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Valuation of China’s Stock Market Mis pricing of Earnings Components. In-Mu HawTexas Christian University Shu-hsing LiNational Taiwan University Donghui Wu Hong Kong Polytechnic University Woody Wu Chinese University of Hong Kong. Outline. Introduction Literature Review Hypothesis - PowerPoint PPT Presentation
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Valuation of China’s Stock MarketMispricing of Earnings Components
In-Mu Haw Texas Christian University
Shu-hsing Li National Taiwan University
Donghui Wu Hong Kong Polytechnic University
Woody Wu Chinese University of Hong Kong
2
Outline
Introduction Literature Review Hypothesis Sample & Data Empirical Tests Conclusion & Implications
3
Introduction
The development of China’s stock market The stock market in China was established in
early 1990s. Market capitalization: ascended to the third place
in Asia by April 2001 Open to foreign money managers
October 2002 – first Sino-foreign fund management license.
May 2003 – QFII licenses are issued to Nomura Securities and UBS.
4
The number of stocks listed (1990 – 2007)
0
200
400
600
800
1,000
1,200
1,400
1,600
1990
/12
1991
/12
1992
/12
1993
/12
1994
/12
1995
/12
1996
/12
1997
/12
1998
/12
1999
/12
2000
/12
2001
/12
2002
/12
2003
/12
2004
/12
2005
/12
2006
/12
2007
/12
N
5
Total market value of A-shares(1990 – 2007)
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
9,000,000
10,000,000
1990
/12
1991
/12
1992
/12
1993
/12
1994
/12
1995
/12
1996
/12
1997
/12
1998
/12
1999
/12
2000
/12
2001
/12
2002
/12
2003
/12
2004
/12
2005
/12
2006
/12
2007
/12
Mill
ions
RM
B Y
uan
6
The Shanghai A-Share Index(1990.12 – 2003.12)
Ln(Index) = 5.746+0.131xR2 = 70.9%
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
1990
/12
1991
/12
1992
/12
1993
/12
1994
/12
1995
/12
1996
/12
1997
/12
1998
/12
1999
/12
2000
/12
2001
/12
2002
/12
2003
/12
2004
/12
2005
/12
2006
/12
2007
/12
7
Literature Review
The rapid development of China’s stock market has directed researchers’ attention to the role of accounting numbers in this market. Haw et al. (1999) – Earnings are highly relevant to
investors’ decisions. Chinese GAAP vs. IAS for the AB share firms
Both Haw et al. (1998) and Abdel-khalik et al. (1999) – IAS-based accounting numbers are not necessarily more useful to investors.
Abdel-khalik et al.: Can we make sense of the numbers?
8
Literature Review (Cont.)
Underlying the above studies is the efficient market hypothesis (EMH). A convenient and parsimonious framework for
understanding capital market. However, EMH may preclude us from discovering
something that is not “known” by the market. There is mounting evidence suggesting that the
market may not be as efficient as once believed. We put the descriptive validity of the market
efficiency assumption in China to an empirical test.
9
Literature Review (Cont.)
The mispricing literature The post-earnings-announcement drift
Ball & Brown, 1968. Bernard & Thomas, 1989 & 1990; Ball & Bartov, 1996;
Soffer & Lys,1999. Pricing of earnings components
Debt-equity swap gains – Hand, 1990. Accruals vs. cash flows – Sloan, 1996; Collins &
Hribar, 2000; Xie, 2001. Foreign earnings vs. domestic earnings – Thomas,
2000 & 2004. Special items – Burgstahler et al., 2002.
10
Our Approach
Decompose earnings into core earnings & non-core earnings
We find that: Chinese investors do not differentiate core from
non-core earnings.
11
Hypotheses
Why decompose total earnings into core and non-core parts? According to the standard income statement
prepared by Chinese firms (Figure 2): Core earnings (CE) – operating net income Non-core earnings (NCE) – all other I/S items
Income from investments Government subsidy income Other items, e.g., gains or losses from disposal of fixed
assets, assets revaluation, debt restructuring, etc.
12
Shanghai Petrochemical Co. Ltd. Consolidated Profit and Loss Account
For the year ended 31 Dec. 2000
Items 2000 1999
Revenue from principal operations 20,467,583 14,386,482
Less: Cost of sales 17,150,495 11,458,011
Business taxes and surcharges 548,713 349,895
Profits from principal operations 2,768,375 2,578,576
Add: Profit from other operations 84,194 69,477
The reversal of provisions for inventories 28,725 0
Less: Provisions for inventories 3,571 18,131
Operating expenses 314,870 275,003
Administrative expenses 1,125,449 1,056,494
Financial expenses 272,186 368,287
Income from operation 1,165,218 930,138
Add: Income from investments -17,748 8,989
Subsidy income 5,465 5,667
Non-operating income 26,077 24,398
Less: Non-operating expenses 98,367 92,951
Total Profit 1,080,645 876,241
Less: Income tax 153,415 122,495
Minority income 23,298 15,932
Net Profit 903,932 737,814
Core earnings
Non-core earnings
13
Hypotheses (Cont.)
In the valuation perspective: ∆CE are caused by changes in principle
operations More likely to affect future operations Thus more persistent
∆NCE are primarily caused by non-recurring transactions Less likely to persist into the future.
14
Hypotheses (Cont.)
In the earnings management perspective: To meet regulatory targets, Chinese firms often
manage earnings by timing the transactions related to NCE (Chen and Yuan, 2004; Haw et al., 2005).
Managed earnings are more likely to reverse in the next period.
15
Hypotheses (Cont.)
Therefore, CE are expected to be more persistent than NCE.
However, are Chinese investors aware of the difference between CE & NCE and price them differently?
16
Hypotheses (Cont.)
Why CE and NCE could be mispriced in China? The dominance of individual investors
On 2002/10/31, at Shanghai Stock Exchange: Individual investors Institutional investors
# 35,240,000 190,000% 99.47%
0.53% High trading volume: annual turnover rate > 400%.
Is this justified, given Chinese listed firms’ the limited disclosures and low coverage by financial press?
Is this driven by noisy traders?
17
Hypotheses (Cont.)
If investors are unsophisticated and attach the same weight to CE and NCE, then CE are undervalued NCE are overvalued
18
Sample and Data
Sample period – 1995 ~ 2005. Sample firms
All the firms listed in Shanghai and Shenzhen stock exchanges.
10,510 firm-year observations, representing 99.3% of all non-financial observations during the period.
Data source: Financial statement – CSMAR & Genius Stock price and other data items – CSMAR
19
Sample and Data (Cont.)
Measurement of the earnings variables Core Earnings: pre-tax earnings from principle
operations. Non-Core Earnings: all other income statement
items.
All the earnings variables are winsorized at the 1st and 99th percentile.
20
Sample and Data (Cont.)
Measurement of abnormal stock returns: size- and BE/ME-adjusted returns. 5×5 benchmark portfolios are formed by sorting stocks into
quintiles by their market value of equity and BE/ME at the beginning of each calendar month.
Annual buy-and-hold abnormal returns =∏(monthly raw returns – mean returns of benchmark portfolios).
This controls for the returns from rational pricing of the risk factors proxied by size and
BE/ME, and/or mispricing associated with these two variables per se.
21
Monthly excess returns to the benchmark portfolios
1
2
3
4
5
1
2
3
4
5
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
Ave
rage
Mon
thly
Exc
ess
Ret
urn
s
BE/ME Quintile
Size Quintile
22
The Actual and Implied Persistency of CE & NCE
The Mishkin (1983) framework for testing rational pricing
Equation (1) estimates the actual persistency of earnings components.
Equation (2) infers the persistency of earnings components implied by the market prices in year t+1.
∆Et+1 = α0 + α1∆CEt + α2∆NCEt + εt, (1)
SBMARt+1 = β0 + β1εt + ωt+1 = β0 + β1(∆Et+1 – α0 – α1
*∆CEt – α2*∆NCEt) + ωt+1.
(2)
23
The Actual and Implied Persistency of CE & NCE (Cont.)
Mishkin (1983) demonstrates that if the market’s pricing of the value-relevant information is unbiased, then: αi* = αi
The consistency of regression coefficients between two equations can be tested by non-linear least square method.
If χ2 (q) = 2n Ln (SSRC/SSRU) is sufficiently large, then one can reject the rational pricing hypothesis.
24
The Actual and Implied Persistency of CE & NCE (Cont.)
A. The actual persistence of
earnings components
B. The implied persistence
of earnings components
ΔCE α1 -0.128*** α1* -0.385***
(-9.740) (-7.246)
ΔNCE α2 -0.437*** α2* -0.265***
(-20.053) (-3.056)
Difference α1 – α2 0.309*** α1* – α2
* -0.121
(11.292) (-1.100)
C. Tests for equality of actual and implied persistence (χ2)
H0: α1* = α1 21.99*** H0: α2
* = α2 3.73*
25
The Actual and Implied Persistency of CE & NCE (Cont.)
Therefore, CE is actually more persistent than NCE, which is consistent with: NCE’s transitory nature. NCE are more likely to result from earnings
management.
However, the weight assigned by investors to ΔCE is significantly less than the actual persistence; ΔNCE is significantly greater than the actual
persistence.
26
Predicting Future Stock Returns– The Portfolio Test
If CE are undervalued relatively to its persistency, then the market would be “surprised” by the higher earnings realization in the subsequent period. ΔCE should be positively related to next period’s
returns. Similarly,
ΔNCE should be negatively related to next period’s returns.
Therefore, profitable portfolios can be formed by the earnings composition.
27
Predicting Future Stock Returns– The Portfolio Test (Cont.)
∆CE Portfolios Long (short) in the stocks with largest (smallest) ∆CEt within each
∆Et decile. ∆NCE Portfolios
Long (short) in the stocks with smallest (largest ) ∆NCEt within each ∆Et decile.
Changes in total earnings (∆E) is controlled for in the above strategies.
∆CE&∆NCE Portfolios Stocks are first grouped into quintiles by ∆CEt and ∆NCEt
independently. Long (short) in the stocks that are in both the top (bottom) ∆CEt
quintile and bottom (top) ∆NCEt quintile.Information on both the ∆CE and ∆NCE is utilized simultaneously.
28
Predicting Future Stock Returns– The Portfolio Test (Cont.)
The portfolios are formed at the beginning of May after year t and held for a year. Implementable trading rule; Predictive test.
While firm size and BE/ME effects are controlled for in measuring abnormal returns, some unknown risk factors may still lead to positive hedge portfolio returns.
Therefore, we report yearly abnormal returns on the portfolios and infer statistical significance by: T-test based on time-series variations of returns. Binomial test based on the signs of the yearly returns.
29
Predicting Future Stock Returns– The Portfolio Test (Cont.)
Abnormal Returns (%) to Hedge Portfolios in Year t+1
Long Short Hedge
(1) ∆CE Portfolios
Mean returns 3.678 -3.945 7.624
t-statistics (2.600**) (-3.817***) (3.667***)
# as expected 10*** 9** 11***
(2) ∆NCE Portfolios
Mean returns 4.101 -3.922 8.022
t-statistics (1.872*) (-4.501***) (3.136**)
# as expected 8* 9** 10***
(3) ∆CE&∆NCE Portfolios
Mean returns 5.647 -4.084 9.731
t-statistics (3.135**) -(3.083**) (3.674***)
# as expected 9** 10*** 10***
30
Predicting Future Stock Returns– The Portfolio Test (Cont.)
The positive returns to the hedge portfolios suggest that information on current earnings composition predicts future stock returns. Thus, current information is not fully reflected into
stock prices when it is available. The returns are positive for most of the sample
years. It would be difficult to attribute the returns to some
unidentified risk factors.
31
Predicting Future Stock Returns– The Portfolio Test (Cont.)
The concentration of abnormal returns during earnings announcement periods. Concentration would occur if a large amount of
unexpected earnings information becomes available to market participants on the earnings announcement dates.
If abnormal returns are simply risk premium, then the higher or lower returns should be evenly distributed in year t+1.
Earnings announcement periods include the 3-day windows centering on the interim and annual earnings announcement dates.
32
Predicting Future Stock Returns– The Portfolio Test (Cont.)
Abnormal Returns (%) to Hedge Portfolios in Year t+1
(Earnings Announcement Periods)
Long Short Hedge
(1) ∆CE Portfolios
Mean returns 0.029 -0.670 0.699
t-statistics (0.067) (-2.047*) (2.178*)
# as expected 5 8* 9**
(2) ∆NCE Portfolios
Mean returns 0.279 -0.869 1.147
t-statistics (0.745) (-2.800**) (2.827**)
# as expected 6 9** 8*
(3) ∆CE&∆NCE Portfolios
Mean returns 0.414 -1.689 2.104
t-statistics (0.727) -(3.665***) (2.505**)
# as expected 7 10*** 8*
33
Predicting Future Stock Returns– The Portfolio Test (Cont.)
Why there is presence of clustering of abnormal returns in the short positions but absence of clustering in long positions? Low litigation risks against the Chinese managers – firms
are likely to encourage early disclosure of good news. Information contained in good news earnings
announcements is more likely to be preempted by other sources than that in bad news announcements. That is, bad news travels slowly.
34
Predicting Future Stock Returns– The Regression Analysis
The regression approach More convenient to control for other factors affecting
both the stock returns and our experimental variables.
Test whether the mispricing of CE is incremental to NCE.
The regression model:RETt+1 = α + β1∆CEt + β2∆NCEt + γ1BE/MEt + γ2Sizet
+ γ3MKTRETEQt+1 + γ4MKTRETVL
t+1
+ γ5ROt + γ6ROt-1 + γ7Delistt + γ8MAOt + ε,
35
Predicting Future Stock Returns– The Regression Analysis (Cont.)
Cumulative raw returns are dep. var., and two market return indexes are indep. var.
Firm size and BE/ME are used as indep. var. to control for normal returns.
Seasoned equity offerings (ROt and ROt-1), closeness to delisting (Delistt), and modified audit opinions (MAOt).
36
Predicting Future Stock Returns– The Regression Analysis (Cont.)
Regression results
Pooled regression (N = 10,510) Annual regressions (N = 11 years) Variables
Coefficient t-statistics
Mean Coeff. t-statistics # Positive
Intercept -0.019 -2.488** 0.226 1.414 7
∆CEt 0.262 4.340*** 0.411 2.168* 10***
∆NCEt -0.298 -2.998*** -0.353 -2.380** 3*
BE/MEt 0.132 13.532*** 0.154 1.866* 8*
Sizet -0.080 -8.135*** -0.095 -1.488 6
MKTRETEQt+1 0.925 18.961*** – – –
MKTRETVLt+1 0.057 1.050 – – –
ROt -0.005 -0.423 0.014 0.620 7
ROt-1 -0.017 -1.369 -0.007 -0.424 4
Delistt 0.047 2.466** 0.079 2.962** 9***
MAOt -0.029 -2.749*** -0.043 -1.736 3*
Adj. R2 73.41% 12. 35%
37
Predicting Future Stock Returns– The Regression Analysis (Cont.)
The regression results are consistent with
those from portfolio tests.
Furthermore, the regression analysis
suggests that mispricing of CE is incremental
to that of NCE.
38
Effect of Delayed Responses on Value Relevance of CE & NCE
The value relevance of earnings – how relevant are earnings to users’ pricing decisions.
Earnings response coefficients (ERCs): RETt = α + βEARNt + e ERCs: β, the rates at which earnings are mapped
into stock prices. Thus, one may expect CE to have higher
ERCs than NCE.
39
Effect of Delayed Responses on Value Relevance of CE & NCE (Cont.)
ERCs of CE and NCE for different event windows
When τ = t When τ = [t, t+1]
CE 3.764 4.885
NCE 3.662 3.417
Difference 0.102
(t = 0.124)
1.468
(t = 2.183)
40
Effect of Delayed Responses on Value Relevance of CE & NCE (Cont.)
When only the contemporaneous association between returns and earnings are considered, ERCs on CE is not higher than those on NCE. The market does not value CE more than NCE.
When the return window is extended to include year t+1 so that correction for mispricing can be considered: Coefficients on CE increase and become higher than those
on NCE. Be cautious when inferring the value relevance of the
accounting numbers of Chinese listed firms by contemporaneous returns-earnings association.
41
Conclusion
Core earnings are more persistent than non-core earnings.
But the market does not understand such difference. Core & non-core earnings have similar ERCs. The implied persistency is lower than actual value for
CE but higher than actual value for NCE. Profitable portfolios can be formed by the information
contained in current earnings composition.
The mispricing of ∆CEt and ∆NCEt are incremental to
each other in the regression analysis.
42
Implications
An “out-of-sample” analysis on the accumulated evidence on mispricing obtained in the U.S.
Helpful to reassess how value-relevant are the financial data disclosed by Chinese firms.
Direct implications for equity investors who are interested in China’s capital market.
Policy implications for Chinese regulators responsible for disclosure issues.
43
THANK YOU!