possible change in the characteristics of the company over the period. 

From table 7 we can see that companies with the ratio “book to market” high generally have a low 
coefficient associated with expected earnings (exceptions are Italy and United Kingdom for developed 
countries and China for emerging countries): 2.92  against 6.27   to USA, 8.40 against 9.73 for other 
developed countries and 4.19 against 7.72 for the emerging countries. The observation is consistent 
with two explanations: (i)  the PER are lower for these companies, (ii) the weight of PER is more 
reduced in the valuation of shares. The test does not make it possible to decide between these two 
reasons. The same observation can be made for the coefficient associated with the expected abnormal 
variation of earnings per share. We have 4.52 against 17.48 for the USA, 8.68 against 20.80 for other 
developed countries and 2.93 against 7.38 for emerging (with the exception of Italy and United 
Kingdom). The contribution of amended PEG in the valuation is certainly much reduced for these 
populations which probably contain many business of extremely poor performance. The expected 
implied rate of return is high for companies with the high “book to market” ratio in the three geographic 
zones. This hierarchy is consistent with the presence of a stronger risk factor for these sub-samples, 
although the rate obtained for US companies in high ratio seems extremely high (24.8%). Finally, the 
synthetic coefficient g, linked to persistence (δ) and the speed (γ) of abnormal growth is lower for 
firms of “book to Market” ratio high. This is consistent with the presence of fewer opportunities for 
growth, even in the existence of deceleration of expected abnormal earnings. 

Companies of big size as a general rule have a higher coefficient associated with expected earnings 
(the only exceptions are Australia and United Kingdom) :7.59 against 6.94 for  USA , 12.23 against 
10.30 for other developed countries and 8.64 against 6.59 for the emerging countries. The observation is 
compatible with two explanations: (i) the PER are higher for these companies, (ii) the weight of PER is 
greater in the valuation of shares. The same observation cannot be carried out for the coefficient 
associated with the expected abnormal variation of earnings per share. We have a smaller coefficient for 
large companies in USA (16.57 against 18.15) and the opposite in the other two zones (27.15 against 
15.52 for other developed countries and 12.36 against  5.52  for emerging), with  two exceptions  
Canada and Korea. It is possible that the U.S. sample contains relatively more small performing 
businesses, for which the market has more visibility on their future growth. The expected implied rate 
of return is greater for small businesses within the 3 geographic zones. This hierarchy is consistent with 
the presence of a risk factor related to the size, but the difference between the obtained rates for  US 



Stock Prices And Implied Abnormal Earnings Growth 
 

209 
 

companies is low (10.7% against  11.2%). Finally, the synthetic coefficient  g, linked to persistence (δ) 
and speed (γ) of abnormal growth is lower for small firms in other developed countries and emerging 
countries and slightly higher in USA. This is consistent with the presence of more numerous growth 
firms in the American sub-sample of small companies. 
5.2 Implied return and precision of forecasts 

The precision with which the analysts forecast the earnings per share can have a double influence 
on the parameter of the valuation model. On one hand, the more the analysts’ forecasts are accurate, the 
greater the correlation with market expectations. The measurement errors in dependent variables are 
reduced. On the other hand, the forecast error may be related to risk of asset/share. The more it is 
difficult to predict the earnings, the more high is the risk of a share. In this case, one can hypothesize 
that the rate of return required by shareholders should be higher. 

The forecast error is measured by the absolute value of the difference between the consensus of 
analysts at year and the final earnings reported by IBES, so benefiting from homogenous measurement. 
The difference is normalized, as is always the case, by the value of share in the beginning of year. For 
each country separately, the companies were ranked according to these normalized differences in two 
groups: those with high precision (values below the median) and those with a low precision. 
    The table 8 shows that in developed countries, the coefficient associated to expected earnings is 
higher when the precision is high (8.38 against 6.53 in the USA, 12.26 against 10.59 in other developed 
countries except the United Kingdom and Sweden). The differences are not significant in emerging 
countries. This may be due to a lower rate of return required by shareholders and therefore a higher 
PER or a better measure of expected earnings. The effect is less noticeable for emerging countries 
where in general the link between the market value and expected earnings by the analysts is less strong. 
The expected effect on the coefficient associated with the abnormal variation of earnings is more 
ambiguous. On the one side, if the forecast error is correlated with a risk factor, the lower rate of return 
increases the value of the coefficient. It is the same if the variation expected by the market is measured 
with less error. On the other hand, it is possible that the companies whose performances are most 
difficult to predict are those who benefit from more opportunities of growth. If these last are persistent, 
then the parameter g of the model is larger and the coefficient associated higher. But it is also possible 
that the reverse is true. We see in the table 8 that in the USA the coefficient is greater when the 
precision is high (25.31 against 16.31) and that it is smaller in other developed countries (17.58 against 
22.54 with the exception of Australia and Canada) and in most emerging countries. 
5.3 Direct estimates of the rates of persistence of the abnormal earnings growth 

One of the results presented concerns the dynamics of the “abnormal” growth of earnings per share. 
Contrary to the hypothesis advanced by Ohlson and Juettner-Nauroth (2005), the theoretical model 
developed in section 2 suggest that this abnormal growth does not necessarily follow a constant increase 
in the long term, but on the contrary guided by various dynamics of which some are compatible with 
limited persistence. The implicit measures that are derived from the estimates of the associated 
coefficients of expected earnings and from expected abnormal growth are all consistent with the 
hypothesis of limited persistence (the negative parameter g). In order to complement this empirical 
result, we proceeded to the estimation of an autoregressive model with a lag of one year for expected 
abnormal variation. The need to dispose of consecutive measurement has reduced the size of the 
sample. The table 9 provides the obtained results. 

It can be noted that for the most important sample, the USA, the two estimates of g are very close  
(-0.394 and -0.399).  In the case of other developed countries, the direct estimate is higher than implicit 
(-0.364 and -0.521), while remaining in the order of the magnitude not too far, except for Canada. In the 
case of emerging countries, the differences are more marked (-0.456 and -1.136) and especially the 
found implicit values are smaller than -1. As the implicit values of the g are obtained from the 
relationg = − , the errors contained in the implicit values most certainly come from an under 
valuation of the coefficient β  attached to the abnormal growth. The values found in emerging countries 
and Canada are low in comparison to those obtained in other countries, growth in earnings per share are 
less well anticipated by the consensus of the analysts. It is also noted that these samples are small in 
size. 
 

 



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210 
 

Table 6. Expected implicit rates of return as a function of market value, expected earnings and growth 

This tables presents the estimated values for the coefficients and their T for a regression model whose dependent variable is market capitalization at year-end normalized by total 
assets, and the independent variables are the earnings per share for the coming year and increase in expected earnings for the following year plus the income generated by the 
reinvestment of dividends and normalized by total assets per share, the same variable multiplied by a dummy variable indicating the suspected manipulation of forecast and a 
synthetic accounting variable measuring the past growth. The size was introduced as a control variable, as well as dummy variable for each reporting year. The regression were 
carried out by country , but taking into account all the years. The coefficients for year dummies are not reported. The coefficients T were calculated from “heteroskedasticity 
consistent standard errors”. The study period extends from 2001 to 2008.The data come from Worldscope and IBES databases provided by Thomson Financial. 

 EPS1 [EPS1]*Dm EPS2-EPS1+r.DPS1 
[EPS2-EPS1+r.DPS1 

]*Dm 
Growth Rank Size  

Implicites 
measures 

No. of 
obs. 

 1 T 1m T 2 T 2m T 3 T 4 T R
2 r g  

USA 7.265 21.071 1.697 2.810 17.883 14.174 -0.113 -0.039 -0.140 -2.720 0.022 2.843 0.472 10.9% -0.406 5 533 

 
                

Germany 11.849 12.093 6.057 1.677 34.672 5.255 -25.987 -2.825 0.024 0.250 0.088 6.296 0.747 7.0% -0.342 588 
Australia 8.564 9.436 -1.473 -1.155 13.690 4.659 1.101 0.103 0.172 1.551 0.117 7.548 0.667 10.1% -0.626 695 
Canada 7.894 14.504 0.608 0.782 7.478 4.376 2.823 0.823 -0.359 -3.738 0.073 6.585 0.544 11.4% -1.056 667 
France 13.862 16.126 -0.079 -0.064 23.977 6.650 -8.138 -1.483 0.016 0.199 0.063 7.482 0.710 6.5% -0.578 698 
Italy 11.536 13.738 2.882 1.916 29.489 4.583 -13.781 -1.952 0.018 0.236 0.054 4.574 0.772 7.3% -0.390 307 
Japan 15.252 44.817 0.703 1.241 22.295 12.253 -3.101 -0.944 0.180 8.772 0.057 12.348 0.746 6.0% -0.684 3 400 
United Kingdom 9.646 11.235 0.123 0.121 17.487 6.180 2.328 0.549 -0.164 -1.659 0.117 10.066 0.585 8.9% -0.549 852 
Sweden 12.539 22.766 0.211 0.177 23.422 5.558 -2.114 -0.332 -0.226 -1.818 0.054 4.132 0.763 7.0% -0.535 365 
Other developed 
countries 11.393  1.129  21.564  -5.859  -0.042  0.078   8.0% -0.595 7 172 

                 
Brazil 2.959 2.168 1.013 0.870 4.400 1.580 1.843 0.563 0.141 1.030 0.148 5.188 0.488 24.7% -0.673 209 
China 5.449 4.258 -2.071 -0.687 8.860 2.883 14.428 0.798 0.160 0.908 0.110 3.747 0.328 14.8% -0.615 279 
Korea 9.314 8.547 -2.574 -1.967 8.250 3.167 -2.282 -0.731 0.138 1.098 -0.037 -1.857 0.627 9.9% -1.129 256 
Hong Kong 7.652 12.866 2.325 1.574 11.551 6.691 -0.238 -0.044 0.432 4.031 0.188 11.488 0.598 11.2% -0.662 552 
Indonesia 8.870 11.636 1.684 0.962 8.740 4.383 4.698 0.672 0.284 1.980 0.152 4.844 0.831 10.2% -1.015 203 
Malaysia 10.925 17.689 0.253 0.281 5.415 2.278 -2.913 -0.707 0.353 4.279 0.113 4.620 0.775 8.8% -2.018 402 
Singapore 8.850 12.679 3.264 1.910 13.770 6.503 -6.916 -1.141 -0.016 -0.142 0.205 11.005 0.707 9.8% -0.643 244 
Taiwan 9.644 26.248 -0.438 -0.684 6.491 6.109 7.982 2.433 0.019 0.290 0.096 7.290 0.828 9.7% -1.486 430 
Thailand 7.428 9.397 0.610 0.501 8.501 3.643 -0.132 -0.022 0.204 2.419 0.136 7.691 0.668 11.9% -0.874 336 
Emerging 
countries 7.899  0.452  8.442  1.830  0.191  0.123   12.3% -1.013 2 911 



Stock Prices And Implied Abnormal Earnings Growth 
 

211 
 

Table 7. Expected implicit rates of return by country and risk factors 

This table presents the estimated values of the first two coefficients and their T for a regression model whose dependent variables is market capitalization at year-end normalized 
by total assets, and the independent variables are the expected earnings per share for coming year and expected increase in earnings for the following year plus the income 
generated by the reinvestment of dividends  and normalized by total assets per share, the same variables multiplied by a dummy variable indicating the suspected manipulation 
of forecasts and a synthetic accounting variable measuring the past growth. The size was introduced as a control variable, as well as dummy variables for each reporting year. 
The regression were carried out by country, but taking into account all the years. The coefficients T were calculated from “heteroskedasticity consistent standard errors”. The 
study period extends from 2001 to 2008.The data come from Worldscope and IBES databases provided by Thomson Financial. 

Panel  A : With partition of the samples according to the Book to Market ratio 

 Low BM ratio High BM ratio 

 EPS1 EPS2-EPS1+r.DPS1 Implicites measures 
Number of 

obs. EPS1 EPS2-EPS1+r.DPS1 Implicites measures 
Number of 

obs. 

 1 T 2 T r g 
 1 T 2 T r g  

USA 6.272 14.696 17.484 11.081 12.0% -0.359 3 338 2.920 12.139 4.524 6.368 24.8% -0.646 2 195 

               
Germany 10.963 9.225 40.292 5.225 7.2% -0.272 349 8.129 12.224 6.211 2.276 11.3% -1.309 239 
Australia 7.590 6.931 12.799 3.910 11.1% -0.593 405 5.241 6.735 4.502 1.552 16.7% -1.164 290 
Canada 6.555 9.101 8.079 3.615 13.1% -0.811 361 5.806 11.833 2.272 2.104 16.2% -2.556 306 
France 13.714 12.491 27.881 5.593 6.5% -0.492 386 8.201 13.285 7.279 3.650 11.1% -1.127 312 
Italy 8.745 13.028 6.761 2.575 10.6% -1.294 179 15.468 13.684 18.228 2.507 6.0% -0.849 128 
Japan 16.081 37.295 24.938 11.310 5.7% -0.645 1 848 9.177 24.815 9.647 6.354 9.9% -0.951 1 552 
United Kingdom 3.668 11.507 8.578 8.645 18.9% -0.428 440 6.865 6.412 15.764 5.360 11.5% -0.436 412 
Sweden 10.518 11.997 37.076 6.154 7.5% -0.284 188 8.287 14.176 5.544 3.153 11.2% -1.495 177 
Other developed 
countries 9.729  20.801  10.1% -0.602 4 156 8.397  8.681 

 11.7% -1.236 3 416 

               
Brazil 3.789 2.423 3.757 1.058 21.7% -1.008 117 0.067 0.090 3.325 1.432 53.9% -0.020 92 
China 2.229 1.212 6.951 1.614 25.2% -0.321 161 4.860 8.535 1.426 0.804 19.5% -3.409 118 
Korea 10.001 6.925 5.383 1.673 9.5% -1.858 146 4.491 4.880 5.087 3.763 18.4% -0.883 110 
Hong Kong 6.193 8.490 11.296 5.268 13.0% -0.548 313 4.364 10.192 1.597 1.221 21.3% -2.732 239 
Indonesia 9.884 11.678 10.855 4.274 9.2% -0.911 128 3.819 9.396 2.110 1.744 23.2% -1.810 75 
Malaysia 10.729 11.770 5.534 1.531 8.9% -1.939 240 4.789 12.720 -0.019 -0.162 nc nc 162 
Singapore 9.935 8.075 8.209 2.229 9.3% -1.210 137 3.748 6.704 5.624 3.276 20.4% -0.666 107 
Taiwan 9.949 16.932 6.161 3.874 9.5% -1.615 189 6.330 19.591 3.018 4.323 14.8% -2.097 241 
Thailand 6.808 6.206 8.278 2.279 12.7% -0.823 194 5.273 14.138 4.168 3.592 16.8% -1.265 142 
Emerging countries 7.724  7.380  13.2% -1.137 1 625 4.193  2.926  23.5% -1.610 1 286 

 



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212 
 

Panel  B : With partition of the samples according to size 

 Small Firms Big Firms 

 EPS1 EPS2-EPS1+r.DPS1 Implicites measures 
Number of 

obs. EPS1 EPS2-EPS1+r.DPS1 Implicites measures 
Number of 

obs. 

 1 T 2 T r g 
 1 T 2 T r g  

USA 6.936 13.418 18.152 11.131 11.2% -0.382 2 918 7.593 17.393 16.569 8.706 10.7% -0.458 2 615 

               
Germany 10.201 10.032 25.146 3.783 8.2% -0.406 341 12.122 6.710 53.316 4.529 6.4% -0.227 247 
Australia 10.401 9.885 11.123 3.281 8.8% -0.935 349 6.83 4.980 19.765 3.727 11.1% -0.347 346 
Canada 7.428 13.037 7.964 3.709 11.9% -0.933 343 8.568 8.473 6.417 2.218 10.8% -1.335 324 
France 11.919 17.888 17.179 4.353 7.6% -0.694 413 15.507 9.198 41.920 5.796 5.6% -0.370 285 
Italy 6.969 11.699 7.934 3.578 12.6% -0.878 156 14.737 16.977 17.979 2.903 6.3% -0.820 151 
Japan 13.674 33.516 19.878 10.399 6.7% -0.688 1 883 17.126 34.827 29.650 10.543 5.3% -0.578 1 857 
United Kingdom 10.406 7.473 13.069 3.739 8.7% -0.796 406 9.317 8.426 20.204 4.780 9.0% -0.461 446 
Sweden 11.389 10.511 21.894 4.138 7.7% -0.520 165 13.657 19.670 27.908 3.822 6.5% -0.489 200 
Other developed 
countries 10.298  15.523  9.0% -0.731 4 056 12.233  27.145 

 7.6% -0.578 3 856 

               
Brazil 0.931 0.688 2.895 1.208 44.9% -0.322 93 3.426 2.492 8.343 3.318 19.7% -0.411 116 
China 6.119 3.043 2.323 0.478 15.4% -2.635 145 6.956 4.098 8.221 2.200 12.5% -0.846 134 
Korea 9.063 4.045 11.000 3.411 9.9% -0.824 128 9.595 9.470 4.784 1.696 9.9% -2.006 128 
Hong Kong 6.695 7.945 8.402 5.016 12.9% -0.797 296 8.217 9.708 20.053 5.657 9.8% -0.410 256 
Indonesia 3.683 10.188 0.103 0.106 27.0% nc 95 10.327 13.454 12.168 5.190 8.8% -0.849 108 
Malaysia 8.849 13.668 4.298 1.926 10.7% -2.059 202 11.833 14.970 10.075 2.549 7.9% -1.175 200 
Singapore 8.275 10.099 12.690 5.711 10.4% -0.652 134 10.054 6.982 17.810 2.458 8.6% -0.565 110 
Taiwan 9.330 23.828 4.709 3.706 10.2% -1.982 245 10.089 16.081 9.468 7.369 9.1% -1.066 185 
Thailand 6.339 9.621 3.244 1.951 14.7% -1.954 195 7.272 4.758 20.317 4.391 10.6% -0.358 141 
Emerging countries 6.587  5.518  17.3% -1.403 1 533 8.641  12.360  10.8% -0.854 1 378 

 
 



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213 
 

Table 8. Expected implicit rates of return expected by country and forecast accuracy 

This table presents the estimated values for the first two coefficients  and their T for a regression model whose dependent variable is market capitalization at year-end 
normalized by total assets , and the independent variables are the expected earnings per share for the coming year and expected earnings growth for the for the following year 
plus the income generated by the reinvestment of dividends and normalized by total assets per share, the same variables multiplied by a dummy variable indicating the suspected 
manipulation of the forecast and a synthetic accounting variable measuring the past growth. The size was introduced as a control variable, as well as dummy variables for  each 
reporting year. The regressions were carried out by country, but taking into account all the years. The coefficients T were calculated from “heteroskedasticity consistent standard 
errors”. The study period extends from 2001 to 2008.The data come from Worldscope and IBES databases provided by Thomson Financial. 

 High Precision Low Precision 

 EPS1 EPS2-EPS1+r.DPS1 Implicites measures No. of obs. EPS1 EPS2-EPS1+r.DPS1 Implicites measures No. of obs. 

 1 T 2 T r g 
 1 T 2 T r g  

USA 8.378 11.686 25.307 7.988 9.3% -0.331 2 396 6.533 15.954 16.314 12.351 11.8% -0.400 3 137 

               
Germany 13.101 11.191 23.294 2.702 6.8% -0.562 321 10.364 8.198 39.355 4.784 7.5% -0.263 267 
Australia 9.459 11.584 29.451 8.031 8.4% -0.321 405 8.144 7.669 12.304 4.117 10.6% -0.662 309 
Canada 10.296 11.480 15.613 5.556 8.6% -0.659 392 6.627 9.628 6.200 3.391 13.4%   -1.069 275 
France 16.182 14.264 22.251 3.704 5.7% -0.727 391 12.510 11.214 23.693 5.046 7.1% -0.528 307 
Italy 12.670 23.010 3.279 1.558 7.7% -3.864 154 10.775 13.050 33.554 7.035 7.5% -0.321 153 
Japan 16.325 26.352 16.722 5.282 5.8% -0.976 1 713 13.671 27.589 21.966 10.201 6.6% -0.622 1 687 
United Kingdom 8.235 9.232 12.775 2.191 10.4% -0.645 440 9.920 7.437 17.683 5.780 8.7% -0.561 412 
Sweden 11.808 17.732 17.280 6.546 7.6% -0.683 190 12.726 15.213 25.594 4.813 6.9% -0.497 175 
Other developed 
countries 12.260  17.583  7.6% -1.055 4 006 10.592  22.544 

 8.5% -0.565 3 585 

               
Brazil 4.172 2.136 2.780 0.521 21.0% -1.506 105 1.971 1.202 6.594 2.142 26.8% -0.299 104 
China 0.836 0.224 -1.165 -0.096 nc nc 130 8.890 9.437 8.733 2.859 10.2% -1.018 149 
Korea 13.323 7.408 3.946 0.802 7.3% -3.377 121 8.994 5.980 6.987 3.205 10.3% -1.287 135 
Hong Kong 7.945 7.099 19.689 4.594 10.1% -0.404 301 7.426 11.607 9.397 5.138 11.7% -0.790 251 
Indonesia 8.194 9.205 4.133 1.903 11.5% -1.983 115 8.482 7.935 9.436 3.154 10.6% -0.899 88 
Malaysia 11.351 18.135 6.274 2.196 8.4% -1.809 214 10.947 11.801 5.581 1.537 8.7% -1.961 188 
Singapore 10.690 8.751 14.396 4.371 8.4% -0.743 137 7.443 8.694 14.479 8.105 11.1% -0.514 107 
Taiwan 9.167 19.838 9.557 7.870 9.9% -0.959 215 10.023 18.455 5.154 3.615 9.5% -1.945 215 
Thailand 7.915 7.917 7.184 3.123 11.4% -1.102 181 7.345 6.358 9.696 2.737 11.8% -0.758 155 
Emerging countries 8.177  7.422  11.0% -1.485 1 519 7.947  8.451  12.3% -1.052 1 392 

 



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Table 9. Direct estimates of the rate of persistence of abnormal earnings growth 

This table presents the estimated values of the coefficients and their T for a regression model whose dependent 
variable is expected variation of abnormal earnings EPS2-EPS1+r.DPS1, normalized by total assets per share, and 
the independent variable is the same variable but shifted by one period. The sample is identical to that of table 
11.The estimates of cost of capital have been included. The coefficients T were calculated from 
“heteroskedasticity consistent standard errors”. The study period extends from 2001 to 2008.The data come from 
Worldscope and IBES databases provided by Thomson Financial. 

EPS2-EPS1+r.DPS1   Table 11 
Number of 

observations 
1 T R

2 g g implicite  
USA 0.606 24.945 0.460 -0.394 -0.399 3 165 

 
 

Germany 0.556 9.056 0.367 -0.444 -0.306 413 
Australia 0.601 5.504 0.450 -0.399 -0.676 490 
Canada 0.595 5.635 0.334 -0.405 -0.910 360 
France 0.617 11.492 0.410 -0.383 -0.544 477 
Italy 0.624 11.729 0.461 -0.376 -0.375 209 
Japan 0.519 19.169 0.310 -0.481 -0.587 2 177 
United Kingdom 0.806 11.008 0.557 -0.194 -0.394 538 
Sweden 0.772 9.934 0.585 -0.228 -0.377 243 
Other developed countries 0.636   

-0.364 -0.521 4 907 

      
Brazil 0.605 9.289 0.415 -0.395 -0.263 111 
China 0.404 4.643 0.231 -0.596 -1.520 137 
Korea 0.466 4.360 0.255 -0.534 -1.313 130 
Hong Kong 0.688 12.156 0.567 -0.312 -0.458 345 
Indonesia 0.738 9.349 0.459 -0.272 -0.621 120 
Malaysia 0.540 5.709 0.355 -0.460 -2.934 253 
Singapore 0.579 7.804 0.314 -0.421 -0.835 158 
Taiwan 0.439 8.639 0.352 -0.561 -1.518 193 
Thailand 0.450 6.979 0.331 -0.550 -0.766 189 
Emerging countries 0.545   

-0.456 -1.136 1 636 
 

6. Conclusion 
The model of the type AEG (for example, (Ohlson & Juettner-Nauroth, 2005), (Ohlson & Gao, 

2006)) provide a parsimonious way of valuing shares by referring to two variables: expected earnings 
per share and its expected “abnormal” growth. This paper shows that in the context of an international 
comparison, estimates of these two variables obtained from two years forecasts prepared by financial 
analysts (source: IBES) are significantly associated with the  market values, at least in developed 
countries. In the latter case, the expected earnings per share in 2 years has an information content that 
complements a forecasting year. This observation is less evident in the case of the most of emerging 
countries. 

The theoretical model that we developed suggest that a valuation based on only these two variables 
can lead to an under-valuation or to over-valuation according the type of growth experienced by the 
companies. Using a synthetic measure based on past accounting data, we show that in some countries 
(for example USA, Canada), a model of type AEG can lead to an over valuation of companies who 
have experienced a strong growth in the recent past. The past dynamics cannot be prolonged over a long 
period and a negative correction term is applied to these companies. In contrast, for others, the growth 
has not yet led to an increase in earnings per share, enough to account for all the value creation potential 
of these firms. In most of the emerging countries but also for certainly different reasons in Japan, a 



Stock Prices And Implied Abnormal Earnings Growth 
 

215 
 

positive corrective term is proposed. The study outlines the limitation of AEG models to explain the 
stock market values. 

The results suggest that the abnormal growth of earning per share is unlikely to perpetuate   by 
following a constant pace of progress as was initially suggested by Ohlson and Juettner-Naurauth. On a 
regular basis, the process that seems to best describe the expected evolution of this variable is 
autoregressive in nature with limited persistence. The estimates for developed countries are coherent on 
average (around 0.6 to USA and somewhat less for other developed countries). They remain very 
inaccurate in the case of emerging countries, but still very low. By suggesting using a long term rate of 
growth, O J-N contributes to propose specification of the models AEG strongly over estimating the 
values of shares. In addition, by accepting these more complex dynamics for the expected variation of 
abnormal earnings per share, we can deduce using the models AEG implicit values for the rate of return 
expected by investors. The results emphasize that these estimates remain consistent with the various 
commonly recognized factors of risk. Finally, we conclude with a practical remark: the combined use of 
two heuristics that practitioners frequently use in valuation, namely the PE ratio and PEG ratio is 
justified in the context of developed countries and unfortunately less powerful in emerging countries. 

 
Acknowledgement 

This research was supported by Lille School of Finance (Faculty of Finance, Bank and Accounting–
USDL and SKEMA Business School).The authors have received  countless advices and comments from 
Eric De Bodt. Errors and omissions remain their own responsibility. 
 
References 
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Bartov, E., Givoly, D., Hayn, C. (2002). The rewards to meeting or beating earnings expectations. 
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Brief, R.P. (2007). Accounting Valuation Models: A Short Primer. ABACUS, 43(4), 429-437. 
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ISSN: 2146-4138 
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216 
 

 
Annex 

Method of calculation of the synthetic variable of growth and company rank according o their 
stage of growth 

The	synthetic	variable	y:	is	defined	by:	

y , =
x , , − x ,

σ ,
	

With	

x =
Sales
Sales

− 1	

	

x =
Equities − Equities − Net	Income −Net	Income

Equities
	

	

x =
Capital	Expenditures + Capital	Expenditures

Depreciation + Depreciations
	

	
 
We have truncated their values using the fifth percentile as minimum and ninety fifth percentile as a 
maximum, the reference populations are all profitable firms of the country concerned. I n order to 
aggregate them; we calculated their values centered and reduced by country. The sum of the variable 
refers to synthetic growth. 
 
Companies are then classified each year t as a function of this synthetic variabley.Their rank is 
normalized by the number of observations of the year and noted R , .  In order to take into account the 
persistent phenomenon, we have preferred an aggregate measure over two years.:	RC , = (R , +
R , )/2. Finally, to facilitate interpretation, we calculated : 1 − RC , .