European Financial Management Symposium 2009 Judge Business School, University of Cambridge, UK

Media Coverage, Stock Price Informativeness and Trading Activity: Evidence

from China

Stephen X.H. Gong and Ferdinand A. Gul

Hong Kong Polytechnic University

• Research objectives• Background & literature review• Research hypotheses• Sample selection, variable measurement and testing method• Empirical results• Further analyses • Conclusion

Presentation outline

• Primary objective: examine the effects of media coverage on stock price performance in China’s emerging market context• Specifically, how does media coverage affect stock price informativeness and trading activity?• What drives the observed relationship between media coverage and stock price performance? • Parallel objectives: (1) What determines the level of media coverage a company gets? (2)The relative descriptive validity and explanatory power of alternative measures of media coverage (quantity versus quality)? • The Chinese stock market (with its inadequate information environment and speculative investors who tend to “herd”) provides a fertile testing ground for these research objectives.

Research objectives

• No role for information aggregators in economic and finance models that assume perfect markets.• Two conflicting schools: one school of thought questions the validity of the press as an important information intermediary in the economy (Jensen, 1979; DeAngelo, DeAngelo and Gilson, 1994, 1996). • Some research (based in US) finds that substantial movements in share prices do not seem to correspond to changes in economic fundamentals (Cutler, Poterba and Summers, 1989), and that stories reported in the financial press have little impact on stock returns and trading volume activity over short-event horizons (Mitchell and Mulherin, 1994; Berry and Howe, 1994) • Such results do not rule out the possibility that media coverage may affect individual firms or the broader market in ways other than immediate price or volume changes. • Shiller (2005) concludes that while the news stories that broke around the time of the stock market crashes of 1929 and 1987 were not the essential cause of the crashes, news events have the effect of causing an attention cascade among investors by heightening their fixations on market moves, and in so doing, the news media become fundamental propagators of speculative price movements.

Background and literature review

• In the real world, people obtain much of their information from the media, which play an important role in selecting which pieces of information to communicate to the public and in adding credibility to information collected through other sources (Dyck and Zingales, 2002). • The impact of media coverage on stock prices is formalized in Merton (1987), who posits an asset pricing theory that deviates from the Sharpe-Lintner capital asset pricing model in that investors do not have full information about the available securities and thus choose to invest in the shares of companies they know about. • Merton (1987) notes that a newspaper or other mass media story about the firm or its industry that reaches a large number of investors who are not currently shareholders could increase the number of investors in the firm, and hence reduce the firm’s cost of capital and increase the market value of the firm.• This “incomplete information” hypothesis is empirically supported in Amihud, Mendelson and Uno (1999) in the Japanese market context and Fang and Peress (2009) in the U.S. market context. •The availability of low-cost (perhaps new, substantive) information through the mass media improves the cost-benefit tradeoff on information collection, leading to more informed trading and more informative pricing (Grossman and Stiglitz, 1980).

Background and literature review (2)

• Media coverage increases the reputation cost of regulators, intermediaries and managers, and thus has the potential to improve corporate governance (Dyck, Volchkova and Zingales, 2008). Better corporate governance, in turn, leads to more informative stock prices by encouraging collection of, and trading on, private information (Ferreira and Laux, 2007). • Apart from being an information intermediary, the mass media have the potential to breed familiarity and hence affect overall stock performance (e.g. propensity to invest/trade in stocks)• There is a growing body of research on investors’ tendency to invest with a company they know or think they know (e.g. Huberman, 2001; Li, 2004; Lehavy and Sloan, 2008). • Grullon, Kanatas and Weston (2004) find that investors’ degree of familiarity with a firm affects its cost of capital and consequently its value. • Individual investors prefer holding stocks with high recognition (Frieder and Subrahmanyam, 2005).• Barber and Odean (2008) report that individual investors are net buyers of attention-grabbing stocks, e.g. stocks in the news, stocks experiencing high abnormal trading volume, and stocks with extreme one-day stock returns.

Background and literature review (3)

• Thus, both finance theory and practice predicts that media coverage affects stock performance.• In countries with under-developed institutions (e.g. poor information environment and investor protection), mass media has the potential to play an important role, either in providing the general public with (perhaps new, substantive) information, and/or in breeding familiarity or grabbing investors’ attention.• Such potential may however be adversely affected by the credibility/independence of the media and investors’ behavioral patterns (e.g. are they “fixated” on (swayed by) the mere appearance of a company in the media, or do they also care about the quality of coverage?)

Background and literature review (4)

• In China’s emerging stock market, the financial mass media are just “opening up” for relatively free reporting, and there is concern for the independence and credibility of the media sector. • Both institutional investors and (even more so) individual investors engage in active short-term trading, with the latter (which account for the lion’s share of stock turnover) tending to hold and trade in stocks characterized by smaller capitalization, lower price, poorer performance, and higher price-earnings ratio (CRSC, 2008). • A high level of stock synchronicity in China (Morck, Yeung & Yu, 2000)—Is the mass media a driving force behind such documented co-movement?

Background and literature review (5)

• The presence and extent of media effects in emerging markets is an empirical question. • We focus on two key aspects of the media effect: stock price informativeness and stock market participation. • Stock price informativeness (idiosyncratic volatility, IV): the extent to which stock prices incorporate firm-specific relative to marketwide information.• Prior research (e.g. Morck et. al, 2000; Chan and Hameed, 2006; Jin and Myers, 2006) suggests that if the firm’s information or institutional environment causes stock prices to incorporate more firm-specific information, market factors should explain a smaller proportion of the variation in stock returns, i.e. the return synchronicity or R-square from a market model regression should be lower. • Verrecchia’s (1979) hypothesizes that the relative degree of pricing efficiency of a security is predicated on the number of traders who actively participate in a market for that security.• H1: Is stock price informativeness enhanced by media coverage? The literature review (as well as intuition) suggests so.

Research hypotheses

• Stock turnover: how frequently do investors buy/sell stocks.• Given the short-term trading behavior of Chinese investors, media coverage may fuel trading volume without necessarily increasing the number of registered shareholders. Hence our focus on stock turnover instead of shareholder base as a measure of the level of market participation.• H2: Is stock turnover enhanced by media coverage?• There is a need to control for possible endogeneity: media coverage may follow from high stock turnover (an attention-grabbing event--Barber and Odean, 2008) • Hence our parallel interest in the determinants of media coverage, H3. There are few existing studies on this. Our tests are exploratory in nature.• H4, the source of media effects: Does the media effect arise from media reports providing new, substantive information, or does it arise from media coverage breeding familiarity?

Research hypotheses (2)

• Designing strong tests to discriminate between these competing hypotheses is difficult. We made a first attempt at this important question by devising and comparing two measures of media coverage, one based primarily on the quantity (times mentioned) of coverage, the other on the quality of coverage (i.e. the content of newspaper reports). The result on the relative descriptive validity and explanatory power of these measures may shed light on the sources of the media effect, and offer possible reasons for the conflicting results in prior studies with respect to the existence of media effects (e.g. Core, Guay and Larcker, 2008).• Although we propose H1 and H2 separately, there is a close link between stock price informativeness and stock turnover. • Trading is theoretically linked to the quality or extent of private information (e.g. Blume, Easley and O’Hara, 1994). • Ferreira and Laux (2007) consider stock turnover as one alternative to stock price informativeness in proxying for the intensity of private information flowing to a stock’s market (they also use PIN etc as alternative measures). • Active short-term trading is prevalent among Chinese investors. Such trading may reflect factors unrelated to the quality or extent of private information (it can be fuelled by the quantity of media coverage).

Research hypotheses (3)

• All China-listed A-shares during the period 2000-2006. • Stock price informativeness (idiosyncratic volatility, IV):

• IV captures the effects on stock returns of company-specific information relative to marketwide factors (Fama, 1976). This measure has been used in many papers (e.g. Morck et al. 2000; Ferreira and Laux, 2007) • Recent research (e.g. Durnev et al. 2003) suggests that higher firm-specific return variation as a fraction of total variation signals more information-laden stock prices and therefore more efficient markets. • Chen, Goldstein and Jiang (2007) use stock return nonsynchronicity as a measure of private information incorporated into stock prices and find that investment responds more to stock prices when the stock return synchronicity is lower. • There is a continuing debate on the relationship between market model R-square and the quality of firm-specific information (e.g. Teoh, Yang and Zhang, 2008).

Sample selection, variable measurement and testing method

),ln()1

ln(22

2

2

2

iei

ie

R

RIV

• Stock turnover: Annual average of total number of shares traded per day over number of tradable shares per day (monthly alternatives, as well as annual total number of shares and total values of shares traded, are also used as a check on robustness)• Media coverage: (1) A simple frequency count (i.e. how many times is a company mentioned in the newspaper reports? Multiple mentions in the same article is counted once only). (2) A content-analysis-based measure (i.e. each newspaper report is read and scored on the basis of its correspondence with key information categories, e.g. discussions relating to current/future operations, earnings forecast, risk-return, corporate governance, management turnover, litigation, regulatory investigation, M&A and spin-offs, related party transactions, etc). We adopt the content analysis methodology that is well-established in social sciences and communication research (Neuendorf, 2002; Krippendorff, 2004; Riffe, Lacy and Fico, 2005) . See Appendix 1 for details.

Sample selection, variable measurement and testing method (2)

• Explanatory variables: our focus is on media coverage as the key explanatory variable. Following Ferreira and Laux (2007), Chen, Goldstein and Jiang (2007), Wei and Zhang (2006), and Gaspar and Massa (2006) amongst others, we include the following control variables in the stock price informativeness and stock trading tests: profitability (ROE), profits volatility (VROE), financial leverage (LEVERAGE), market-to-book ratio (MTB), natural log of market capitalization of equity (SIZE), number of analysts following (ANALYST), percentage ownership by institutional investors (INSTITUTION), a dividend payer dummy (DD), firm age since listing (AGE), year dummies, industry dummies, and a stock exchange dummy. In addition, we add systematic risk (BETA) as an additional control variable in the stock price informativeness tests following Dasgupta, Gan and Gao (2008). Institutional controls specific to China (e.g. ST, dual listing, real identity of beneficial controller) are also included. See Table 1 for definitions.

Sample selection, variable measurement and testing method (3)

• Pooled cross-sectional OLS regression–Typically used in prior research, but there is concern that the sample observations are not random (independent) drawings from the population.

• System of equations method (2SLS and 3SLS)

– Media coverage and stock turnover may be jointly determined (i.e. endogeneity)—2SLS. There may be cross-equation correlation in the error terms—3SLS.

• Fixed effects panel data model– We use first differencing to control for unobserved (but time-constant) firm effects and strengthen the inference of causality.

Sample selection, variable measurement and testing method (4)

Empirical resultsTable 2: Descriptive and summary statistics Panel A. Descriptive statistics Variable N Mean Median Maximum Minimum Std. Dev.

LNRSQ 8792 0.45 0.40 7.61 -2.81 0.96

RSQ 8792 0.42 0.42 0.94 0.00 0.19

TURNOVER 8219 1.60 1.27 13.32 0.02 1.11

HITS 8420 41.79 34.00 472.00 0.00 34.39

CONTENT 8420 1.80 0.00 30.51 0.00 3.19

SIZE 8228 21.41 21.35 27.60 18.77 0.89

LEVERAGE 8231 49.04 48.92 100.00 0.81 19.92

MTB 8234 4.07 2.65 100.00 0.00 6.80

BETA 8792 1.06 1.08 2.42 -0.21 0.25

A_TRADABLE 8220 0.39 0.38 1.00 0.02 0.14

AGE 8227 9.79 9.00 49.00 -6.00 4.28

SHAREHOLDER 8226 10.37 10.37 13.60 7.42 0.86

CONCENTRATION 8234 60.24 61.80 99.48 0.00 13.13

ROE 8225 0.02 0.06 75.91 -38.01 1.08

VROE 7052 0.05 0.01 47.67 0.00 0.88

DY 8225 0.01 0.00 0.24 0.00 0.01

RESIDUALVOL 8792 0.00 0.00 0.01 0.00 0.00

ABS_PAST_RET 8207 0.17 0.12 2.76 0.00 0.20

LAG_RETURN 8207 0.04 -0.05 2.76 -0.40 0.26

ANALYST 3951 2.92 0.00 29.00 0.00 5.71

INSTITUTION 4712 10.39 2.84 109.69 0.00 15.83

Table 3. Correlation matrix

(1)

LNRS

(2)

TURN-

OVER

(3) HITS (4)

CONTENT

(5)

SIZE

(6)

LEVER

(7)

MTB (8) BETA

(9)

TRADLE

(10)

AGE

(11)

SHARE-

HOLDER

(12)

CONCENT

(13)

ROE

(14)

VROE (15) DY

(16)

RESID_VOL

(17)

ABS_PAST_

RET

(18)

LAG_RET

(19)

ANALYST

(20)

INSTI-

TUTION

1.00 0.26 1.00 0.21 0.36 1.00 0.19 0.31 0.44 1.00 0.11 -0.03 0.35 0.26 1.00 0.09 0.08 0.07 0.09 -0.09 1.00 0.22 0.08 0.03 0.06 0.05 0.09 1.00 -0.60 0.23 -0.09 -0.08 -0.26 0.03 -0.09 1.00 0.09 0.12 0.12 0.11 -0.16 0.08 -0.01 -0.06 1.00 0.11 -0.01 0.11 0.07 0.02 0.14 0.06 -0.04 0.28 1.00 -0.30 -0.22 0.06 0.05 0.35 0.01 -0.16 0.10 0.16 0.18 1.00 -0.02 -0.07 -0.03 -0.05 0.19 -0.12 0.03 -0.01 -0.80 -0.40 -0.29 1.00 -0.04 -0.04 0.00 0.00 0.16 -0.13 -0.04 -0.05 0.00 -0.04 0.01 0.04 1.00 0.06 0.02 0.03 0.03 -0.04 0.05 0.01 -0.05 -0.01 0.00 -0.02 0.01 -0.01 1.00 -0.14 -0.07 0.15 0.07 0.31 -0.15 -0.08 -0.16 -0.12 -0.13 0.10 0.18 0.12 -0.03 1.00 0.63 0.53 0.30 0.25 0.02 0.11 0.17 0.06 0.11 0.11 -0.19 -0.06 -0.06 0.03 -0.22 1.00 0.04 0.12 0.09 0.02 -0.05 -0.01 0.03 0.06 -0.06 -0.10 -0.11 0.05 -0.02 0.04 -0.04 0.06 1.00 0.08 0.02 0.18 0.07 0.38 -0.08 0.05 -0.12 -0.04 -0.08 -0.05 0.13 0.11 -0.04 0.17 0.00 0.12 1.00 0.11 0.09 0.46 0.27 0.66 -0.03 0.06 -0.19 -0.03 -0.05 0.01 0.16 0.11 -0.02 0.32 0.04 0.02 0.33 1.00 0.22 -0.02 0.33 0.18 0.58 -0.08 0.11 -0.29 0.01 -0.07 -0.25 0.24 0.12 -0.02 0.20 0.06 0.04 0.39 0.67 1.00

Brief note on summary and descriptive stats

• Average R-squared is 0.42.• HITS has a wide dispersion. In contrast, CONTENT for many firms is zero. This is largely the result of our use of a random sampling procedure in order to make the task manageable (we first randomly select one day per month during the period 2000-2006. All news articles for each company on these days are then identified and content analyzed).• Correlation between HITS and CONTENT is 0.44—they are positively but imperfectly correlated. • IV and stock turnover are each correlated with media coverage in the expected direction (significantly +ve).

Table 4 (partial). Distribution of news coverage over time and across stock exchanges (a positive time trend, with no significant difference between the 2 stock exchanges)

Table 4: Distribution of news coverage over time and across stock exchanges

Panel A: News coverage measures for overall sample

CONTENT HITS Year

N MEAN STD MIN MAX MEAN STD MIN MAX

2000 1060 0.46 1.56 0.00 12.88 10.45 8.61 0.00 78.00

2001 1139 0.74 2.03 0.00 15.16 13.86 8.86 0.00 75.00

2002 1210 2.47 3.47 0.00 19.93 41.77 18.95 3.00 151.00

2003 1277 1.51 2.77 0.00 16.25 26.97 14.28 0.00 104.00

2004 1377 1.00 2.33 0.00 15.67 32.61 24.88 0.00 318.00

2005 1392 2.19 3.64 0.00 30.51 76.99 40.14 0.00 472.00

2006 1458 3.43 3.93 0.00 20.71 67.71 32.12 0.00 313.00

Panel B: News coverage measures for companies listed on the Shanghai Stock Exchange

CONTENT HITS Year

N MEAN STD MIN MAX MEAN STD MIN MAX

2000 559 0.88 2.06 0.00 12.88 14.71 8.64 0.00 78.00

2001 637 1.19 2.44 0.00 15.16 16.96 9.74 0.00 75.00

2002 707 2.36 3.49 0.00 19.93 37.62 17.61 3.00 137.00

2003 774 1.49 2.76 0.00 16.25 25.99 14.08 0.00 100.00

2004 835 0.87 2.14 0.00 11.63 28.61 24.05 0.00 243.00

2005 838 2.02 3.60 0.00 30.51 74.15 41.86 1.00 472.00

2006 852 3.5 3.92 0.00 20.71 68.67 29.85 0.00 313.00

Table 5. Determinants of media coverage (Panel A): HITS Variable \ model (1) (2) (3) (4) (5) (6) (7)

Panel A. Media coverage is measured by HITS

SIZE 0.23 0.20 0.20 0.20 0.23 0.22 0.21

(23.41) (15.83) (15.87) (16.55) (17.54) (14.52) (16.90)

MTB 0.06 0.03 0.03 0.01 0.01 0.00 0.02

(5.18) (2.48) (2.46) (1.37) (0.81) (0.19) (1.45)

TURNOVER 0.05 0.08 0.08 0.08 0.08 0.09 0.08

(5.21) (8.22) (7.76) (7.82) (8.46) (8.17) (8.31)

SHAREHOLDER 0.00 0.00 0.00 0.02 -0.00 0.00 -0.00

(0.47) (0.20) (0.17) (2.56) (0.26) (0.43) (0.26)

CONCEN -0.17 -0.16 -0.16 -0.18 -0.18 -0.19

(6.71) (5.38) (5.39) (6.13) (5.48) (6.51)

INSTITUTION 0.003 0.002 0.003

(5.69) (3.46) (5.89)

ROE 0.00 0.00 0.00 -0.10 -0.11 -0.08

(0.47) (0.76) (0.76) (2.84) (3.03) (2.47)

DY 0.64 0.46 0.49 0.89 0.79 0.83

(1.45) (1.19) (1.26) (2.24) (1.99) (2.16)

ANALYST 0.01 0.01 0.01 0.007

(6.77) (6.75) (6.96) (4.19)

OTHEROWNERS 0.04 0.06 0.04

(1.93) (3.18) (2.06)

SOECG -0.01 0.03 0.00

(0.63) (1.25) (0.16)

SOELG -0.01 0.05 -0.00

(0.87) (0.28) (0.32)

Table 5. Determinants of media coverage (Panel B): CONTENTPanel B. Media coverage is measured by CONTENT

SIZE 0.18 0.14 0.14 0.14 0.21 0.16 0.20

(13.48) (5.74) (5.83) (5.90) (8.40) (5.42) (8.67)

MTB 0.04 0.03 0.03 0.03 0.02 0.03 0.02

(2.95) (1.41) (1.38) (1.45) (1.09) (1.18) (0.94)

TURNOVER 0.03 0.02 0.02 0.02 0.03 0.02 0.02

(2.58) (1.43) (1.03) (1.27) (1.56) (1.03) (1.50)

SHAREHOLDER 0.01 0.06 0.05 0.07 0.02 0.02 0.19

(1.42) (2.78) (2.75) (3.77) (1.15) (2.00) (0.87)

CONCEN -0.10 -0.06 -0.06 -0.09 -0.06 -0.09

(2.66) (1.04) (1.05) (1.58) (0.95) (1.82)

INSTITUTION 0.00 -0.00 0.00

(0.47) (0.21) (0.59)

ROE -0.00 -0.00 -0.00 -0.16 -0.13 -0.15

(0.61) (0.37) (0.38) (2.44) (1.84) (2.39)

DY 0.56 0.52 0.59 1.18 1.29 0.75

(0.84) (0.72) (0.78) (1.59) (1.62) (1.04)

ANALYST 0.01 0.01 0.01 0.00

(3.35) (3.34) (3.23) (2.52)

OTHEROWNERS 0.00 0.00 -0.00

(0.04) (0.21) (0.07)

SOECG -0.00 0.00 -0.01

(0.11) (0.19) (0.24)

SOELG 0.00 -0.03 -0.00

(0.09) (0.80) (0.03)

RESIDUALVOL 36.52 64.83 61.69 91.09 84.31 90.89 64.91

Table 5. Determinants of media coverage (summary of key results)

• As a first control for possible endogeneity, we use lagged values of stock turnover in all model specifications here. Later a simultaneous equation is estimated.• Key results: • A much better “fit” when measuring media coverage by HITS (which captures mainly the quantity of media coverage), with R-squared ranging from 65% to 70%.

– Size, (lagged) turnover, institutional ownership, analyst following, ST companies, and those controlled by private investors (relative to those controlled by SAMB) have a consistent strong positive association with media coverage – Shareholding concentration and companies with both A and B shares have a consistent strong negative association with media coverage

• When media coverage is measured by CONTENT (with emphasis on the quality of media reports), goodness of fit ranges from 22% to 27%.

– Size, analyst following, and residual volatility have a consistent strong positive association with media coverage – Companies with both A and B shares has a consistent strong negative association with media coverage

• The results here in part results from the lack of sufficient dispersion in CONTENT.

Table 6. Relationship between share price informativeness and media coverage

Variable \ model (1) (2) (3) (4) (5) (6) (7)

Panel A. Media coverage is measured by HITS

HITS 0.16 0.17 0.17 0.16 0.05 0.15 0.26

(14.19) (14.64) (14.76) (13.79) (6.49) (12.25) (11.66)

SIZE 0.06 0.04 0.03 0.01 0.07 -0.04 0.07

(6.34) (3.87) (3.49) (1.43) (7.25) (3.66) (3.76)

MTB 0.008 0.009 0.009 0.01 0.008 0.01

(7.52) (8.50) (8.78) (11.65) (8.43) (7.39)

LEVER 0.04 0.05 0.06 0.09 0.09 0.04 0.05

(3.97) (4.82) (5.13) (7.80) (7.33) (3.56) (3.52)

BETA -1.71 -1.75 -1.76 -1.75 -1.73 -1.74 -1.59

(68.87) (70.06) (70.22) (67.66) (64.32) (60.94) (44.00)

SHAREHOLDER -0.22 -0.18 -0.18 -0.20 -0.20 -0.16 -0.22

(26.14) (21.55) (21.40) (23.37) (22.66) (16.86) (13.99)

CONCEN -0.08 -0.05 -0.09 -0.15 0.004

(2.08) (1.40) (2.27) (3.57) (0.07)

INSTITUTION -0.002

(3.11)

ROE -0.02 -0.03 -0.03 -0.03 -0.03

(3.72) (3.91) (3.84) (4.74) (2.71)

VROE 0.01 0.01 0.01 0.01 -0.003

(2.81) (3.09) (3.04) (2.83) (0.17)

DD -0.25

(20.22)

DY -8.01 -8.25

(19.08) (19.67)

Table 6. Relationship between share price informativeness and media coverage (summary of key results)

• The model specifications have good fit, with R-squared ranging from 55% to 72% (mostly over 70%).• Media coverage (no matter if HITS or CONTENT is used), MTB, leverage, age, absolute past return, and ST firms are positively associated with stock price informativeness. • Beta, No. of shareholders, ROE, and DY are negatively associated with stock price informativeness. • Relative to firms controlled by private parties, firms controlled by the central government (firms controlled by local governments) have greater (lower) stock price informativeness. • Thus, greater media coverage is associated with higher stock price informativeness, even after controlling for other relevant factors.

Table 7. Relationship between share turnover and media coverage (Panel A): HITS

Variable \ model (1) (2) (3) (4) (5) (6) (7)

Panel A. Media coverage is measured by HITS

HITS 0.09 0.08 0.08 0.09 0.17 0.20 011

(11.45) (10.23) (10.20) (10.37) (20.07) (25.35) (7.55)

SIZE -0.03 -0.04 -0.04 -0.03 0.01 0.00 0.00

(4.16) (4.80) (4.48) (3.52) (1.64) (0.51) (0.24)

MTB 0.001 0.001 0.00 0.00 0.003 0.00

(1.18) (1.05) (0.8) (1.11) (4.39) (1.12)

LEVER 0.03 0.03 0.06 0.03 0.02 0.04 0.02

(3.16) (3.53) (5.13) (3.26) (2.09) (3.56) (1.43)

BETA 0.67 0.68 0.69 0.68 0.66 0.68 -1.59

(29.89) (30.40) (30.22) (30.40) (27.30) (29.76) (44.00)

SHAREHOLDER -0.09 -0.09 -0.09 -0.09 -0.11 -0.12 -0.22

(12.69) (12.29) (12.38) (12.55) (13.92) (15.97) (13.99)

CONCEN -0.20 -0.21 -0.18 -0.17 -0.17 -0.17 -0.14

(5.73) (5.94) (5.22) (4.87) (4.63) (4.94) (2.46)

INSTITUTION -0.00

(10.25)

ROE 0.002 0.001 -0.00 -0.00 0.00

(0.42) (0.20) (0.13) (0.53) (0.02)

VROE -0.003 -0.004 -0.00 0.00 -0.00

(0.79) (0.76) (0.72) (0.19) (0.33

DD 0.02

(2.36)

DY 2.49 2.66

(6.62) (7.07)

Table 7. Relationship between share turnover and media coverage (summary of key results)

• The model specifications have good fit, with R-squared ranging from 55% to 65% (similar no matter how media coverage is measured).• Media coverage (no matter if HITS or CONTENT is used), beta, dividend yield, and those with both H-share and A-shares are positively associated with stock turnover.•No. of shareholders, shareholding concentration, age, residual volatility and absolute past return are negatively associated with stock turnover. • Thus, greater media coverage is associated with higher levels of stock turnover, even after controlling for other relevant factors.

Further analyses

• To address the possible endogeneity problem involving media coverage and stock turnover, we run 2SLS (assuming no cross-equation correlation in the error terms) and 3SLS (appropriate if there is cross-equation correlation in the error terms).• The key results remain qualitatively the same as those obtained when running OLS for the separate equations.• To further support the inference of causality, we are in the process of estimating a fixed effects first-differencing panel data model.

Further analyses (2)

• An on-going effort looks at the possible sources of the documented media effects (e.g. “the media breeds familiarity” vs “the media conveys substantive information”). This involves constructing more refined measures of media coverage, one that emphasizes mainly the quantity of media, the other emphasizing the quality (e.g. how much the newspaper reports relate to fundamental corporate information). • Some preliminary analyses divide media coverage into those that have high quantity but low quality, and those that have high quality but low quantity. Although the number of cases with divergent quality-quantity is relatively small, the preliminary results (see online version of paper) suggest that the quantity of media coverage has a stronger and more consistent impact on stock price informativeness and stock turnover. However, in many cases the quality of media coverage has incremental power beyond the quantity dimension of media coverage. • Work is in progress using other measures of information flow or information risk (PIN, bid-ask spread, etc).

Conclusion• Media coverage is positively associated with greater stock price informativeness and stock turnover (so yes to both H1 and H2). This holds no matter how media coverage is measured (quantity or quality). • Thus in the Chinese emerging stock market, media coverage plays a positive role in causing more firm-specific relative to market-wide information to be impounded into stock prices. Given that greater media coverage is also associated with higher levels of stock trading, a trading link may be established as the mechanism through which the media exerts its influence.• More work remains in order to answer H4: Does the media effect arise from media reports providing new, substantive information, or does it arise from media coverage breeding familiarity? • The preliminary evidence suggest the quantity of media coverage has a stronger and more consistent impact than does the quality of media coverage, although the latter has incremental power in some cases. Better empirical measures of the quality of media coverage are necessary and there lies the challenge. • Suggestions/comments are welcome.