Transcript DOMAIN KNOWLEDGE SPECIFICITY AND JOINT NEW …

CORPORATE SIZE, STOCK RETURN,
AND COST EFFICIENCY
Ruey-Shii Chen
Tatung University
Taipei, Taiwan
[email protected]
OUTLINE
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INTRODUCTION
LITERATURE REVIEW
HYPOTHESIS
RESEARCH METHOD
ESTIMATED RESULTS
CONCLUSION
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INTRODUCTION
 The Capital Asset-Pricing Model (CAPM) of
Sharpe (1964) 、 Lintner (1965) and Black
(1972) depicted that there existed a positive
relation between risk and expected returns and
the systematic risk (beta) is the only risk factor
to predict expected returns.
 Fama and French (1992, 1993) examined a
number of securities markets and show that
the stocks of small firms generally provide
higher mean returns than do the stocks of
large firms. The empirical observation are
known as the size effect.
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INTRODUCTION
 The validity of size effect as well as
other
empirical
anomalies
is
a
controversial issue in empirical finance.
 Size effect are found in many countries
including U.S (Banz (1981), Reinganum
(1983), Keim (1983), Fama and French
(1992)), Japan (Kato and Schallheim
(1985)), and Australia (Brown, Klein,
Kleidon and Marsh (1983)).
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EXISTENCE OF SIZE EFFECT
 Scholars also found that size effect is related
to seasonality (Keim (1983)) , business cycle
and market condition (Bhardwaj and Brooks
(1993), Ibbotson and Sinquefeld (1995) and
Kim and Burnie (2002) ).
 Empirical studies on U.S. found that size
effect disappeared after 1980 (Bhardwaj and
Brooks (1992), Jagannathan and McGrattan
(1995), Hawawini and Keim (1995), Dichev
(1998) and Horowitz, Loughran and Savin
(2000)).
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CAUSE OF SIZE EFFECT
 infrequent trading of small firms (Banz
(1981), Lustig and Leinbach (1983))
 the difference of price-earnings ratio
(Cook and Rozeff, 1984)
 transaction costs (Stoll and Whaley, 1983)
 tax-loss selling (Reinganum, 1983)
 information effect (Barry and Brown, 1984)
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RISK FACTOR BEHIND SIZE EFFECT
 Size effect could be explained by
default risk (Chan, Chen and Hsieh
(1985), Chen, Roll and Ross (1986),
and Fama and French (1993) )
 Size effect is generated by inefficient
firm’s high leverage and cash flow
problems. (Chan and Chen (1991))
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ARTICLES AT ODDS RISK ASPECT
 Daniel and Titman (1997) and Daniel,
Titman and Wei (1999) use factor loadings
of three factors model proposed by Fama
and French (1993) to form portfolios, they
argued that it’s characteristics other than
factor loadings influencing expected returns.
 Dichev
(1998)
demonstrated
that
bankruptcy risk is not rewarded by higher
returns but at the same time there still
existed size effect.
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DELISTING BIAS
 Shumway and Warther (1999) argued
that most of the survived small firms in
the stock market are good ones.
 Firms are delisted from stock market
due to their poor performance are small
firms often.
 That is why there exists size effect if
studies use listed firms as their sample.
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LONG-TERM AVERAGE COST
LMC
AC
LAC
MES
Q
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LMC and LAC
 When a firm is producing at an output at
which the long term average cost is falling,
the long term marginal cost is less than
long term average cost.
 Conversely, when long term average cost is
increasing, long term marginal cost is
greater than long term average cost.
 The two curves intersect at a point where
long term average cost achieves its
minimum.
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HYPOTHESIS
 Under the U-shaped average cost curve
 If firm size is less than the corresponding
size of the minimum long term average cost,
the firm expanding its scale, and they tend
to, would decrease average cost and
improve efficiency. (higher stock return)
 On the other hand, if firm size is larger
than the corresponding size of the
minimum long term average cost, the firm
expanding its scale would increase average
cost.
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DATA SOURCES
 two databases in Taiwan.
 Manufacturing census data from DirectorateGeneral of Budget, Accounting and Statistics,
Executive Yuan, R.O.C. during the period of
1981 to 1996.
 Census data is used to estimate minimum
efficient scale for each industry.
 Taiwan’s listed companies data from the
Taiwan Economics Journal (TEJ) database
during the period of 1976 to 2004.
 The second database is used to calculate
listed corporate related variables.
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DATA PROCESS
 The census data classify industries according to
the CIC code, and the listed companies in
Taiwan are classified into industries by Taiwan
Exchange Stock Corporation.
 Only after adjusting the two different industry
classification codes to be consistent, we can
apply the minimum efficient scale estimated
from census data to listed companies.
 The data in this study exclude financial firms
and observations within one year of IPO. We
also delete some observations due to missing
book value or other related variables.
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DESCRIPTIVE STATISTICS
 Ret is the log monthly return, in percent. Beta is
computed following Fama and French (1992). Size
is the market price multiple number of common
stock outstanding denominated in ten billions of
dollars. BtM is book to market ratio.
Mean
SD
Minimum Maximum
Ret
1.206
15.549
-128.680
125.310
Beta
0.952
0.037
0.893
1.032
Size
1.286
2.725
0.014
65.708
BtM
0.496
0.280
0.011
2.702
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CORRELATION COEFFICIENTS
 In the first stage, we calculate
correlation coefficients during period of
1981 to 1996 each month. In the
second stage, we calculate the monthly
average correlation coefficients.
 Economies of scale dummy variable
(ESDM) is equal to one if firm’s net sales
revenue more than the MES of the
industry, and zero otherwise.
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PEARSON CORRELATIONS
Ret
Beta
Size
Beta
0.078
Size
-0.214
-0.139
ESDM
-0.021
-0.008
0.211
BtM
0.089
0.134
0.164
ESDM
0.149
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Trans-log Cost Function
 We assume firms only employ two factors—
labor and capital. The two factors trans-log
cost function is :

2
ln C   0   Y ln Y  YY  ln Y 
2
1
   i ln Pi 
2
i
 
i
ij
ln Pi ln Pj
j
   Yi ln Y ln Pi
i
 Cost elasticity
CE 
C
Y  MC     ln Y   ln P   ln P
Y
YY
YL
L
YK
K
C
AC
Y
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Trans-log Cost Function
 We use iterating SUR (seemingly
unrelated regression) to estimate translog cost functions for industries each
year . The results are presented in
Appendix 1 to 4. We then use the
estimated coefficients to calculate cost
elasticity.
 The MC and AC curves intersect at a
point where long term average cost
achieves its minimum.
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ESTIMATION-1
 We run cross-sectional regressions each month
using various specifications of the following
model:
Re ti ,t  f (betai ,t , Sizei ,t , ESDMi ,t , BtMi ,t )
 At the end of December of each year t,
portfolios are formed on the basis of ranked
values of size or pre-ranking beta. The preranking betas use 5 years of monthly returns
ending in December of t. Stocks are assigned
the post-ranking beta of the size-beta portfolio
they are in at the end of December of year t.
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ESTIMATION-1
The slope is the time-series average of
the monthly regression slopes for
January 1981 to December 1996, and
the t-statistics is the average slope
divided by its time-series standard error.
The values in parentheses are tstatistics.
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Table 4-1
Beta
Size
1
2
3
4
5
-2.092
-2.911
-2.999
-2.765
-3.087
(-0.542)
(-0.809)
(-0.856)
(-0.784)
(-0.893)
-0.509＊
-0.455＊
-0.442
-0.381
(-1.776)
(-1.709)
(-1.514)
(-1.436)
0.357
0.194
(1.134)
(0.637)
ESDM
BtM
5.370＊＊
5.343＊＊
(3.042)
(3.041)
***, **, * significant at 0.01, 0.05, 0.1 level respectively
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FINDINGS
 Size is negatively significant in second
model.
 Size still remains significant when BtM is
added in third model .
 Size becomes not significant when dummy
variable ESDM substitute for BtM in the
fourth model.
 Size is not significant in the fifth model
when ESDM and BtM are added together.
 The significance of size largely depends on
whether ESDM is included in the model.
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Data Envelopment Analysis
 Charnes et al. (1978) developed the CCR
model which assumes constant returns to
scale (CRS) and can measure overall
technical efficiency (OTE).
 Banker et al. (1984) allowed for variable
returns to scale (VRS) in their model, which
is known as the BCC model. The overall
technical efficiency in the CRS model can be
decomposed into pure technical efficiency
(PTE) and scale efficiency (SE): OTE = PTE
 SE in VRS model.
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EFFICIENCY ESTIMATION (DEA)
 We choose net sales revenues as the
single output and the value of the assets,
staff number, and R&D expenditures are
the three inputs.
 Using the results from DEA estimation
we calculate the extent of scale, pure
technical, and total technical efficient
improvement (symbolized by DSE, DPTE,
and DTTE respectively) for each firmyear observations.
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ESTIMATION-2
 This regression covers all listed electronic
industry firms in Taiwan from 1986-2004.
 There are 1980 firm-year observations in
our pooled dataset.
 Dummy variable DMS equals one if the
firm’s scale is allocated in increasing
returns to scale, and zero otherwise.
 We regress firm’s yearly return on DSE
(DTTE or DPTE) and the interaction term
DSE*DMS (DTTE*DMS or DPTE*DMS).
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ESTIMATION-2
 The dependent variables in all regressions
are all the same -- stock return.
 The influence of total technical efficiency
change on stock return is decomposed into
pure
technical
efficiency
and
scale
efficiency change.
 The whole period 1996-2004 is divided into
two sub-periods 1996-2000 and 2001-2004.
The values in parentheses are p-values.
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Table 4-2
1996-2000
2001-2004
1996-2004
constant
23.58***
(0.000)
-10.94***
(0.000)
-1.77
(0.197)
DSE
26.18**
(0.017)
-0.01
(0.155)
-0.01
(0.115)
DSE*DMS
33.38*
(0.080)
39.84***
(0.000)
29.51***
(0.000)
constant
83.37***
(0.000)
-4.92
(0.165)
10.92***
(0.000)
DTTE
50.33**
(0.036)
-0.01
(0.132)
-0.01*
(0.074)
DTTE*DMS
17.03***
(0.000)
4.91
(0.206)
15.53***
(0.000)
Panel A
Panel B
Table 4-2 (cont.)
1996-2000
2001-2004
1996-2004
22.74***
(0.000)
-8.80***
(0.000)
-0.78
(0.569)
DPTE
-0.37
(0.987)
-0.93
(0.157)
-1.02
(0.116)
DPTE*DMS
1.36
(0.975)
7.53
(0.502)
12.69
(0.238)
Panel C
constant
***, **, * significant at 0.01, 0.05, 0.1 level respectively
FINDINGS
 Panel A of table 4-2 shows that the
interaction
term
DSE*DMS
is
positively significant in all period
regressions.
 The positive relationship between
extent of scale efficient improvement
and stock return is pronounced for
firms with increasing returns to scale.
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FINDINGS
 Panel B of table 4-2 shows similar results
as panel A, which indicate firms with
increasing returns to scale enhancing the
positive influence on total technical
efficiency except period 2001-2004.
 Panel C of table 4-2 show the extent of
pure technical efficient improvement is not
significant at all.
 Overall, it is scale efficiency plays an
important role in influencing stock return.
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CONCLUSIONS
 For firms with scale less than the
minimum efficient scale tend to increase
their production scale and gain benefits
from
cost
saving.
We
therefore
hypothesize it is the cause of size effect.
 In the first regression we find that
whether size effect is significant or not
largely depends on if economies of scale
dummy ESDM is included in the model.
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CONCLUSIONS
 In the second regression we find the
positive relationship between extent of
scale efficient improvement and firm’s
return is pronounced for firms with
increasing returns to scale.
 The results of this study thus support
our hypothesis that size effect may
result from firms with scale less than the
MES of the industry gain benefits if they
increase their scale.
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THE END
Thank you very much
for your attention!