FINANCIAL PERFORMANCE CHARACTERISTICS OF
SUCCESSFUL LISTED REAL ESTATE COMPANIES
Executive Summary
In this paper, we hypothesize three main determinants of firm value for real estate companies to be growth,
profitability and leverage and investigate a total of 11 different company specific characteristics as
potential indicators of superior performance. We find that successful real estate companies are generally of
larger size and command attractive market valuation relative to their underlying book value. They are
usually profitable and are more likely to take advantage of positive financial leverage effects, contributing
to higher sustainable growth and profitable growth in the longer term. In addition, the financial variables
that influence successful performance are largely similar for all countries and regions, but differ in degree
and in some cases the influence works in the opposite direction. This indicates a potential gain in portfolio
diversification across the global real estate markets.
1.
Background and Motivation
This paper examines two issues with respect to the key financial performance characteristics of
successful listed real estate companies in 24 countries and three continents over the period 2000 through
2006. First, we test the hypothesis that the three main determinants of firm value for real estate companies
are growth, profitability and leverage; and second, we investigate which financial variables are most
important in explaining and predicting the level of two measures of stock market success, i.e., the Sharpe
ratio and Jensen’s alpha. Our motivation is to search for common elements between companies with a view
to determine whether there are similar characteristics that successful listed real estate companies share.
As an extension of the prior real estate studies such as Ling and Naranjo (2002), Bond et al. (2003)
and Hamelink and Hoesli (2004) which examine cross-sectional differences in commercial real estate
market returns, this paper adopts a corporate finance -based approach. Specifically, the literature on valuebased planning, which regards shareholder value creation as the central premise, provides motivation and
intuition for our study. Under this theory, two main determinants of value; namely profitability and growth
are identified (Varaiya et al. 1987). The key idea here is that corporate management seeks to create
shareholder value by ensuring that the warranted market value, MV, of the equity capital invested in the
firm by the shareholders exceeds the book value, BV, of equity. Therefore, value is created for shareholders
if MV>BV or MV/BV>1; value is destroyed if MV<BV or MV/BV<1 and value is sustained if MV=BV or
MV/BV=1. In addition, financial leverage is included as the third value driver in our expanded model
because many real estate firms are capital intensive (Chiang et al. 2002). Only Ooi and Liow (2004)’s work
has some similarities to the present study. They examine the performance of real estate stocks of seven East
Asia developing countries over 1992-2002 using Sharpe Index (SI). Employing OLS panel regression
technique, they find that size, book-market value ratio, capital structure and market diversification have
significant influence on the firms’ SI performance.
We investigate the key financial (including operational, asset investment and financing) of the
listed real estate companies in a multiple number of countries from 2000-2006, benchmarked against
equivalent performance indicators for other major domestic, regional and world real estate companies. In
reference to the value-based planning, four main corporate performance equations (i.e. market-book ratio,
sustainable growth, profitability, capital structure as endogenous variables) will be modeled using systemequation approach. Finally, we will link the stock market and financial performance of the companies and
investigate main key financial performance indicators of successful real estate companies.
In identifying the contribution of successful listed real estate companies to their shareholders and
the real estate industries in Asia, Europe and North America, our paper brings four contributions to the
extant literature. First, we extend the knowledge of the performance of global listed real estate companies
which are expected to become an increasingly important component of institutional investors’ asset
portfolios. Although not intended to be part of this paper, our additional review on their risk-return and
correlation dynamics confirms the observation made by Eichholtz and Koedijk (1996) that the global teal
estate securities market has developed significantly over the last decade, both in the market capitalization
and the number of listed companies, and is set to continue to grow. Second, a larger and more varied
sample of 336 listed real estate companies from 24 countries and 3 regional markets allow us to examine
whether any findings about one or two countries from other business fields can be generalized to real estate
companies that have their underlying asset value in the direct property sector. Our approach recognizes
there are probably significant regional, country and sector factors contributing to differences in the
financial performance of successful real estate companies since real estate is mainly a local business. Third,
an added contribution of this paper is that a system multivariate regression methodology is employed to
link the four key performance measures simultaneously. Compared to the Ordinary Least Square (OLS)
regression approach, the use of the iterated two-stage least square (IT2SLS) system estimation considers
the endogeneity and interactions of the financial variables and is able to produce more efficient coefficient
estimates regarding the three main determinants of firm value, i.e., growth, profitability and capital
1
structure. Finally, we measure real estate company performance from a portfolio manager’s viewpoint by
linking two conventional risk-adjusted return indicators, i.e. Sharpe index (SI) and Jensen’s alpha (JI) to the
financial performance indicators. The results from a binary logit model will provide evidence of strong
predictive power of those indicators of successful real estate companies. To the authors’ best knowledge,
this is probably the first empirical study which investigates the main determinants of the value creation in
global listed real estate companies. Results from this study can provide interesting and practical insights to
global investors and fund managers in including successful real estate companies to their investment
portfolios.
Our results suggest successful real estate companies are generally of larger size and command
attractive market valuation relative to their underlying book value. They are usually profitable and are more
likely to take advantage of positive financial leverage effects, contributing to higher sustainable growth
rates (SGRs) and profitable growth in the longer term. Moreover, the financial variables that influence
successful performance are largely similar for all countries and regions, but differ in degree and in some
cases the influence works in the opposite direction. This indicates a potential gain in portfolio
diversification across the global real estate markets. Our results provide practical insights to global
investors and fund managers in including successful real estate companies into their investment portfolios.
This paper is organized as follows. Section 2 presents the real estate company dataset and key
financial indicators. Section 3 explains the empirical procedures that include the system modeling and logit
regression. Then, we discuss the results and implications in Section 4. The article ends with a summary of
the key findings and highlights the limitations of they study in Section 5.
2.
Data sources and sample characteristics
The dataset includes 336 public real estate investment and development firms for the period 2000
through 2006. Based on market capitalization (in US dollar term) as of December 31, 2006, all real estate
companies available from Osiris database1 were ranked in the descending order. This gives rise to a total of
1
Osiris is a comprehensive database of listed companies, banks and insurance companies around the world.
In addition to the income statement, balance sheet, cash flow statement and ratios it contains a wide range
of complementary information such as news, ownerships, subsidiaries, M & A activities and ratings. Osiris
contains information on 38,000 companies from over 130 countries including 30,000 listed companies and
8,000 unlisted or de-listed companies.
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336 real estate companies that were continuously listed from 2000-2006 and had an equity capitalization of
at least USD 1 million from 24 securitized real estate markets across Asia, Europe and North America.
Among them, The US property equity market is the largest real estate securities market in the world. Most
property companies are structured as tax exempt real estate investment trusts (REITs), but real estate
operating companies (ROECs) that do pay tax also exists. The British real estate securities market is by far
the largest in Europe, both in numbers and size. It is followed by France and Netherlands, which has the
second and third largest market capitalization in Europe. Lastly, Japan is the largest and one of the very two
developed (the other market is Australia) real estate securities markets in Asia. It has long history of real
estate companies. Other markets such as Hong Kong and Singapore have established track record of
property investment and development companies in their respective stock markets.
The financial data and ratios of the 336 companies were derived from Osiris. The stock market
index and return data comes from Datastream. We are aware that our sampling procedure will introduce
survivorship bias; however this might be unavoidable to be in line with the objective of assessing
successful real estate companies over the longest period (i.e. 2000-2006) that the data permit.
Each of the 336 real estate firms has a market capitalization of at least USD 1.5 million as of
Financial Year 2006. Exhibit 1 provides the 2006 key financial indicators of the companies grouped by
country. The largest real estate company (by market capitalization) is Japan’s Mitsubishi Estate which has a
MV of USD35736.26 million as of December 2006. The next 14 largest global listed real estate companies
are: Sun Hung Kai Properties (HK), Cheung Kong Holdings (Hong Kong), Mitsui Fudason (Japan), British
Land (UK), Metrovacesa (Spain), Sacyr Vallehermoso (Spain),Sumitomo Realty & Development (Japan),
Rodamco Europe (Netherlands), Gecina (France), CapitaLand (Singapore), Henderson Land Development
(Hong Kong), Sino Land (Hong Kong), Hang Lung Properties (Hong Kong) and Liberty International (UK)
(Exhibit 1 here)
We include eleven financial indicators to assess and identify financial successful real estate
companies. These financial indicators are popular measures of real estate firms’ success considered by
analysts as well as extensively documented in the literature. They are briefly discussed below. Exhibit 2
provides the mean, median and standard deviation of these variables over the entire sample period (20002006).
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(a)
Market- to Book ratio (MV/BV)
The use of this ratio is in line with the findings of Fama and French (1992)’s book-to market ratio
(BV/MV), who show that the BV/MV of individual stocks can explain cross-sectional variation in stock
returns. This ratio (or its reciprocal) is widely used as a measure of a firm’s growth opportunities through
value-based planning models which argue that at maximum, a firm’s management chooses strategies that
produce the largest MV, given BV.
(b)
SIZE (MV)
Company size is another popular variable that explain stock return. Large firms are typically more
diversified and less risky. The seminal work of Fama and French (1992) shows that stock returns are
related to size and positively related to book-to-market ratio. Research by Laporta et al. (1997) reveals that
the relation between size and stock returns is similar for financial and non-financial firms. The variable is
measured here as the natural log of market capitalization and is used as a controlled variable in the
simultaneous equations.
(c)
Sustainable growth rate (SGR)
Corporate finance theories suggest that companies need to trade off with the inherent constraints
with respect to policies regarding leverage and dividend payout in planning for its future growth. For
example, a real estate company may decide to retain a greater portion of its earnings in its reserve and
finance new projects or acquisitions out of the retained earnings. By doing so, the firm may not need to
increase its gearing and at the same time avoid give a signal to the market about its investment plan. The
sustainable growth rate (SGR) is the highest growth rate a firm can maintain without increasing its financial
leverage. This indicator was proposed by Higgins (1977) to relate a firm’s growth with its financial stability.
This rate depends on the firm’s return on equity (ROE) and earnings retention ratio (ERR); i.e.
SGR=ROE*ERR. The higher the sustainable growth, the more financial flexibility the firm has to expand
through organic growth or acquisitions. Liow (1998) finds the actual growth rate (AGR) of many Singapore
real estate companies were higher than their SGR. Consequently, these real estate firms relied on increasing
financial leverage to sustain their high growth.
(d)
Return on equity (ROE)
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This is a summary indicator that links a firm’s “net earnings’ from its Profit & Loss account and
“shareholders’ fund” from its balance sheet. It thus measures the profitability of investment from the
perspective of shareholders. It is a key driver of SGR and is measured as the product of ROA and debt ratio
(i.e. ROE = ROA* DEBTR)
(e)
Debt ratio (DEBTR)
Capital structure measured as the ratio of long-term debt to total assets (DEBTR) is a proxy for
leverage. While the expected common stock returns are positively related to the ratio of debt to equity after
controlling for beta and firm size due to positive leverage effect, Fama and French (1992) find a negative
relation between debt and firm value. At higher levels of debt, the shareholders-bondholders’ agency
problems arise when debt is risky predict a negative relationship between leverage and profitability. This
ratio is highly relevant for many real estate firms which are very capital intensive
(f)
Cost of equity (K e)
The cost of equity (K e) is the minimum return that shareholders of the firm demand consistent
with the riskiness of their investment in the firm. Value is created (destroyed) when a firm adopts strategies
that produce a return which exceeds (falls short of) its cost of equity. It is measured by the earnings yield
(reciprocal of price-earning ratio) of the firm in Year t.
(g)
Spread
The magnitude of the percentage spread, (ROE – Ke), expected to be earned, implies that a
positive ROE alone is not a sufficient indicator of a profitable business. Instead, positive spread (i.e. ROE –
K
e
>0) implies profitable growth and hence the higher a firm’s value (MV/BV); where negative spread
implies unprofitable growth.
(h)
Fixed tangibility (FA/CA)
Tangible fixed assets reduce the likely costs of financial distress, so firms with significant tangible
assets (such as real estate companies) would be expected to employ more debt. It is defined in this study as
ratio of fixed assets to current assets. Further, those firms with higher fixed tangibility such as real estate
firms could have lower profit level due to higher operating leverage.
(i)
Earnings retention ratio (ERR)
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This ratio is measured as (1- dividend payout ratio). Finance theory postulates that when a firm
pays out less of its earnings as dividend (i.e. lower payout ratio), it will accumulate more of its retained
earnings (higher ERR) to build up its reserve. Consequently, this firm is able to accrue higher SGR (as
SGR =ROE*ERR) and requires lesser debt financing (DEBTR) to supports its expansion or asset
acquisitions.
(j)
Actual growth rate (AGR)
This represent the annual growth level experienced by a firm in Year t, and is measured as the %
change in total assets from year to year.
(k)
Profitability (ROA)
Return on assets (ROA) is an asset utilization ratio that indicates how effectively or efficiently a
firm uses its assets. Usually the higher the ROA, the higher the growth potential of the firm
(Exhibit 2 here)
3.
Methodology
With the selected financial variables, our empirical procedures comprise three steps.
First, we test the hypothesis that the three main determinants of firm value (measured by the
MV/BV ratio) for real estate companies are growth, profitability and leverage. We run four separate
simultaneous equations on the pooled data from 2000 to 2006 for the 24 countries, 3 continents and 6 years
using Iterated Weighted Two-stage Least Square (ITW2SLS) estimation method. This system approach is
necessary because financial variables are related in ways that makes it difficult, if not impossible, to
determine causality, and that they are often simultaneously determined by each other. The correlation
results reported in Exhibit 3 indicate that many of the financial variables are reasonably correlated. A
specific example is that in corporate finance, it is known that capital structure is endogenous to
performance; sustainable growth is endogenous to performance, capital structure and dividend policy.
Compared to the OLS regression approach, the use of the ITW2SLS system estimation method considers
the endogeneity of the financial variables and is able to generate more efficient coefficient estimates.
Before estimation, we have manually checked the financial data for outliers. They include zero
values for variables such as MV and earnings yield; negative values for MV/BV, DEBTR, ERR and
FA/CA ratios as well as extraordinarily large observations for any of the variables (defined as more than
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three standard deviations away from the mean). Consequently, a number of firms for which these
observations occurred have been removed from the samples, so the final test sample contains 1131 firmobservations (48.1% - with complete data for all financial indicators) during the period 2000-2006. The
four simultaneous equations of the system model are specified as:
(1)
MV/BV = f (MV, SGR, ROE – K e, FA/CA)
(2)
SGR = f (MV, MV/BV, FA/CA, ROA, DEBTR, ERR)
(3)
ROA = f (MV, FA/CA, DEBTR, SGR)
(4)
DEBTR = f (MV, FA/CA, ROA, AGR, ERR)
(Exhibit 3 here)
Equation (1) tests the three main determinants of firm value, MV/BV, to be (a) the spread (ROE –
K e), (b) sustainable growth rate (SGR) and (c) fixed tangibility (FA/CA). There are two main predictions:
(a) the higher the spread, ceteris paribus, the higher a firm’s MV/BV; (b) the higher the SGR, ceteris
paribus, the higher a firm’s value, MV/BV, if the spread is positive. Conversely, the higher the SGR, the
lower will be a firm’s value if the spread is negative. The MV variable is used as a controlled variable in
the four equations. Equation (2) is an expanded SGR model that includes the main determinants of Higgins
(1977)’s original SGR model (i.e. ROA, DEBTR and ERR) and extra variables (MV/BV, FA/CA and MV).
The profitability (Equation 3) and capital structure (Equation 4) variables are interrelated; with Chiang et al.
(2002) find that capital structure is positively related to asset but negatively with profitability for Hong
Kong property and construction firms. The trade-off model postulate profitable firms can use more debt to
derive more tax shelter benefits. In contrast, the pecking order theory suggests that more profitable firms
have less need for external debt financing.
Second, we use two conventional measures of investment performance, i.e. the Sharpe index (SI)
and Jensen’s alpha (JI) to measure firms’ success from the capital market perspective. The SI is closely
associated with the CAPM and measures excess return per unit of risk. A positive JI indicates the
occurrence of positive abnormal returns. Similarly, negative JIs indicate returns below the level anticipated
by the CAPM according to the risk-free rate, systematic risk and market risk premium. With the possible
indicators for identifying financially successful real estate companies (as explained above), an OLS
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regression model investigates which financial variables are most important in explaining the level of the
two measures of stock market success, i.e. SI and JI. The two estimating equations are:
(a)
SI = f {intercept, LNMV, LN(MV/BV), SGR, ROA, D/E, SPREAD, DREG1, DREG2,
DREG4}
(b)
JI = f {intercept, LNMV, LN(MV/BV), SGR, ROA, D/E, SPREAD, DREG1, DREG2,
DREG4}
Where: DREG i = dummy variable if firm i is in Group X, X = 1 (Asia-Pacific developed /matured
markets: Australia, New Zealand, Japan, Singapore, Hong Kong), 2 (Asia-Pacific developing markets:
Malaysia, Thailand, Philippines, Indonesia and China), 3 (European markets: UK, France, Germany, etc) or
=0, otherwise; Intercept is the SI (or JI) mean values for Group 4 (North Americas markets: Canada and
USA)
Finally, we estimate two binary logit models to predict whether each measure of financial
performance will beat the average value of JI (SI) for the firms in the sample. In the JI model, the
dependent variable is a dummy variable representing one if JI is positive and zero otherwise. In the SI
model, the dependent variable is a dummy variable representing one if the value of SI is greater than the
average for the final 170 firms (average SI is 2.1522) and zero otherwise. The logit model predicts correctly
when the predicted probability for a firm, derived from the log of the odds, is greater than 0.5 and the
dependent variable for the firm is one. Similarly the model predicts correctly when the predicted
probability is less than or equal to 0.5 and the dependent variable for the firm is zero.
4.
Results
The simultaneous regression estimation results are reported in Exhibits 4 -7. For each equation,
the regressions are run for the overall pooled sample, 19 country- pooled models, 3 regional- pooled
models and 6 yearly - pooled models. This approach hopes to test the robustness of the results.
(Exhibits 4 to 7 here)
After controlling for the endogeneity and simultaneity of firm value, sustainable growth rate,
profitability and capital structure, we find significant and important co-movements in the variables as well
as the directions of influence in most cases are consistent with the various hypotheses. Another observation
8
is that the financial variables that influence the performance are largely similar for all countries, regions
and different periods; but differ in degree and in some cases the influence works in the opposite direction.
This indicates a potential gain in portfolio diversification across the global real estate securities markets.
Firm value (represented by the MV/BV ratio) is positively influenced by the market capitalization
(SIZE) significantly except Sweden, Norway and France where the coefficients are insignificantly negative.
That is to say, larger real estate firms are able to capture higher stock market valuation, evidence which
appears inconsistent with that of Fama and French (1992). Contrary to the overall sample that documents a
positive relationship between the sustainable growth rate (SGR) and firm valuation, the influence is
significantly negative for Germany and France; insignificantly negative for the USA, Thailand and Sweden;
and insignificantly positive for Philippines, Norway and China. Similarly, spread is found to have a
significantly positive effect on firm value for the overall sample. However, the coefficient is insignificantly
negative in Thailand, Switzerland, Norway, Germany, China and Australia implying that in these real estate
securities market, unprofitable growth (i.e. negative spread) has a negative effect on firm value. In contrast,
the influence of the spread variable is significantly positive in the USA, UK, Spain, Singapore, Netherlands,
Malaysia, Japan, Hong Kong, France, Finland and Belgium implying that (highly) profitable growth of real
estate firms in these markets contribute significantly to higher stock market valuation. Finally, tangibility
has a moderately negative impact on firm valuation since higher operating leverage (due to higher fixed
asset such as property ownership) reduces firm value. This negative coefficient holds except for the USA,
Sweden, Malaysia and China.
Of the six financial variables that are hypothesized to affect the SGR, the most influencing
variables are firm valuation (significantly positive), profitability (significantly positive) and earnings
retention ratio (significantly positive). These positive effects are not surprising based on the SGR literature.
The majority of the individual ERR coefficients are significantly positive, suggesting that the higher the
ERR (the lower the dividend payout ratio), the higher the SGR. This is consistent with the observation that
many high-growth real estate companies usually pay low or no dividends to their shareholders and instead
use these retained earnings to invest in positive NPV projects which in turn contribute to higher SGR of the
companies. The profitability coefficients for 10 countries are significantly positive confirming that higher
SGR can be achieved from higher profitability; in other cases the coefficients are mostly positive but
9
insignificant. Finally, in 6 of the 22 firms’ MV/BV coefficient, we confirm the significant and positive
relationship between MV/BV and SGR. However, this result is not conclusive as the remaining 16
countries (regions) coefficients are either insignificantly positive or insignificantly negative, indicating that
the difference in the dynamic relationship between firm value and sustainable growth needs to be exploited
For Asian and European real estate firms, the influence of SIZE and SGR on profitability is both
significantly positive indicating larger and higher growth real estate firms are associated with higher return
on assets (ROA); these two coefficients are also positive and yet statistically insignificant for the NorthAmerican real estate firms. Tangibility also has a positive and significant effect on the profitability
performance for Asian and North-American firms; the positive effect is nevertheless weaker for European
firms. Finally, for real estate firms in all three regions, profitability is consistently and negatively
influenced by the capital structure (represented by DEBTR). That is to say, higher debt level contributes
negatively to return on assets. This result is not surprising as borrowing interests are the first charge and
reduces the profit of the business. Contrary to other countries, higher debt level contributes to higher
profitability in France and Australia.
Lastly, the results reported in Exhibit 7 clearly indicate that the negative and significant
relationship between profitability and capital structure exists for real estate firms from the three regions.
Two notable exceptions are the Philippines and Australia where their DEBTR coefficients are significantly
positive. Firm size has a positive effect on borrowing by European and Asian real estate firms. Whilst the
positive coefficient is significant for the European firms, it is statistically indistinguishable from zero for
the Asian firms. Hence, in general, larger real estate firms are able to borrow more (in percentage term of
total assets) compared to smaller firms. In general, both the tangibility and actual growth rate variables
have a positive effect each on capital structure. Specifically, Asian and North-American real estate firms
have an average positive and significant AGR indicating that higher growth (in total assets) requires higher
borrowing. In contrast, the average positive effect of the tangibility variable is not clear in the regional and
country analyses since many of the coefficients are either insignificantly positive or insignificantly
negative ; only Norway, China and Australia have a significantly positive debt ratio coefficient indicating
that tangible assets (/properties) contribute to borrowing level because of the collateral role it plays. Finally,
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although there is a negative relationship shown between the ERR and DEBTR, this negative relationship is
statistically indistinguishable from zero in many instances.
In summary, our system modeling and analyses have reasonably established the main financial
characteristics of successful real estate companies. In particular, successful (measured by higher firm value,
MV/BV) real estate companies are usually larger and are associated with higher sustainable growth and
positive spread as well as higher fixed tangibility (in percentage term). In addition, these companies are
usually profitable and nevertheless maintain a higher plough back (earnings retention) ratio as well as able
to borrow more (in percentage term of total assets) to support their continued growth. Our empirical work
also reveals the financial variables that influence successful real estate company performance are largely
similar for all countries, regions and in different periods, but differ in degree and in some cases the
influence works in the opposite direction. This indicates a potential gain in portfolio diversification across
the global real estate markets. However, some country results must be interpreted with cautions. This is
because no meaningful results can be obtained from countries like Switzerland, Finland, Norway and
Germany etc with only up to three companies. They are probably not going to provide adequate parameter
stability due to the too few firms within the samples.
For the capital market analysis, .the source data are the 336 firms for the last three years (2004 2006). In addition, those firms with one or more missing values for the variables gathered were excluded.
This leaves us with a total of 170 firms (50.6%) with complete data for the 3-year period. The regression
results are reported in Exhibit 8. The intercepts represent a test of the hypothesis that the mean SI (JI) for
Group 4 countries is zero. The other DREG coefficients represent tests for significant differences in the
means for SI and JI against the means for Group 4. Focusing on the financial variables, the results indicate
that five indicators (MV, MV/BV, SGR, ROA and DEBTR) are significant (up to 5 percent level) in
explaining successful JI performance. The SI results indicate that two indicators are statistically significant
in contributing to successful real estate company performance: market capitalization of equity (MV) and
profitability (ROA). Overall the results provide strong evidence that large size and highly profitable real
estate companies have on average superior investment performance. Other factors such as capital structure
(DEBTR ratio), sustainable growth rate (SGR) and profitability (ROA) also contribute to the real estate
firm’s stock market success.
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(Exhibit 8 here)
Finally, the results for the two logit models reported in Exhibit 9 provide reasonably strong
evidence of the predictive power of the defined indicators of successful real estate companies. They predict
correctly in about 84 percent of the cases with respect to JI and about 77 percent for SI. The binary logit
models indicate that successful real estate companies (i.e. with positive JI and above-average SI
performances) are generally of larger size and command attractive market valuation relative to their
underlying book value. They are profitable and are more likely to take advantage of positive financial
leverage effects, contributing to higher SGR and profitable growth in the longer term.
(Exhibit 9 here)
One last comment is in order regarding the impacts of current financial/liquidity crisis on
corporate financial performance. As the global financial landscape has changed considerably since 2006,
one question that may be of interest to readers is how well these successful real estate companies are doing
now. Some guesses are that a number of these firms are probably in great financial difficulties coupled
with their unfavorable market valuations (in term of MV/BV). Some are probably even on the verge of
collapse – particularly in regard to the negative financial leverage effect. Under these difficult market
conditions, one view is that these companies have to re-think and re-manage their sustainable growth rates
(SGRs) in relation to the firms’ growth and financial stability; i.e. while struggling to improve their return
on asset (ROAs) through more efficient asset utilization, they should probably pursue a zero-growth (or
even negative-growth) strategies so as to cut down /minimize their debts. By doing so, they hope to
maintain/improve the bottom lines as well as reduce their financial and bankruptcy risks due to high
financial gearing. With more data available, this research can be extended to evaluate and compare the
financial characteristics of the successful real estate companies before and after the present financial crisis
and derive important lessons that can be learnt in regard to the corporate financial management under crisis
situations.
5.
Conclusion
To conclude, we find that successful real estate companies (i.e. with positive JI and above-average
SI performances) are generally of larger size and command attractive market valuation relative to their
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underlying book value. They are usually profitable and are likely to take advantage of positive financial
leverage effects, contributing to higher SGR and profitable growth in the longer term. Moreover, the
financial variables that influence successful performance are largely similar for all countries and regions,
but differ in degree and in some cases the influence works in the opposite direction. This indicates a
potential gain in portfolio diversification across the global real estate securities markets. This information is
very useful for international investors and portfolio managers to understand better the dynamics of growth
and value creation in real estate companies. With the increasing significance of securitized real estate as
property investment vehicles for international investors to gain real estate exposure globally (Worzala and
Sirmans, 2003), this study on global real estate company performance is timely and has significant
implications for ongoing international real estate investment strategies and academic research in the
financial performance dynamics of listed real estate companies. It would probably generate a lot of interest
among both academics and practitioners, alike – in terms of determining common characteristics of listed
property around the world.
Some limitations of this study are also noted. First, in order to include as many as real estate firms
possible and at the same time ensure that the time period is long enough for any meaningful conclusion (s)
to be drawn, we adopt a compromised time span of seven-year (2000-2006) bearing in mind that a longer
time-series of financial data would help improve the power of analysis. Second, only real estate firms that
are continuously listed during the entire period are included. Like most other studies, our sampling
procedure is thus subject to survivorship bias (which is largely unavoidable) and care must be taken to
interpret the results generated from the study. Third, there is large number of missing as well as negative
ratios especially for some variables like DEBTR, ERR and earnings yield, resulting in only 48.1 percent of
the usable sample. Finally, we might not have included all important financial and stock market
performance indicators in the study.
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14
Exhibit 1
Key Financial Indicators of Global Real Estate Companies (as of FY2006)
Continent
Country
Asia
AUSTRALIA
10
CHINA
35
57
Number of firms
1575.77
212.80
1769.77
130.56
1407.17
157.71
52.72
171.74
1052.40
144.42
667.51
1591.01
1135.68
761.42
3966.68
755.91
2397.31
4391.38
956.20
3399.55
1460.47
655.71
1363.18
1902.87
782.28
346.79
2324.53
360.53
2103.56
216.72
4875.28
119.43
60.26
343.40
1916.46
219.04
1253.92
1693.92
1332.76
1182.64
5632.93
1795.01
2677.52
5931.34
1480.41
12869.99
2017.94
762.60
1493.64
3239.22
846.34
784.30
564.53
815.32
CANADA
1
US
19
AVERAGE
20
1998.90
INDONESIA
4
JAPAN
23
MALAYSIA
53
NZ
1
31
PHILIPPINES
North America
Book equity
('million)
2846.41
559.28
2774.73
268.69
5143.52
274.86
53.80
320.91
2281.49
351.31
1487.50
3572.76
1974.11
2914.39
8494.19
2509.00
6132.03
7384.23
3134.06
21704.18
3604.26
1797.28
2555.98
5481.37
2626.69
1371.11
HONGKONG
Europe
Total assets
('million)
SINGAPORE
18
THAILAND
17
AVERAGE
249
AUSTRIA
1
BELGIUM
4
FINLAND
2
FRANCE
4
GERMANY
3
ITALY
1
NETHERLANDS
3
NORWAY
2
SPAIN
3
SWEDEN
3
SWITZERLAND
2
UK
39
AVERAGE
67
Current Liabilities
('million)
Fixed Assets ('million)
270.75
17.36
196.37
19.72
136.98
10.10
7.67
13.19
103.62
56.19
83.20
78.71
158.90
114.12
858.32
82.98
271.95
970.10
198.20
1114.18
288.80
44.71
342.36
376.94
39.44
73.42
583.37
235.02
251.93
97.96
1259.62
102.17
0.74
77.61
442.49
110.79
316.17
150.25
188.18
360.57
643.08
519.39
191.15
490.61
235.30
4639.88
578.28
553.83
159.47
725.83
91.11
81.77
2399.78
178.60
1832.18
92.16
3486.29
270.28
29.20
210.98
1487.15
216.91
1020.35
2888.07
1850.32
1189.40
5113.82
1983.91
5157.32
7271.72
2990.73
16129.81
3535.06
1676.39
2336.04
4343.55
2582.53
596.29
56.43
86.44
1589.41
Market capiNet profit after tax
talization ('million)
('million)
Source: derived from Osiris and Datastream
15
Exhibit 2
Descriptive statistics of key financial ratios for Global Real Estate Companies: 2000-2006
Financial variables
Market capitalization (‘m) Sustainable growth rate Return on equity Debt ratio Spread (ROE‐ cost of capital) Cost of capital (earnings yield) Earnings retention ratio Tangibility (Fixed assets / current assets) Growth (change in total assets) Market‐to‐book value ratio Return on capital No of valid
observations
2246 2063 2213 1890 1698 1718 1979 2278 Mean
Median
Std deviation
795.99 0.0202 0.0505 0.2999 ‐0.0057 0.0829 0.7961 9.0942 128.72 0.0292 0.0540 0.2754 ‐0.0031 0.0610 0.8990 2.2183 2645.48 0.3003 0.1692 0.1857 0.1422 0.0808 0.2767 21.3987 2271 2195 2304 0.2756 1.3092 0.0220 0.0492 0.8675 0.0245 5.2517 1.8303 0.6256 Source: derived from Osiris and Datastream
Exhibit 3
Correlation analysis of financial ratios: 2000-2006
Correlation
MV
SGR
ROE
DEBTR
SPREAD
EP
ERR
FACA
GTA
MB
MV 1 SGR 0.082 1 ROE 0.274**
0.358*** 1 DEBTR SPREAD EP ‐0.248** 0.0391 ‐0.158 1 0.365*** 0.203 0.655*** ‐0.471*** 1 ‐0.175 0.124 0.234* 0.435*** ‐0.581*** 1 ERR ‐0.704*** ‐0.007 ‐0.178 0.221* ‐0.342*** 0.249** 1 FACA ‐0.071 0.087 0.126 ‐0.223* 0.127 ‐0.028 ‐0.138 1 GTA 0.166 ‐0.070 0.364*** 0.201 0.173 0.170 ‐0.130 0.046 1 MB 0.839*** 0.047 0.310** ‐0.305** 0.499*** ‐0.309** ‐0.675*** 0.065 ‐0.018 1 ROA ‐0.027 0.168 0.703*** ‐0.478*** 0.574*** 0.019 0.007 0.375*** 0.131 0.126 Notes: ***, **, * - indicates two tailed significance at the 1, 5 and 10% levels respectively
16
Exhibit 4 Results of system equation estimation (Equation 1: Market-book ratio, MV/BV)
Sample (No of pooled observation) Intercept Size ALL (1131) ‐0.9661 (‐13.22***) 0.1599 (12.50***) USA (63) ‐0.4844 (‐1.95*) ‐0.3452 (‐3.34***) ‐2.4694 (‐7.71***) UK (158) Thailand (60) Switzerland (8) Sweden (18) Spain (18) Singapore (66) Philippines (58) Norway (12) Netherlands (11) Malaysia (154) Japan (90) Hong Kong (180) Germany (7) France (19) Finland (10) China (128) Belgium (17) Australia (37) Asia (778) Europe (289) N America (64) 2000 (130) 2001 (147) 2002 (147) 2003 (161) 2004 (175) 2005 (193) 2006 (178) ‐1.3077 (‐5.44***) 0.4861 (0.71) ‐2.2761 (‐6.03***) ‐0.5651 (‐2.49**) ‐2.1257 (‐6.90***) 1.9706 (2.98***) ‐0.6261 (‐2.88***) ‐1.3718 (‐7.46***) ‐1.2180 (‐5.94***) ‐1.3363 (‐8.38***) ‐2.8213 (4.85***) 0.6907 (1.57) ‐3.1708 (‐9.17***) ‐1.6770 (‐3.20***) ‐1.2675 (‐6.48***) ‐1.0050 (‐2.69***) ‐1.1882 (‐12.56***) ‐0.6118 (‐6.63***) 0.4758 (‐1.91*) ‐0.8787 (‐3.62***) ‐1.0128 (‐3.99***) ‐1.0083 (‐4.56***) ‐0.7466 (‐4.09***) ‐1.0057 (‐6.83***) ‐1.0542 (‐7.12***) ‐1.0987 (‐5.91***) Sustainable Growth 0.7965 (4.30***) By country 0.1901 ‐0.5980 (3.60***) (‐1.17) 0.0288 1.1383 (1.63*) (2.87***) 0.5983 ‐0.9357 (8.24***) (‐1.57) 0.2376 0.8572 (7.95***) (2.17*) ‐0.0597 ‐0.2922 (‐0.54) (‐0.30) 0.3600 0.6956 (6.58***) (2.09**) 0.0821 1.0471 (2.34**) (2.25**) 0.3166 2.0821 (5.04***) (1.05) ‐0.2518 1.1273 (‐1.04) (1.11) 0.0959 1.4291 (2.78**) (2.79**) 0.2102 0.8948 (4.87***) (2.67***) 0.2275 0.6743 (7.34***) (1.88*) 0.1392 1.3622 (5.86***) (3.52***) 0.6140 ‐10.1704 (10.11***) (‐6.07***) ‐0.0042 ‐9.6919 (‐0.07) (‐3.16***) 0.5028 ‐0.6344 (9.05***) (‐0.85) 0.4381 1.0430 (4.58***) (1.18) 0.1844 ‐0.3989 (5.93***) (‐0.49) 0.1959 ‐0.5322 (2.87***) (‐0.57) By continents 0.2059 0.8747 (12.132***) (3.72***) 0.0919 0.8895 (6.26***) (2.92***) 0.1838 ‐0.5451 (3.42***) (‐1.07) By Year 0.1490 0.8098 (3.52***) (1.49) 0.1688 3.3528 (3.81***) (3.26***) 0.1665 ‐0.1558 (4.14***) (‐0.28) 0.1157 1.4061 (3.50***) (3.02***) 0.1341 2.2088 (5.29***) (5.53***) 0.1817 ‐0.0355 (6.94***) (‐0.10) 0.2050 0.2825 (6.84***) (0.58) Spread Tangibility System
weighted R2
1.7879 (9.40***) ‐0.0016 (‐1.95*) 0.272
2.8744 (3.79***) 1.1871 (3.92***) ‐0.1156 (‐0.27) 0.0004 (0.32) ‐0.0021 (‐2.03**) ‐0.0276 (‐2.87***) 0.472
‐0.3937 (‐0.63) 1.2338 (1.24) 1.9409 (5.15***) 5.0240 (5.79***) 1.8961 (1.21) ‐0.0486 (‐0.07) 1.8579 (2.07*) 1.8514 (3.75***) 2.3080 (4.30***) 1.1084 (3.31***) ‐0.7436 (‐1.23) 3.8528 (1.89*) 1.7298 (3.32***) ‐0.3026 (‐0.57) 1.7517 (3.39***) ‐0.0767 (‐0.07) ‐0.0120 (‐2.66**) 0.0023 (1.64) ‐0.1299 (‐4.13***) ‐0.0388 (‐2.83***) ‐0.0021 (‐0.08) ‐0.0092 (‐1.15) ‐0.0054 (‐2.01*) 0.0017 (0.71) ‐0.0051 (‐1.82*) ‐0.0098 (‐3.05***) ‐0.1440 (‐3.16) ‐0.0058 (‐1.11) 0.0015 (1.08) 0.0845 (1.22) ‐0.0024 (‐1.46) ‐0.0122 (‐1.43) 0.972
1.7367 (7.24***) 1.3787 (5.18***) 2.9092 (3.83***) ‐0.0096 (‐4.40***) ‐0.0014 (‐2.00**) 0.0046 (0.33) 2.5139 (3.95***) 0.2487 (0.38) 1.8569 (3.51***) 3.1759 (5.78***) 2.6770 (6.08***) 1.1471 (3.22***) 1.0222 (2.03**) ‐0.0041 (‐1.39) ‐0.0091 (‐1.97**) ‐0.0040 (‐1.15) ‐0.0018 (‐0.74) 0.0008 (0.82) ‐0.0016 (‐0.86) ‐0.0021 (‐0.96) 0.288
0.576
0.260
0.901
0.473
0.472
0.374
0.774
0.329
0.492
0.423
0.972
0.464
0.975
0.176
0.749
0.220
0.296
0.362
0.463
0.254
0.213
0.217
0.358
0.440
0.299
0.317
Notes: Estimation using weighted two-stage least square (W2SLS); ***, **, * - indicates two tailed significance at the
1%, 5% and 10% levels respectively
17
Exhibit 5
Results of system equation estimation (Equation 2: Sustainable growth rate: SGR)
Sample (No of pooled observation) Intercept Size MV/BV Tangibility Profitability Debt ratio Earnings retention (ERR) System weighted R2 ALL (1153) ‐0.0187 (1.04) 0.0022 (1.03) 0.0175 (3.91***) 0.5922 (7.72***) 0.00019 (0.01) 0.0622 (3.17***) 0.103 US (63) UK (158) Thailand (60) Switzerland (8) Sweden (18) Spain (18) Singapore (66) Philippines (58) Norway (12) Netherlands (11) Malaysia (154) Japan (90) Hong Kong (180) Germany (7) France (19) Finland (10) China (128) Belgium (17) Australia (37) 0.1519 (0.57) ‐0.0231 (‐0.88) ‐0.1093 (‐0.72) 0.0351 (0.64) ‐0.0965 (‐1.36) 0.2339 (0.60) 0.1570 (2.01**) ‐0.1024 (‐2.62***) ‐0.6250 (‐2.56**) ‐0.0025 (‐0.01) 0.0070 (0.11) 0.1205 (1.06) 0.0497 (0.91) NC ‐0.0276 (‐0.74) ‐0.4752 (‐1.74***) ‐0.0793 (‐1.31) ‐0.0833 (‐0.76) 0.1311 (0.64) ‐0.0005 (‐0.03) 0.0036 (1.14) 0.0509 (2.08**) ‐0.0124 (‐1.56) 0.0650 (1.06) ‐0.0619 (‐1.09) ‐0.0237 (‐2.56**) 0.0073 (1.73*) 0.0567 (1.89*) ‐0.0066 (‐0.22) 0.0138 (1.20) ‐0.0067 (‐0.53) ‐0.0018 (‐0.35) NC 0.0025 (0.87) 0.0739 (2.50**) 0.0141 (1.49) ‐0.0083 (‐0.70) ‐0.0044 (‐0.19) ‐0.0156 (‐0.53) 0.0380 (2.71***) ‐0.0426 (‐1.35) 0.0042 (0.16) 0.0215 (0.27) 0.1618 (1.68*) 0.0231 (0.83) ‐0.0110 (‐1.18) ‐0.0531 (‐0.54) 0.1814 (1.79*) 0.0294 (1.40) ‐0.0019 (‐0.06) 0.0493 (3.92***) NC ‐0.0102 (‐0.85) ‐0.0948 (‐1.50) 0.0116 (1.44) ‐0.0504 (‐0.80) ‐0.0122 (‐0.34) 0.8106 (1.33) 0.5154 (4.43***) 0.7312 (1.72*) 3.0451 (17.47***) 0.6131 (0.36) ‐0.9140 (‐0.89) 0.0002 (0.01) 1.5128 (5.24***) 1.4667 (2.60**) 0.8133 (0.62) 0.0676 (0.18) 0.9458 (0.74) 0.3043 (2.52**) NC 0.5576 (4.02***) 0.0413 (0.10) 0.6140 (3.55***) 0.924 (1.69*) ‐0.073 (‐0.13) 0.0327 (0.32) ‐0.0019 (‐0.05) 0.0635 (0.48) 0.0384 (4.05***) 0.5681 (1.01) 0.2044 (1.07) ‐0.1543 (‐1.41) 0.0567 (0.66) 0.4982 (1.83*) 0.00088 (0.01) ‐0.0982 (‐1.12) ‐0.0692 (‐0.63) ‐0.1916 (‐2.66**) NC 0.0215 (0.37) ‐0.0124 (‐0.07) ‐0.0867 (‐1.55) 0.2202 (2.41**) ‐0.2907 (‐1.13) ‐0.0892 (‐0.38) 0.0783e (3.80***) ‐0.1264 (‐1.37) 0.01541 (1.87*) 0.4616 (2.27**) 0.2387 (1.85*) 0.1547 (3.02***) 0.0543 (1.88*) 0.1937 (2.29**) ‐0.0139 (‐0.07) 0.0236 (0.87) ‐0.0211 (‐0.21) 0.0780 (2.27**) NC 0.0597 (2.36**) 0.1427 (5.52***) 0.0733 (3.21***) 0.1138 (1.70*) 0.0359 (0.33) 0.037 0.393 0.267 0.967 0.414 0.274 0.219 0.446 0.596 0.399 0.064 0.028 0.225 NC 0.853 0.807 0.233 0.700 0.061 Asia (778) Europe (289) N. America (64) 0.0322 (1.39) ‐0.0766 (‐3.35***) 0.0248 (0.11) 0.00031 (0.11) 0.0030 (1.11) 0.00060 (0.04) 0.0186 (3.54***) 0.0430 (4.29***) ‐0.0083 (‐0.31) 0.4749 (5.02***) 0.5681 (5.49***) 0.7249 (1.26) ‐0.0910 (‐3.15***) 0.0733 (0.79***) 0.0213 (0.21) 0.0367 (2.37**) 0.1180 (7.92***) 0.0415 (0.22) 0.079 0.428 0.035 2000 (130) 2001(147) 2002 (147) 2003 (161) 2004 (175) 2005 (193) 2006 (178) 0.0377 (0.66) 0.0047(0.15) ‐0.0425 (‐0.68) 0.0054 (0.09) 0.0578 (1.39) ‐0.0708 (‐1.21) ‐0.0222(‐0.64) ‐0.0052 (‐0.75) ‐0.00093 (‐0.24) 0.0087 (1.23) 0.0019 (0.32) ‐0.0122 (‐2.61***) 0.0143 (2.31**) ‐0.0047 (‐0.98) 0.0118 (0.92) 0.0248 (3.77***) ‐0.0071 (‐0.56) 0.0408 (3.20***) 0.0519 (4.59***) 0.0029 (0.19) 0.0137 (1.28) 0.00006 (0.45) By Country 0.00005 (0.17) ‐0.00019 (‐0.98) ‐0.0030 (‐1.19) ‐0.0011 (‐2.74**) ‐0.00013 (‐0.31) 0.0438 (1.86*) ‐0.0037 (‐0.98) 0.00047 (0.32) ‐0.0036 (‐1.17) 0.0011 (1.09) ‐0.00035 (‐0.57) ‐0.00038 (‐0.44) 0.00066 (1.14) NC ‐0.00043 (‐1.61) 0.00054 (2.41**) ‐0.0017 (‐0.27) 0.00040 (0.84) 0.00003 (0.02) By continent ‐0.00018 (‐0.54) ‐0.00005 (‐0.40) 0.000098 (0.26) By Year 0.00013 (0.27) 0.00035 (0.89) 0.00008 (0.15) 0.00012 (0.29) ‐0.000003 (‐0.02) 0.000059 (0.15) ‐0.000055 (‐0.17) 0.6093 (2.27**) 0.6690 (4.55***) 0.2414 (1.32) 0.4061 (1.68*) 0.2997 (1.53) 0.4430 (1.87*) 0.9890 (5.17***) ‐0.1008 (‐1.53) ‐0.0246 (‐0.80) ‐0.0243 (‐0.44) ‐0.0208 (‐0.40) 0.0706 (1.68*) ‐0.0838 (‐1.41) 0.1567 (3.64***) 0.0978 (2.47**) 0.0398 (1.91*) 0.0355 (0.95) 0.0417 (1.17) 0.0711 (2.31**) 0.0752 (1.66*) 0.0644 (3.01***) 0.123 0.226 0.025 0.136 0.173 0.086 0.232 Notes: Estimation using weighted two-stage least square (W2SLS); ***, **, * - indicates two tailed significance at the 1%, 5% and 10% levels respectively, NCnon-convergence
18
Exhibit 6
Results of system equation estimation (Equation 3: Profitability: ROA)
Sample (No of pooled observation) Intercept Size ALL (1131) 0.0159 (3.66***) 0.0048 (6.90***) USA (63) 0.0529 (3.06***) 0.0281 (2.05***) 0.0538 (2/.06**) 0.0084 (1.15) 0.1074 (1.09) 0.0730 (1.48) 0.1547 (3.02***) ‐0.0017 (‐0.21) 0.0063 (0.05) 0.0708 (0.84) 0.0040 (0.04) 0.0409 (4.99***) ‐0.0071 (‐0.38) 0.0492 (0.83) ‐0.0092 (‐0.22) 0.0612 (1.25) ‐0.0099 (‐0.43) 0.0330 (1.20) ‐0.0472 (‐1.13) 0.0002 (0.09) 0.0029 (1.42) 0.0105 (2.16**) 0.0016 (1.71) 0.0030 (0.31) ‐0.0035 (‐0.51) 0.0473 (2.47***) 0.0017 (1.05) 0.0082 (0.48) ‐0.0011 (‐0.07) 0.0103 (4.57***) ‐0.0003 (‐0.32) 0.0088 (3.66***) ‐0.0001 (‐0.02) 0.0006 (0.16) 0.0191 (2.15**) 0.0090 (2.20**) 0.0061 (1.43) 0.0113 (2.37**) 0.0195 (3.55***) 0.0364 (3.83***) 0.0527 (3.09***) 0.0044 (4.95***) 0.0032 (2.35**) 0.0003 (0.11) 0.0137 (1.06) ‐0.0066 (‐0.59) ‐0.0129 (‐0.84) 0.0272 (2.60***) 0.0372 (3.88***) 0.0463 (4.77***) 0.0195 (2.02**) 0.0038 (1.92*) 0.0044 (2.36**) 0.0067 (2.56**) 0.0014 (0.75) 0.0030 (1.91*) 0.0028 (1.77*) 0.0060 (4.29***) UK (158) Thailand (60) Switzerland (8) Sweden (18) Spain (18) Singapore (66) Philippines (58) Norway (12) Netherlands (11) Malaysia (154) Japan (90) Hong Kong (180) Germany (7) France (19) Finland (10) China (128) Belgium (17) Australia (37) Asia (778) Europe (289) N America (64) 2000 (130) 2001 (147) 2002 (147) 2003 (161) 2004 (175) 2005 (193) 2006 (178) Tangibility 0.0002 (4.17***) By country 0.0002 (2.55**) ‐0.00002 (‐0.19) ‐0.0004 (‐1.54) 0.0003 (3.46***) ‐0.00007 (‐1.23) 0.0084 (1.99**) 0.0020 (0.79) 0.0005 (0.64) 0.0003 (0.34) 0.0006 (1.06) ‐0.000007 (‐0.05) ‐0.0001 (‐1.13) 0.0008 (2.22**) ‐0.0042 (‐1.35) 0.0001 (0.50) 0.0002 (0.88) ‐0.0037 (‐1.18) 0.0002 (0.71) ‐0.0002 (‐0.41) By continents 0.0002 (1.74*) 0.00005 (1.26) 0.0002 (2.57**) By Year 0.00004 (0.26) 0.0002 (1.06) 0.00006 (0.26) 0.0001 (0.77) 0.0001 (1.56) 0.0004 (3.04***) 0.0004 (3.76***) Debt ratio Sustainable growth System weighted R2 ‐0.0406 (‐5.79***) 0.0861 (8.02***) 0.133 ‐0.0680 (‐3.43***) ‐0.0397 (‐1.83*) ‐0.1404 (‐4.12***) ‐0.0152 (‐6.15***) ‐0.1454 (‐2.05**) ‐0.0107 (‐0.27) ‐0.0689 (‐2.61) 0.1083 (2.80***) ‐0.0825 (‐0.96) ‐0.0801 (‐0.75) ‐0.0478 (‐2.51**) ‐0.0412 (‐4.03***) ‐0.0989 (‐2.31**) ‐0.0151 (‐0.48) 0.0267 (0.50) ‐0.2650 (‐3.80***) ‐0.0978 (‐4.04***) ‐0.1083 (‐2.78***) 0.1300 (1.53) 0.0325 (1.23) 0.2886 (6.69***) 0.0766 (2.10**) 0.3035 (29.99****) 0.1248 (4.07***) ‐0.0331 (‐0.69) 0.0186 (0.60) 0.2354 (4.46***) 0.1674 (1.43) 0.2897 (2..21**) 0.0136 (0.76) 0.0087 (0.79) 0.1396 (3.35***) 0.0940 (0.78) 0.8815 (6.55***) 0.1468 (1.01) 0.1244 (3.10***) 0.3898 (4.70***) ‐0.0068 (‐0.15) 0.321 ‐0.0556 (‐5.34***) ‐0.0616 (‐4.71***) ‐0.0677 (‐3.47***) 0.0677 (5.18***) 0.1994 (7.89***) 0.0322 (1.24) 0.109 ‐0.0282 (‐1.45) ‐0.0140 (‐0.84) ‐0.0035 (‐0.14) ‐0.0217 (‐1.26) ‐0.0600 (‐3.70***) ‐0.0606 (‐3.55***) ‐0.0640 (‐3.98***) 0.0580 (2.13**) 0.1667 (4.14***) 0.0428 (1.14) 0.0764 (2.97***) 0.0593 (2.21**) 0.0442 (2.06**) 0.1317 (5.16***) 0.272 0.433 0.987 0.725 0.201 0.184 0.461 0.410 0.640 0.189 0.173 0.212 0.678 0.826 0.813 0.279 0.732 0.152 0.245 0.322 0.079 0.159 0.055 0.067 0.109 0.139 0.303 Notes: Estimation using weighted two-stage least square (W2SLS); ***, **, * - indicates two tailed
significance at the 1%, 5% and 10% levels respectively
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Exhibit 7
Results of System Equation Estimation (Equation 4: Debt ratio: DEBTR)
Sample (No of pooled observation) Intercept Size All (1131) 0.2750 (11.45***) 0.0068 (2.24**) USA (63) 0.7506 (2.77***) 0.3397 (6.86***) 0.3100 (2.87***) 3.2434 (3.97***) 1.5110 (9.20***) 0.3244 (1.06) 0.3310 (4.57***) 0.8284 (1.41) 0.9989 (3.52***) ‐0.2717 (‐1.35) 0.2072 (4.64***) 0.3869 (4.24***) 0.2437 (5.35***) 1.8859 (43.62***) 0.6336 (7.52***) 0.5337 (3.06***) 0.2001 (2.00**) 0.2647 (1.33) 0.5210 (6.39***) ‐0.0754 (‐4.76***) 0.0140 (1.92*) 0.0011 (0l.07) ‐0.3714 (‐4.01***) ‐0.1280 (‐5.52***) 0.0357 (0.96) ‐0.0110 (‐1.13) ‐0.0059 (‐1.06) ‐0.0753 (‐1.57) 0.0876 (3.32***) ‐0.0072 (‐0.73) ‐0.0115 (‐1.28) ‐0.0098 (‐2.16**) ‐0.1292 (‐32.39***) ‐0.01453 (‐1.13) 0.0055 (0.17) 0.0043 (0.26) 0.0263 (0.92) ‐0.0489 (‐4.86***) 0.2169 (8.64***) 0.3523 (8.34***) 0.8797 (3.99***) 0.0034 (1.09) 0.0164 (2.73***) ‐0.0789 (‐5.17***) 0.2794 (3.85***) 0.3480 (4.54***) 0.2889 (3.58***) 0.2455 (3.41***) 0.2385 (3.69***) 0.2112 (3.24***) 0.2532 (5.17***) 0.0034 (0.37) ‐0.0082 (‐0.84) 0.0044 (0.43) 0.0127 (1.47) 0.0128 (1.77*) 0.0103 (1.65*) 0.0056 (0.81) UK (158) Thailand (80) Switzerland (8) Sweden (18) Spain (18) Singapore (66) Philippines (58) Norway (12) Netherlands (11) Malaysia (154) Japan (90) Hong Kong (180) Germany (7) France (19) Finland (10) China (128) Belgium (17) Australia (37) Asia (778) Europe (289) N America (64) 2000 (130) 2001(147) 2002 (147) 2003 (161) 2004 (175) 2005 (193) 2006 (178) Tangibility 0.00094 (4.65***) By Country ‐0.0002 (‐0.45) ‐0.0030 (‐0.;67) ‐0.00053 (‐0.22) 0.0175 (1.51) ‐0.00018 (‐1.07) 0.0011 (0.04) ‐0.0028 (‐0.69) 0.0016 (0.70) 0.0079 (2.54**) ‐0.00072 (‐0.49) 0.00023 (0.41) ‐0.00061 (‐0.75) ‐0.00035 (‐0.59) ‐0.0710 (‐17.28***) 0.0015 (1.44) ‐0.00026 (‐0.37) 0.0274 (2.47**) ‐0.0015 (‐0.99) 0.0023 (1.98**) By Continent 0.00062 (1.50) 0.00011 (0.37) ‐0.00021 (‐0.54) By Year 0.0018 (2.62***) 0.0030 (2.86***) 0.0023 (2.89***) 0.0012 (1.75*) 0.00007 (0.24) 0.0014 (2.86***) 0.0013 (2.38**) Profitability Growth (TA) ERR System weighted R2 ‐0.7709 (‐6.49***) 0.0747 (4.87***) ‐0.0273 (‐1.47) 0.067 ‐2.4148 (‐4.16***) ‐0.3081 (‐1.16) ‐1.3598 (‐3.48***) ‐11.7280 (I‐2.85***) 0.5322 (0.54) 0.5221 (0.37) ‐1.7259 (‐3.49***) 1.3992 (3.21***) ‐0.8555 (‐0.86) ‐3.2212 (‐2.06*) ‐0.8427 (‐2.60***) ‐4.8726 (‐4.49***) ‐0.4008 (‐3.13***) ‐14.6005 (‐18.12***) 0.5309 (0.89) ‐1.8333 (‐3.06***) ‐1.2704 (‐4.56***) ‐2.5515 (‐1.49) 0.6447 (1.80*) 0.5123 (4.90***) 0.0062 (0.14) 0.0120 (0.22) ‐0.3460 (‐1.49) 0.1169 (2.5r6**) 0.0915 (2.02**) 0.1926 (2.55**) ‐0.0073 (‐0.11) ‐0.1160 (‐1.54) 0.0034 (0.03) 0.0537 (1.49) ‐0.0084 (‐0.13) 0.02010 (1.14) ‐0.0772 (‐0.53) 0.0259 (0.91) 0.0707 (1.81*) 0.0062 (0.14) ‐0.00019 (‐0.01) 0.0339 (1.02) ‐0.0333 (‐0.14) ‐0.0667 (‐1.39) 0.1297 (1.42) ‐0.8210 (‐2.88***) ‐0.1544 (‐1.23) ‐0.2680 (‐1.74*) 0.1263 (2.31**) 0.0320 (0.68) ‐0.1106 (‐0.75) 0.3710 (1.59) 0.0288 (1.14) 0.2436 (2.55**) 0.0125 (0.35) 0.7044 (22.90***) ‐0.2979 (‐3.46***) 0.1205 (1.88*) 0.0104 (0.25) 0.2227 (0.98) ‐0.0399 (‐0.57) 0.535 ‐0.7538 (‐6.61***) ‐0.6861 (‐2.81***) ‐2.3786 (‐4.12***) 0.0468 (2.96***) 0.0353 (1.29) 0.5177 (4.98***) 0.0215 (1.10) ‐0.0538 (‐1.51) ‐0.1521 (‐0.78) 0.057 ‐0.6077 (‐1.79*) ‐0.4976 (‐1.25) ‐0.2284 (‐0.80) ‐0.6391 (‐1.83*) ‐1.2341 (‐3.78***) ‐1.1265 (‐4.03***) ‐0.9961 (‐3.17***) ‐0.0402 (‐1.08) 0.0829 (1.49) 0.0958 (1.95*) 0.0759 (3.04***) 0.1338 (2.52**) 0.0713 (2.12**) 0.1187 (2.54**) 0.0168 (0.29) ‐0.0453 (‐0.81) ‐0.0494 (‐0.89) ‐0.0487 (‐0.90) ‐0.0186 (‐0.35) 0.0407 (0.73) 0.0099 (0.27) 0.103 0.044 0.306 0.622 0.839 0.155 0.250 0.244 0.340 0.663 0.082 0.232 0.123 0.984 0.530 0.532 0.216 0.006 0.474 0.071 0.492 0.077 0.098 0.110 0.112 0.122 0.090 Notes: Estimation using weighted two-stage least square (W2SLS); ***, **, * - indicates two tailed
0significance at the 1%, 5% and 10% levels respectively
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Exhibit 8
OLS Regression Results
Variables
Intercept LN (market value) LN (Market equity / Book equity) Sustainable growth rate (SGR) Profitability (ROA) Capital structure (D/E) Spread (return on equity – cost of equity) DREG1 DREG2 DREG3 Adjusted R2 F‐stat Prob (F‐stat) Jensen ‘s alpha (JI)
Coefficient
t-statistic
Sharpe Index (SI)
Coefficient
t-statistic
‐0.01356*** 0.00195*** 0.00415*** 0.03848*** 0.04326** 0.00299** 0.00182 ‐2.90 2.69 3.20 7.75 2.25 2.02 0.19 0.64576 0.40993*** 0.28657 ‐0.44880 8.09315** ‐0.38674 1.64655 0.70 4.18 0.98 ‐0.38 2.01 ‐0.96 1.18 0.00382 ‐0.00358 0.00732*** 0.547 23.73 0.0000 1.13 ‐1.20 2.63 ‐0.29675 ‐1.83137*** 1.44638** 0.575 26.37 0.0000 ‐0.45 ‐2.82 2.46 Notes: ***, ** - indicates two-tailed statistical significance at the 1% and 5% levels; DREG i = dummy variable if firm
i is in Group X, X = 1 (Asia-Pacific developed /matured markets: Australia, New Zealand, Japan, Singapore, Hong
Kong), 2 (Asia-Pacific developing markets: Malaysia, Thailand, Philippines, Indonesia and China), 3 (European
markets: UK, France, Germany, etc) or =0, otherwise; Intercept is the SI (or JI) mean values for Group 4 (North
Americas markets: Canada and USA)
Exhibit 9
Binary Logit Regression Results
Prediction Evaluation (cutoff for success C = 0.5)
Jensen’s alpha (JI)
Sharpe Index (SI)
Prob (dep = 1) <=C 51 69 No correct 33 46 % correct 64.71 66.67 Prob (dep = 1)>=C 119 101 No correct 109 85 % correct 91.60 84.16 Total 170 170 No correct 142 131 % correct 83.53 77.06 Note: (a) “correct” classifications are obtained when the predicted probability is less than or equal 0.5 and the
observed dependent variable =0, or when the predicted probability is greater than 0.5 and observed dependent
variable = 1, (b) For JI, the dependant dummy is 1 if JI is positive; and is 0 when JI is negative, (c) For SI, the
dependant dummy is I if SI >2.1522 (sample average), or o otherwise
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