stock market in china - fdjpkc.fudan.edu.cn
TRANSCRIPT
Stock market in China
Jinfeng Ge
Fudan University
8 11th, 2017
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 1 / 38
Outline
Overview of China’s stock market.
Some special features of China’s stock market.
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 2 / 38
Number of firms
FE09CH10_Carpenter ARI 18 September 2017 12:24
ChiNext
SME board
Shenzhen main board
Shanghai main board
Other nontradable
Nontradable executiveor employee
Nontradable legal person
Nontradable state owned
Tradable mutual fund
Other tradable
a
b
500
0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
1,000
1,500
2,000
2,500
3,000
3,500
Year
Year
0.5
0.0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Num
ber o
f lis
ted
firm
sM
arke
t cap
ital
izat
ion
(tri
llion
s of
RM
B)
Figure 1(a) Number of firms and (b) market capitalization of firms listed on China’s stock market, 1991–2016. (b) The time series of marketcapitalization is split at the year 2006 to accommodate a significant increase in scale. Figure adapted from Carpenter, Lu & Whitelaw(2017). Abbreviations: RMB, renminbi; SME, Small and Medium Enterprise.
The Split-Share Structure Reform of 2005 ushered in a second stage of privatization in China.As Li et al. (2011) and Liao, Liu & Wang (2014) explain, regulators and investors had becomeincreasingly aware of problems created by the split-share ownership structure, which weakenedminority shareholder protection and stifled the market for corporate control. After a number ofunsuccessful attempts to unlock nontradable shares, the CSRC devised a market mechanism tocompensate holders of tradable shares for potential adverse price impacts. Holders of nontradableshares in each firm would have to negotiate compensation to holders of tradable shares sufficientto secure their approval of the unlocking, which in turn would take place gradually over a periodof 1 year or longer. Most firms completed the reform by the end of 2007.
Liao, Liu & Wang (2014) use this reform as a natural experiment to measure the effect ofprivatization on firm performance. They find that the expectation of privatization boosted SOE
www.annualreviews.org • The Development of China’s Stock Market 237
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Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 3 / 38
The time series of market capitalization is split at the year2006 to accommodate a significant increase in scale
FE09CH10_Carpenter ARI 18 September 2017 12:24
ChiNext
SME board
Shenzhen main board
Shanghai main board
Other nontradable
Nontradable executiveor employee
Nontradable legal person
Nontradable state owned
Tradable mutual fund
Other tradable
a
b
500
019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
0420
0520
0620
0720
0820
0920
1020
1120
1220
1320
1420
1520
16
1,000
1,500
2,000
2,500
3,000
3,500
Year
Year
0.5
0.0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0.0
10.0
20.0
30.0
40.0
50.0
60.0
Num
ber o
f lis
ted
firm
sM
arke
t cap
ital
izat
ion
(tri
llion
s of
RM
B)
Figure 1(a) Number of firms and (b) market capitalization of firms listed on China’s stock market, 1991–2016. (b) The time series of marketcapitalization is split at the year 2006 to accommodate a significant increase in scale. Figure adapted from Carpenter, Lu & Whitelaw(2017). Abbreviations: RMB, renminbi; SME, Small and Medium Enterprise.
The Split-Share Structure Reform of 2005 ushered in a second stage of privatization in China.As Li et al. (2011) and Liao, Liu & Wang (2014) explain, regulators and investors had becomeincreasingly aware of problems created by the split-share ownership structure, which weakenedminority shareholder protection and stifled the market for corporate control. After a number ofunsuccessful attempts to unlock nontradable shares, the CSRC devised a market mechanism tocompensate holders of tradable shares for potential adverse price impacts. Holders of nontradableshares in each firm would have to negotiate compensation to holders of tradable shares sufficientto secure their approval of the unlocking, which in turn would take place gradually over a periodof 1 year or longer. Most firms completed the reform by the end of 2007.
Liao, Liu & Wang (2014) use this reform as a natural experiment to measure the effect ofprivatization on firm performance. They find that the expectation of privatization boosted SOE
www.annualreviews.org • The Development of China’s Stock Market 237
Ann
u. R
ev. F
inan
c. E
con.
201
7.9:
233-
257.
Dow
nloa
ded
from
ww
w.a
nnua
lrev
iew
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g A
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s pr
ovid
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y St
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Lib
rary
on
12/0
5/17
. For
per
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onl
y.
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 4 / 38
Normalized Real GDP for Large Countries 1991-2014
40
Figure 1. Normalized Real GDP in Large Countries by Year
This figure plots the normalized real GDP of China and other larger countries: United States, India, Brazil, and
Japan. The GDP data are in local currency and extracted from the World Bank database. The GDP values have been
adjusted for local inflation. The number is normalized to 1 in the starting year. Panel A and B plot the normalized
GDP of China and other larger countries for 1991-2014 and 2000-2014, respectively.
0
1
2
3
4
5
6
7
8
9
10
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
Panel A. Normalized Real GDP for Large Countries: 1991-2014
China United States India Brazil Japan
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel B. Normalized Real GDP of Large Countries: 2000-2014
China United States India Brazil Japan
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 5 / 38
Cumulative Annual Returns of Stock Indices in LargeCountries
41
Figure 2. Cumulative Annual Returns of Stock Indices in Large Countries
The figure plots the cumulative returns of the stock indices in large countries from 1992 to 2014. The indices are:
SSE Composite Index (China), S&P 500 (US), BSE Sensex (India), IBOV (Brazil) and Nikkei 225 (Japan). Annual
index return data are collected from Bloomberg. The nominal returns are in local currency and adjusted for local
inflation, measured by the year-end CPI. SSE and S&P 500 are value-weighted indices with total market
capitalization as the weight; SENSEX and IBOV are value-weighted indices with tradable shares’ market
capitalization as the weight. Nikkei is an equal-weighted index. SSE composite include all stocks listed in Shanghai
Stock Exchange. S&P 500, SENSEX, IBOV, and Nikkei include 500, 30, 50 and 225 stocks, respectively.
0
1
2
3
4
5
6
SSE China S&P500 US BSE SENSEX India
IBOV Brazil Nikkei Japan
Figure 3. Consumer Price Index (CPI) of China for 1992-2014
This figure plots the monthly CPI of China from January 1992 to March 2014. Monthly CPI data is collected from
National Bureau of Statistics (NBS) of China.
-5%
0%
5%
10%
15%
20%
25%
30%
Jan
, 19
92
Au
g, 1
99
2
Ma
r, 1
99
3
Oct
, 19
93
Ma
y, 1
99
4
Dec
, 19
94
Jul,
19
95
Feb
, 19
96
Sep
, 19
96
Ap
r, 1
99
7
No
v, 1
99
7
Jun
, 19
98
Jan
, 19
99
Au
g, 1
99
9
Ma
r, 2
00
0
Oct
, 20
00
Ma
y, 2
00
1
Dec
, 20
01
Jul,
20
02
Feb
, 20
03
Sep
, 20
03
Ap
r, 2
00
4
No
v, 2
00
4
Jun
, 20
05
Jan
, 20
06
Au
g, 2
00
6
Ma
r, 2
00
7
Oct
, 20
07
Ma
y, 2
00
8
Dec
, 20
08
Jul,
20
09
Feb
, 20
10
Sep
, 20
10
Ap
r, 2
01
1
No
v, 2
01
1
Jun
, 20
12
Jan
, 20
13
Au
g, 2
01
3
Ma
r, 2
01
4
Oct
, 20
14
CPI
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 6 / 38
Value-Weighted Buy-and-Hold Returns of Stocks Listed inLarge Countries
42
Figure 4. Value-Weighted Buy-and-Hold Returns of Stocks Listed in Large Countries
This figure plots the value-weighted buy-and-hold returns (BHR) of the stocks listed in China (A-Share), US, India,
Brazil and Japan. The BHRs are calculated by accumulating value-weighted annual returns of all stocks listed in the
country with the lagged-one-year market capitalization as the weight. The returns are adjusted for stock split and
include cash dividends. Nominal returns are adjusted for inflation to be converted to real returns. Inflation is
measured by the year-end CPI rate of the listing country. We set the BHR to be 1 in year 2000. We appreciate the
CAFR-Chinese stock market research project for sharing with us the stock return data of A-share listed firms. Stock
returns of US listed firms are from CRSP. Stock return data for firms listed in other large countries are extracted
from Datastream. Annual stock returns are denominated in local currency. The number of unique firms to make the
plot for China, US, Brazil, India, Japan and Chinese firms listed overseas is 2872, 9369, 867, 3436, 6510 and 758,
respectively.
0
0.5
1
1.5
2
2.5
3
3.5
4
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
China United States
India Brazil
Japan Chinese Firms Listed Overseas
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 7 / 38
Comparison of Returns on Bank Deposits, GovernmentBond and Stocks Listed in A-Share
43
Figure 5. Comparison of Returns on Bank Deposits, Government Bond and Stocks Listed in A-Share
This figure plots the buy-and-hold returns on bank deposits, government bonds and stocks listed in China (A-Share).
The line represents the value-weighted buy-and-hold returns of stocks listed in Shanghai or Shenzhen stock
exchange, with the lagged-one-year market capitalization as the weight. The stock returns have been adjusted for
stock split and include cash dividends. The bars represent cumulative returns on 1-year and 5-year bank deposits,
and 3-year and 5-year government bonds in China. Nominal returns on bank deposits, government bonds and stocks
are adjusted for inflation (measured by the year-end inflation rate) to be converted to real returns. The deposit
interest rate and government bond yield data are extracted from the website of Peoples’ Bank of China (PBOC). If
the government bond is issued for multiple times in one year, we calculate the average yield of these issues and then
cumulate the mean return.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
1-year Deposit 5-year Deposit 3-year Government Bond
5-year Government Bond A-share Stock
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 8 / 38
Operating Performance of Listed Firms and MatchedUnlisted Firms in China
44
Figure 6. Operating Performance of Listed Firms and Matched Unlisted Firms in China
This figure plots the value-weighted average ROA of listed firms and their one-to-one matched unlisted (private)
firms in China (A-Share), with year-end book assets as the weight. For each listed firm, we select from the sample of
unlisted firms the one with the closest book assets measured in the same year as the matching firm. Industry is
defined by the level-2 industry classification in Datastream. We require the book assets of the matching firm to be
within the [80%, 120%] range of the book assets of the listed firm. We exclude newly listed firms in each year. For
the period 1998-2013, 2767 distinct listed firms are matched with one unlisted firm each.
0.00
0.02
0.04
0.06
0.08
0.10
0.12
Listed Unlisted Matched
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 9 / 38
Comparison of Operating Performance of Listed Firmsbefore Special Treatment (/ST0) in China and ListedFirms before Delisting US
45
Figure 7. Comparison of Operating Performance of Listed Firms before Special Treatment (“ST”) in China
and Listed Firms before Delisting US
This figure plots the operating performance of firms listed in China (A-Share) in the [-5,0] year window before
receiving a “special treatment” (“ST”) and that of US listed firms before being delisted. Operating performance is
measured by ROA averaged across firms in the same window. Window 0 denotes the year when a firm becomes
special treated or delisted. “ST” firms in China include temporary ST and permanent ST. The former refers to firms
that ever received special treatment but later got their ST removed; the latter refers to firms that received special
treatment and never re-emerged from the special treatment later during the sample period. In total, there are 527
distinct “ST” firms in our sample, 82 of which are permanent “ST” firms. To make a sensible comparison, we allow
only permanent “ST” firms to enter the plot. For Chinese “ST” firms, window 0 refers to the year when the firm
becomes “ST”. For US delisted firm, window 0 refers to the delisting year, i.e., the year of last stock price available
or the year when the firm’s stock trading becomes inactive, depending on which date appeared later. We extract
delisting information for US listed firms from CRSP. CRSP document 6 major reasons for delisting: merger,
exchange, liquidation, being dropped, expire, and become foreign listed. We keep firms that are delisted for the
reason “liquidation” or “being dropped”. This leaves us 295 distinct firms that are delisted from US stock exchanges.
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
-5 -4 -3 -2 -1 0
China ST US Delisted due to Liquidation or Being Dropped
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 10 / 38
Investment and Net Cash Flows of Listed Firms in A-Sharevs. Listed in Other Large Countries
46
Figure 8. Investment and Net Cash Flows of Listed Firms in China and Other Large Countries
This figure plots the value-weighted average investment and net cash flows of listed firms in China (A-Share) and
other large countries by year. Panel A1 and A2 plot the average investment of listed firms. Investment is measured
by capital expenditure in year t scaled by the book assets in year t-1. Panel B1 and B2 plot the average net cash
flows of listed firms. Net cash flows are scaled by book assets. Net Cash Flow is calculated as EBITDA – Change in
Working Capital - Income Taxes – Capital Expenditure. Both the investment and cash flow measures are averaged
across firms with the year-end book assets as the weight. The sample is restricted to firms that have non-missing
data on EBITDA, capital expenditure, working capital, income taxes and book assets. In Panel A1 and B1, the
number of unique firms that enter the plot for China, US, India, Brazil and Japan is 2573, 7453, 3368, 799 and 6430,
respectively. In Panels A2 and B2, the number of unique Chinese firms listed overseas that enter the plot is 702.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel A1. Capital Expenditure/Lagged Assets: Firms Listed in A-Share
vs. Listed in Other Large Countries
China United States India Brazil Japan
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel A2. Capital Expenditure/Lagged Assets: Chinese Firms Listed
in A-Share vs. Listed Overseas
Firms Listed in China Firms Listed in US
Chinese Firms Listed Overseas
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 11 / 38
Investment and Net Cash Flows of Listed Firms in A-Sharevs. Listed Overseas
46
Figure 8. Investment and Net Cash Flows of Listed Firms in China and Other Large Countries
This figure plots the value-weighted average investment and net cash flows of listed firms in China (A-Share) and
other large countries by year. Panel A1 and A2 plot the average investment of listed firms. Investment is measured
by capital expenditure in year t scaled by the book assets in year t-1. Panel B1 and B2 plot the average net cash
flows of listed firms. Net cash flows are scaled by book assets. Net Cash Flow is calculated as EBITDA – Change in
Working Capital - Income Taxes – Capital Expenditure. Both the investment and cash flow measures are averaged
across firms with the year-end book assets as the weight. The sample is restricted to firms that have non-missing
data on EBITDA, capital expenditure, working capital, income taxes and book assets. In Panel A1 and B1, the
number of unique firms that enter the plot for China, US, India, Brazil and Japan is 2573, 7453, 3368, 799 and 6430,
respectively. In Panels A2 and B2, the number of unique Chinese firms listed overseas that enter the plot is 702.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel A1. Capital Expenditure/Lagged Assets: Firms Listed in A-Share
vs. Listed in Other Large Countries
China United States India Brazil Japan
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel A2. Capital Expenditure/Lagged Assets: Chinese Firms Listed
in A-Share vs. Listed Overseas
Firms Listed in China Firms Listed in US
Chinese Firms Listed Overseas
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 12 / 38
Net Cash Flow/Total Assets: Firms Listed in A-Sharevs.Listed in Other Large Countries
47
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel B1. Net Cash Flow/Total Assets: Firms Listed in A-Share vs.
Listed in Other Large Countries
China United States India Brazil Japan
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel B2. Net Cash Flow/Total Assets: Chinese Firms Listed in A-Share
vs. Listed Overseas
Firms Listed in China Firms Listed in US Chinese Firms Listed Overseas
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 13 / 38
Net Cash Flow/Total Assets: Chinese Firms Listed inA-Share vs. Listed Overseas
47
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel B1. Net Cash Flow/Total Assets: Firms Listed in A-Share vs.
Listed in Other Large Countries
China United States India Brazil Japan
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel B2. Net Cash Flow/Total Assets: Chinese Firms Listed in A-Share
vs. Listed Overseas
Firms Listed in China Firms Listed in US Chinese Firms Listed Overseas
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 14 / 38
Capital Expenditure/Lagged Assets around IPO
48
Figure 9. Investment and Net Cash Flows around IPO for Chinese vs. US Listed Firms
This figure plots the average investment and net cash flow of Chinese firms listed in mainland China (A-Share) and
Chinese firms listed overseas, and firms listed in the US around IPO. We require firms have non-missing capital
expenditure, net cash flows and total assets in the year prior to IPO. Investment is measured as capital expenditure
scaled by the lagged-one-year total assets. Net Cash Flow is calculated as EBITDA – Change in Working Capital -
Income Taxes – Capital Expenditure. Both the measures for investment and cash flows are averaged across firms
with year-end total assets as the weight. The number of firms listed in China, US and Chinese firms listed overseas
that enter the plot is 1599, 2749 and 483, respectively.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
-1 0 1 2 3 4 5
Panel A. Capital Expenditure/Lagged Assets around IPO
Firms Listed in US Firms Listed in China
Chinese Firms Listed Overseas
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
-1 0 1 2 3 4 5
Panel B. Net Cash Flows/Total Assets around IPO
Firms Listed in US Firms Listed in China Chinese Firms Listed Overseas
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 15 / 38
Net Cash Flows/Total Assets around IPO
48
Figure 9. Investment and Net Cash Flows around IPO for Chinese vs. US Listed Firms
This figure plots the average investment and net cash flow of Chinese firms listed in mainland China (A-Share) and
Chinese firms listed overseas, and firms listed in the US around IPO. We require firms have non-missing capital
expenditure, net cash flows and total assets in the year prior to IPO. Investment is measured as capital expenditure
scaled by the lagged-one-year total assets. Net Cash Flow is calculated as EBITDA – Change in Working Capital -
Income Taxes – Capital Expenditure. Both the measures for investment and cash flows are averaged across firms
with year-end total assets as the weight. The number of firms listed in China, US and Chinese firms listed overseas
that enter the plot is 1599, 2749 and 483, respectively.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
-1 0 1 2 3 4 5
Panel A. Capital Expenditure/Lagged Assets around IPO
Firms Listed in US Firms Listed in China
Chinese Firms Listed Overseas
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
-1 0 1 2 3 4 5
Panel B. Net Cash Flows/Total Assets around IPO
Firms Listed in US Firms Listed in China Chinese Firms Listed Overseas
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 16 / 38
Market-to-Book: Firms Listed in A-Share vs. Listed inOther Large Countries
49
Figure 10. Valuation of Firms Listed in China and Other Large Countries
This figure plots the aggregate market-to-book ratio of the firms listed in mainland China (A-Share) and firms listed
in other large countries. For each country, the aggregate market-to-book is calculated as the sum of market
capitalization of all stocks listed in this country divided by the sum of book equity of the same firms. To ensure
consistency of calculation of the numerator and denominator, we use stock-level book equity as the denominator for
firms that are listed in more than one market. Stock-level book equity as calculated as firm-level book equity
multiplied by the ratio of market capitalization of the stock listed in one country out of the total market capitalization
of the firm in all countries that the firm is listed in. In Panel A, the number of unique firms for China, US, India,
Brazil and Japan is 2662, 8467, 3333, 726 and 6432, respectively. In Panel B, the number of unique Chinese firms
listed overseas that enter the plot is 758.
0
1
2
3
4
5
6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel A. Market-to-Book: Firms Listed in A-Share
vs. Listed in Other Large Countries
China United States India Brazil Japan
0
1
2
3
4
5
6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel B. Market-to-Book: Chinese Listed in A-Share vs. Listed
Overseas
Firms Listed in China Chinese Firms Listed Overseas
Firms Listed in US
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 17 / 38
Market-to-Book: Chinese Listed in A-Share vs. ListedOverseas
49
Figure 10. Valuation of Firms Listed in China and Other Large Countries
This figure plots the aggregate market-to-book ratio of the firms listed in mainland China (A-Share) and firms listed
in other large countries. For each country, the aggregate market-to-book is calculated as the sum of market
capitalization of all stocks listed in this country divided by the sum of book equity of the same firms. To ensure
consistency of calculation of the numerator and denominator, we use stock-level book equity as the denominator for
firms that are listed in more than one market. Stock-level book equity as calculated as firm-level book equity
multiplied by the ratio of market capitalization of the stock listed in one country out of the total market capitalization
of the firm in all countries that the firm is listed in. In Panel A, the number of unique firms for China, US, India,
Brazil and Japan is 2662, 8467, 3333, 726 and 6432, respectively. In Panel B, the number of unique Chinese firms
listed overseas that enter the plot is 758.
0
1
2
3
4
5
6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel A. Market-to-Book: Firms Listed in A-Share
vs. Listed in Other Large Countries
China United States India Brazil Japan
0
1
2
3
4
5
6
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Panel B. Market-to-Book: Chinese Listed in A-Share vs. Listed
Overseas
Firms Listed in China Chinese Firms Listed Overseas
Firms Listed in US
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 18 / 38
Chinese Stocks Listed in A-Share and in Overseas Markets
50
Table 1
Distribution of Firms Listed in China and Other Countries by Year
This table presents the summary statistics of firms listed in China and firms listed in other countries by year. Panel A
shows the distribution of Chinese listed firms in our sample by year. Columns 1 to 3 present the distribution for
Chinese firms listed in Shanghai or Shenzhen stock exchanges (“A-Share”). Columns 4 to 6 present the distribution
of firms headquartered in China and listed in overseas markets. Columns 2 and 5 report the number of state-owned
firms (SOE) listed in A-share and Chinese SOEs listed overseas. The state ownership information is extracted from
WIND under the data item “ultimate controller”. We define firms ultimately controlled by central SASAC (State-
owned Assets Supervision and Administration Commission of the State Council), local SASAC, Ministry of Finance,
and other government agency as state-owned firms. Columns 3 and 6 report the average book assets ($ billion) of
Chinese firms listed in A-Share and the average book assets of Chinese firms listed overseas, respectively. Panel B
presents the number of firms listed in other large countries by year, including US, India, Brazil and Japan.
Panel A. # of Chinese Stocks Listed in A-Share and in Overseas Markets
Firms Listed in A-Share Chinese Firms Listed Overseas
Year
# Listed
Firms
# of Listed
SOEs
Average Assets
($ Billion)
# Listed
Firms
# of Listed
SOEs
Average Assets
($ Billion)
(1) (2) (3)
(4) (5) (6)
2000 1041 779 0.25
51 46 1.32
2001 1123 844 0.31
65 47 2.68
2002 1192 900 0.37
80 47 2.42
2003 1255 796 0.43
97 53 2.63
2004 1343 820 0.46
128 62 3.02
2005 1340 813 0.51
167 70 3.32
2006 1418 823 0.69
203 80 8.48
2007 1522 841 0.98
268 102 17.03
2008 1577 858 1.07
337 111 17.47
2009 1723 873 1.20
384 123 19.04
2010 2071 910 1.25
431 131 21.28
2011 2300 902 1.31
490 133 27.40
2012 2464 943 1.35
534 141 31.40
2013 2465 1157 1.47
549 140 30.79
2014 2321 919 1.76 661 172 34.43
Panel B. # of Stocks Listed in Other Large Countries
Year United States India Brazil Japan
2000 6614 606 417 2909
2001 6369 688 379 3065
2002 6179 724 370 3103
2003 6109 877 390 3183
2004 5958 1109 432 3239
2005 5847 1303 431 3284
2006 5613 2517 432 3263
2007 5358 2644 436 3234
2008 5268 2725 424 3175
2009 5232 2758 413 3117
2010 5183 2730 408 3040
2011 5077 2715 386 2970
2012 4852 2652 351 2896
2013 4665 2999 535 2711
2014 4717 2876 506 2696
Total 9369 3436 867 6510
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 19 / 38
Correlation between 5-Year Stock Returns and Future GDPGrowth
51
Table 2
Correlation between 5-Year Stock Returns and Future GDP Growth
This table reports the Pearson correlation between 5-year stock returns and the future GDP growth in that country
for the top 20 countries according to the IMF GDP ranking in 2014. We include South Africa in addition to the top
20 countries. We calculate the correlation for 1991-2014, or for a period starting from the year when the stock return
data become available in our dataset and ending at 2014, if the first stock return data are available after 1991. The
correlation is estimated using cumulative stock returns of a 5-year interval and the cumulative GDP growth in the
next 5-year interval (so we get stock returns for year t, t+5, ….and GDP growth for year t+1, t+6, …), back from
2014 on a rolling basis. Country-level stock returns are calculated as value-weighted stock returns of individual
stocks listed in a country, with the lagged one year market capitalization as the weight. The last row tests the
difference in the correlation coefficients of China and developed countries as a group, and the difference of China
and other emerging countries as a group. We use the OECD Classification to define developed and emerging
countries. Emerging countries include China, Brazil, Russian Federation, India, Mexico, Indonesia, Turkey and
Saudi Arabia. We do not have individual stock return data for South Korea so we calculate the correlation using the
stock market index (KOSPI Korea). For Saudi Arabia, the stock market index data are available for a longer period
than individual stock return data in our sample, so we report the correlation calculated from the stock market index
(the DFMGI Index). ***, ** and * denote the statistical significance at 1%, 5% and 10% levels.
IMF GDP
Ranking Country
Individual Stock
or Index Returns
Sample
Period Correlation
p-
value
1 United States Stock Return 1991-2014 0.565*** 0.004
2 China Stock Return 1991-2014 0.012 0.958
3 Japan Stock Return 1991-2014 0.418** 0.046
4 Germany Stock Return 1991-2014 0.697*** <0.001
5 United Kingdom Stock Return 1991-2014 0.322 0.133
6 France Stock Return 1991-2014 0.602*** 0.003
7 Brazil Stock Return 1995-2014 0.560** 0.012
8 Italy Stock Return 1991-2014 0.286 0.195
9 India Stock Return 1991-2014 0.573*** 0.006
10 Russian Federation Stock Return 1996-2014 0.547** 0.032
11 Canada Stock Return 1991-2014 0.524** 0.014
12 Australia Stock Return 1991-2014 0.469** 0.023
13 South Korea Index Return 1991-2014 -0.156 0.793
14 Spain Stock Return 1991-2014 0.593*** 0.002
15 Mexico Stock Return 1991-2014 0.322 0.143
16 Indonesia Stock Return 1991-2014 0.349 0.121
17 Netherlands Stock Return 1991-2014 0.735*** <0.001
18 Turkey Stock Return 1991-2014 0.414* 0.054
19 Saudi Arabia Index Return 1995-2014 0.196 0.524
20 Switzerland Stock Return 1991-2014 0.288 0.182
1 South Africa Stock Return 1991-2014 0.619*** 0.002
Chinese Stocks Listed
Overseas Stock Return 1991-2014 0.414* 0.069
Difference Group
Mean of
Correlation China
Difference
(Other
p-
Value
Countries-
China)
Developed 0.568 0.012 0.556*** <0.001
Emerging 0.567 0.012 0.555*** <0.001
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 20 / 38
Level and Growth of Net Income of Chinese Firms bySector
52
Table 3
Level and Growth of Net Income of Chinese Firms by Sector
This table reports the level and growth of net income generated by industrial sector in China. We group all industrial
firms into state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). Within the SOE group and
the non-SOE group, we further distinguish firms to Listed SOE, Unlisted SOE, Listed Non-SOE and Unlisted Non-
SOE. Panel A reports the proportions of the aggregate net income of each group out of the aggregated net income of
all industrial firms, listed or unlisted industrial firms in China. Panel B reports the aggregate net income growth rate
of each group. We calculate aggregate net income growth for each group as the increase in net income aggregated
across firms of this group from year t-1 to year t, scaled by the total net income generated by the same group of
firms in year t-1. The bottom row of Panel B reports the Pearson correlations between the net income growth rate of
each group of firms and the contemporaneous GDP growth rate in China. We extract net income data for all
industrial firms and those for SOEs from the statistical yearbook of National Bureau of Statistics (NBS). ***, ** and
* denote statistical significance at 1%, 5% and 10% level, respectively.
Panel A. Net Income Percentage of Chinese Firms Listed in A-Share
Year SOE/All Listed/All Listed SOE/Listed All
Unlisted Non-
SOE/Unlisted All
(1) (2) (3) (4)
2000 53.74% 35.10% 89.94% 65.84%
2001 49.03% 24.96% 94.50% 66.09%
2002 44.10% 24.23% 90.71% 70.81%
2003 43.66% 25.42% 88.31% 71.55%
2004 41.41% 25.85% 89.85% 75.47%
2005 39.58% 23.72% 92.46% 76.87%
2006 39.92% 22.15% 90.26% 74.40%
2007 36.63% 21.20% 86.68% 76.84%
2008 27.98% 17.45% 85.02% 84.07%
2009 26.02% 15.73% 80.97% 84.24%
2010 27.34% 15.14% 79.88% 82.04%
2011 25.65% 14.42% 77.43% 83.08%
2012 23.48% 13.70% 78.17% 85.20%
2013 21.25% 17.62% 79.20% 91.14%
2014 20.01% 19.32% 75.55% 82.31%
Panel B. Net Income Growth of Chinese Firms by Sector
Year All Listed Unlisted Listed SOE
Listed Non-
SOE
Unlisted
Non-SOE
(1) (2) (3) (4) (5) (6)
2001 0.060 -0.246 0.225 -0.208 -0.588 0.23
2002 0.217 0.182 0.229 0.134 0.998 0.317
2003 0.474 0.546 0.451 0.505 0.944 0.466
2004 0.424 0.448 0.415 0.473 0.257 0.493
2005 0.243 0.141 0.279 0.174 -0.152 0.302
2006 0.336 0.247 0.363 0.217 0.611 0.319
2007 0.408 0.349 0.426 0.295 0.843 0.472
2008 0.128 -0.071 0.182 -0.089 0.044 0.294
2009 0.145 0.031 0.168 -0.018 0.311 0.171
2010 0.552 0.494 0.563 0.474 0.579 0.522
2011 0.15 0.095 0.159 0.061 0.229 0.174
2012 0.005 -0.045 0.014 -0.036 -0.077 0.04
2013 0.104 0.42 0.054 0.439 0.353 0.128
2014 0.019 0.064 -0.063 -0.01 0.22 0.034
Average 0.233 0.188 0.247 0.172 0.327 0.283
Correlation Coefficient 0.687*** 0.390 0.713*** 0.361 0.411 0.627**
P-Value 0.007 0.168 0.004 0.204 0.144 0.022
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 21 / 38
Net Income Growth of Chinese Firms by Sector
52
Table 3
Level and Growth of Net Income of Chinese Firms by Sector
This table reports the level and growth of net income generated by industrial sector in China. We group all industrial
firms into state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs). Within the SOE group and
the non-SOE group, we further distinguish firms to Listed SOE, Unlisted SOE, Listed Non-SOE and Unlisted Non-
SOE. Panel A reports the proportions of the aggregate net income of each group out of the aggregated net income of
all industrial firms, listed or unlisted industrial firms in China. Panel B reports the aggregate net income growth rate
of each group. We calculate aggregate net income growth for each group as the increase in net income aggregated
across firms of this group from year t-1 to year t, scaled by the total net income generated by the same group of
firms in year t-1. The bottom row of Panel B reports the Pearson correlations between the net income growth rate of
each group of firms and the contemporaneous GDP growth rate in China. We extract net income data for all
industrial firms and those for SOEs from the statistical yearbook of National Bureau of Statistics (NBS). ***, ** and
* denote statistical significance at 1%, 5% and 10% level, respectively.
Panel A. Net Income Percentage of Chinese Firms Listed in A-Share
Year SOE/All Listed/All Listed SOE/Listed All
Unlisted Non-
SOE/Unlisted All
(1) (2) (3) (4)
2000 53.74% 35.10% 89.94% 65.84%
2001 49.03% 24.96% 94.50% 66.09%
2002 44.10% 24.23% 90.71% 70.81%
2003 43.66% 25.42% 88.31% 71.55%
2004 41.41% 25.85% 89.85% 75.47%
2005 39.58% 23.72% 92.46% 76.87%
2006 39.92% 22.15% 90.26% 74.40%
2007 36.63% 21.20% 86.68% 76.84%
2008 27.98% 17.45% 85.02% 84.07%
2009 26.02% 15.73% 80.97% 84.24%
2010 27.34% 15.14% 79.88% 82.04%
2011 25.65% 14.42% 77.43% 83.08%
2012 23.48% 13.70% 78.17% 85.20%
2013 21.25% 17.62% 79.20% 91.14%
2014 20.01% 19.32% 75.55% 82.31%
Panel B. Net Income Growth of Chinese Firms by Sector
Year All Listed Unlisted Listed SOE
Listed Non-
SOE
Unlisted
Non-SOE
(1) (2) (3) (4) (5) (6)
2001 0.060 -0.246 0.225 -0.208 -0.588 0.23
2002 0.217 0.182 0.229 0.134 0.998 0.317
2003 0.474 0.546 0.451 0.505 0.944 0.466
2004 0.424 0.448 0.415 0.473 0.257 0.493
2005 0.243 0.141 0.279 0.174 -0.152 0.302
2006 0.336 0.247 0.363 0.217 0.611 0.319
2007 0.408 0.349 0.426 0.295 0.843 0.472
2008 0.128 -0.071 0.182 -0.089 0.044 0.294
2009 0.145 0.031 0.168 -0.018 0.311 0.171
2010 0.552 0.494 0.563 0.474 0.579 0.522
2011 0.15 0.095 0.159 0.061 0.229 0.174
2012 0.005 -0.045 0.014 -0.036 -0.077 0.04
2013 0.104 0.42 0.054 0.439 0.353 0.128
2014 0.019 0.064 -0.063 -0.01 0.22 0.034
Average 0.233 0.188 0.247 0.172 0.327 0.283
Correlation Coefficient 0.687*** 0.390 0.713*** 0.361 0.411 0.627**
P-Value 0.007 0.168 0.004 0.204 0.144 0.022
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 22 / 38
Potential listing venues for Chinese firmsFE09CH10_Carpenter ARI 18 September 2017 12:24
Table 1 Potential listing venues for Chinese firms
SSE SZSE SEHK NYSE NASDAQ
Legal costs Low Low Medium High High
Dual class permitted No No No Yes Yes
Earnings/sizerequirement
Strict positiveearningsthreshold for3 consecutiveyears
Strict positive earningsthreshold for3 consecutive years formain board, softerthresholds for SMEand ChiNext boards
Softer 3-yearearningsthreshold
Softer 3-yearearnings or sizethreshold
Even softerearnings or sizethreshold
Selectionmechanism
IB sponsorshipand CSRCapproval
IB sponsorship andCSRC approval
IB sponsorship Registrationbased
Registrationbased
Average processingtime
10 months 10 months 6 months 4 months 4 months
Total marketcapitalization,August 2016(trillions ofdollars)
4.0 3.2 3.2 19.3 9.1
Parent company CSRC CSRC HK Exchangesand ClearingLimited
IntercontinentalExchange
The NasdaqOMX Group
Year founded 1990 1990 1891 1792 1971
Number of listedcompanies, August2016
1,114 1,796 1,925 3,176 3,170
Abbreviations: CSRC, China Securities Regulatory Commission; IB, investment bank; NYSE, New York Stock Exchange; SEHK, Stock Exchange ofHong Kong; SME, Small and Medium Enterprise; SSE, Shanghai Stock Exchange; SZSE, Shenzhen Stock Exchange.
Table 1 summarizes the listing requirements, legal costs, processing time, and number andsize of Chinese firms for mainland, Hong Kong, and US exchanges. Whereas listing in Chinais least expensive, incorporating overseas is generally most expensive because it requires foreignlegal counsel, particularly when the firm uses a complex variable interest entity structure to by-pass Chinese restrictions on foreign direct investment in strategic industries. Therefore, foreignincorporation is generally an option only for larger firms. Requirements on prelisting net incomealso vary across exchanges. The SSE is strictest, requiring 3-year cumulative net profits in excessof RMB 30 million, whereas the NASDAQ is the most tolerant, allowing negative earnings forfirms that meet other criteria. Finally, governance requirements also vary across exchanges. TheUS exchanges allow dual-class structures with differential voting rights, whereas the Hong Kongand Chinese exchanges do not.
In addition to differential listing requirements, Chinese firms may also consider longer-termeffects of listing choice. Evidence from the literature on cross-listing on US exchanges providessome insights: Compared to firms that do not cross-list, firms that cross-list exhibit lower votingpremia and thus better minority shareholder protection; are more likely to terminate poorlyperforming CEOs; and have higher Tobin’s Q, lower cost of capital, and larger stock return and
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Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 23 / 38
EQUITY PRICING
The A-Share Premium Puzzle
Information Asymmetry and Behavioral Effects
Stock Price Informativeness
Cross-Sectional Patterns in Returns
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 24 / 38
The A-Share Premium Puzzle
FE09CH10_Carpenter ARI 18 September 2017 12:24
are negatively correlated with the relative supply of A shares and positively correlated with thesupply of B shares. Chan, Menkveld & Yang (2008) provide an explanation based on informationasymmetry within the A-share market and find that traditional measures of information asymmetryhelp to explain the cross section of A-share premia.
Mei, Scheinkman & Xiong (2009) use the dual-class structure to test the theory that speculativetrading in the presence of short-sales constraints can lead to overvaluation (Miller 1977; Harrison& Kreps 1978; Chen, Hong & Stein 2002; Scheinkman & Xiong 2003). They view B-share pricesas controls for stock fundamentals and find that A-share premia are cross-sectionally correlatedwith turnover rates and idiosyncratic return volatility, proxies for speculative motives in trading.In 2001, the CSRC allowed domestic Chinese investors to hold B shares, which brought B-sharediscounts down to 40%, according to Karolyi, Li & Liao (2009). They find that the firms with thegreatest declines in B-share discounts were those with the lowest state ownership and concludethat political risk is an important determinant of the price differential.
With the introduction of the QFII program in 2002, which allows qualified foreign institutionalinvestors to directly hold A shares, B-share issuance and trading has largely died out. However,A-share premia over corresponding H shares with identical cash flow and voting rights are stillprevalent for firms that are dual-listed in mainland China and Hong Kong. Figure 2 shows thetime series of the median A-H premium, i.e., A-share price divided by H-share price, for the fullsample of dual-listed firms, as well as for firms in the lower half of this sample as ranked by marketcapitalization (i.e., smaller firms) and for firms in the financial and manufacturing sectors, since2006. The full-sample median has been about 1.5 or 2 in recent years, but was over 3 in 2009and peaked in the 10–15 range in the late 1990s and early 2000s. The median A-H premium forsmaller firms is consistently higher than for larger firms, possibly reflecting the shell value of alisting on the domestic Chinese stock market that could potentially be acquired by a firm seekingto circumvent the usual listing process for A shares. A-H premia are consistently higher for firms
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Figure 2Median A-share premium, i.e., A-share price divided by H-share price, 2006–2016. Shown are the full sample of firms with dual listingsof A shares in Shanghai or Shenzhen and H shares in Hong Kong (blue); the half of this sample containing smaller firms (orange); firmsin the financial and insurance sectors ( green); and firms in the manufacturing sector ( purple).
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Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 25 / 38
Information Asymmetry and Behavioral Effects I
Brennan and Cao (1997) show nearby investors are better informedthan those farther away, and will thus react less to information andwill execute trades of opposite sign, they find that trades of investorsacross branches within a given region are positively correlated andthat trades across regions are negatively correlated
Jia, Wang and Xiong (2015) study the reactions of A- and H-shareprices of dual-listed stocks to analysts.revisions of earnings forecastsand find that A-share prices react more strongly to revisions fromlocal, mainland-based analysts, whereas H-share prices react morestrongly to revisions from foreign analysts. They attribute this resultto investors.greater trust in analysts from their home region,associated with social and cultural factors such as those studied byGuiso, Sapienza and Zingales (2008, 2009)
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 26 / 38
Information Asymmetry and Behavioral Effects II
Andrade, Bian and Burch (2013) identify China.s stock market as anatural setting for the study of asset price bubbles generated bydispersion in investor beliefs because of its short-sale constraints andthe dominance of retail investors. Focusing on the 2007 stock pricebubble, they find that stocks with greater analyst coverage hadsmaller bubbles, attributing this to analysts.coordinating beliefs.
Hong et al. (2014) exploit the uneven rise in household wealth andthe growth of the middle class across Chinese regions over the period1998õ2012 to test for evidence of keeping-up-with-the-Jonesespreferences and trading for status concerns. They use a province orcity.s GDP per capita as a proxy for status concerns and use thedifference between small and large stock turnover as a proxy for localstock turnover. They show that investors in regions that becamericher faster traded more actively in small local stocks
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 27 / 38
Stock Price Informativeness I
The framework by Roll (1988): R-squared and the Economy.
Capital Asset Pricing Model (CAPM), represents the expected returnof stock j as
rj ,t = rf + βj (rm,t − rf )
where rm,t is the return on a fully diversified portfolio of assets and rfis the risk-free return. The coefficient βj relates the stock’s return tothe sole pricing factor, the equity risk premium
Using Market Model regressions of the form
rj ,t = αj + βj rm,t + ej ,t
where ej ,t is the residual component of stock j ’s return not explainedby the equity risk premium and and αj = βj (1 − rf ) is non-stochastic
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 28 / 38
Stock Price Informativeness II
Variance decomposition
Variance (rj ,t) = Variance (βj rm,t) + Variance (ej ,t)
where Variance (βj rm,t) is the market-wide variation in the stock’sreturn and Variance (ej ,t) is the firm-specific variation in its return
Roll (1988) notes that the regression R-square measures both thegoodness of fit of the Market Model for stock j.s returns data andthe fraction of the variation in stock j.s return related tomarket-wide fluctuations. A lower R-square merely means that moreof the variation in stock j.s price is firm-specific õthe stock.sreturns are less synchronous with the overall market.
Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 29 / 38
US Stock Comovement, 1926 õ2010
6
Figure 1. US Stock Comovement, 1926 – 2010 Panel A. R-squared is the mean of the R2s of Market Model regressions of each US stock’s weekly (Weds.-to-Weds.) total return of the CRSP value-weighted market return. Means are weighted by either total return variation or market capitalization.
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Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 30 / 38
Firm-specific variation in US Stock Market
7
Panel B. Market-wide variation is the mean across all US stocks of the sum-of-squared variation explained by the Market Model. Firm-specific variation is the corresponding residual variation.
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Jinfeng Ge (Fudan University) Stock market in China 8 11th, 2017 31 / 38
Less Synchronous Stock Returns in Higher IncomeEconomies I
9
Figure 2. Less Synchronous Stock Returns in Higher Income Economies Panel A. Mean stock-level market model R2, by year from 1995 to 2010 for each country, estimated using weekly (Weds.-to-Weds.) DataStream total returns and country total return indexes. Countries are sorted by their mean R2 over all years.
0% 10% 20% 30% 40% 50% 60% 70% 80%
ChinaRussiaTurkey
ColombiaArgentina
TaiwanPakistanMalaysia
SpainMexicoGreece
HungaryCzech Republic
IndiaItaly
JapanPeru
SingaporeChile
NetherlandsBrazil
ThailandFinland
SwitzerlandPortugal
IsraelNorway
IndonesiaSouth Korea
PolandPhilippines
BelgiumAustria
DenmarkHong Kong
United StatesSweden
FranceIreland
GermanyCanada
New ZealandSouth Africa
United KingdomAustralia
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
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Less Synchronous Stock Returns in Higher IncomeEconomies II
10
Panel B. Country means of the stock-level Market Model R2s from Panel A plotted against log of per capita GDP (constant 2000 US dollars). Both are means for 1995 to 2010.
Argentina
Australia
Austria Belgium
Brazil
Canada
Chile
China
Colombia
Czech Rep.
Denmark
Finland
France
Germany
Greece
HK
Hungary
India
Indonesia
Ireland
Israel
Italy Japan
Malaysia
Mexico
Netherlands
New Zealand
Norway
Pakistan
Peru
Philippines
Poland Portugal
Russia
Singapore
S. Africa
S. Korea
Spain
Sweden
Switzerland Thailand
Turkey
UK
US
0%
5%
10%
15%
20%
25%
30%
35%
40%
2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75
Gra
nd
me
an R
sq
uar
ed
(1
99
5 -
20
10
)
Mean from 1995 to 2010 of log of per capita GDP (Constant 2000 US dollars)
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Market Efficiency
Informational efficiency can reflect private arbitrageurs gathering newinformation, reassessing firms.fundamental values, and trading toprofit from those reassessments (Grossman 1976); or more meaningfulpublic announcements (Fama et al. 1969); or more energetic insidertrading (Manne 1966)
Each can push stock prices towards fundamental values, all else equal,raising informational efficiency where informed arbitrage is less costly,disclosure fuller and timelier, or insider trades more informative
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Functional Efficiency I
Informational efficiency is a means to an end, not necessarily an endper se
The social purpose of financial markets is arguably to allocate theeconomy.s savings to their highest value uses (Schumpeter 1911).Tobin (1984) defines the stock market as functionally efficient if stockprice changes push the economy towards a microeconomically efficientallocation of capital, and notes that functional and informationalefficiency need not coincide. Indeed, Grossman and Stiglitz (1980)and Black (1985) argue they cannot if information is costly. Thus,tests of how closely share prices obey a martingale (Griffin, Kelly andNardari 2013) need not gauge functional efficiency
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Functional Efficiency II
Such considerations shift our focus from informational efficiency tofunctional efficiency: Do stock prices that move about moreasynchronously better direct capital to its highest value uses
Wurgler (2000) gauges the functional efficiency of a country.sfinancial system by the correlation of capital spending with valueadded across industries. If a country.s capital spending concentratesin its higher value-added industries, capital flows to where it createsmore new wealth. Wurgler finds more functional efficiency in thefinancial systems of economies with higher mean incomes, largerfinancial sectors, and stronger shareholder rights.
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Functional Efficiency and R-square
21
Figure 3. Functional Efficiency and R2 Higher levels of Wurgler’s (20000) measure of functional efficiency indicate a greater concentration of capital spending in industries with higher value-added. R-squared is from Morck et al. (2000). Both variables use mid 1990s data.
Indonesia
India
Colombia
Turkey
Chile
Philippines
Mexico
Netherlands
Singapore
Portugal
Ireland
Australia
Finland
Canada
Greece
Norway
Korea
Malaysia
Italy
US
Japan
Peru
Sweden
France
Belgium
Spain
NZ
UK
Austria
HK
Denmark
Germany
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2
Mar
ket
Mo
de
l R-s
qu
are
d
Functional efficiency
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Investment in Innovation and Firm-specific Event Intensity
27
Figure 4. Investment in Innovation and Firm-specific Event Intensity
The stocks of firms in US industries with histories of heavier investment in Information
technology-related capital assets exhibit higher firms-specific return volatility in the 1990s
century. Circle sizes reflect relative industry total assets.
Source: Chun et al. (2008, p. 117)
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