Valuation of China’s Stock Market Mis pricing of Earnings Components

1. 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 WuChinese 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)

5. Total market value of A-shares(1990 – 2007)

6. The Shanghai A-Share Index(1990.12 – 2003.12)

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. Non-core earnings 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

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.

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.)

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.) • ∆CEPortfolios • Long (short) in the stocks with largest (smallest) ∆CEt within each ∆Et decile. • ∆NCEPortfolios • 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&∆NCEPortfolios • 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.)

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.)

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:

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.)

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.)

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 valuerelevance 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.

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