This study investigates the relationship between corporate governance and equity liquidity. We suggest that the firms with poor financial transparency and information disclosure have higher agency cost due to managers’ increasing incentives to use their information advantage to pursue their private benefit of control. When agency problem becomes worse, the wealth and rights of small shareholders are easily exploited by insiders such as executives or controlling owners, which causes worse corporate governance. The company with poorer disclosure practice accompanies worse corporate governance and higher information asymmetric risk. Liquidity suppliers will broaden the spread of firm’s equity when it exhibit poor corporate governance, and this price protection action will decrease market liquidity of the stock.
In this study, we use S&P T&D ranking as a proxy variable for corporate governance, and employ it to examine whether firms with higher rankings have better market liquidity of their stocks. We provide four variables, the quoted half-spread, the proportional half-spread, the effective spread, and the relative effective spread, suggested by previous studies to measure equity liquidity. In addition, the information asymmetry component of the effective spread is estimated to measure the information asymmetry cost faced by liquidity suppliers to compensate possible loss when they trade with unidentifiable informed traders.
The empirical evidence supports our hypothesis that the companies with better corporate governance have better market liquidity of their stocks: both the composite and annual basis T&D final rankings have significantly negative partial effects on the quoted half-spread and the effective spread under the 3SLS and GMM estimations. We also find that both the composite and annual basis T&D final rankings are significantly and negatively related to the information asymmetry component of the effective spread, which implies that better
disclosure practice can reduce the information asymmetric risk perceived by market and thus lower the spread of the equity by decrease the information asymmetric cost requested by liquidity supplier to compensate possible loss from informed trading activities. Besides, we find that none of our liquidity measures represents a significant explanatory variable to the T&D ranking in our simultaneous equations, so there is weak evidence that the simultaneity problem exists in our data.
This study has several contributions to the financial literature and practice. First, we link the conceptions of disclosure practice, information asymmetry, agency problem, and corporate governance to the equity liquidity. The empirical results are not only consistent with our prediction but also statistically significant, which supports our hypothesis that better corporate governance accompanies better equity liquidity. Second, this study employs two advanced estimation methods, 3SLS and GMM, to provide more reliable empirical evidence for examining the impact of corporate governance on equity liquidity. Third, we additionally estimate the information asymmetry components of the effective spread to measure the information asymmetry cost requested by liquidity suppliers to compensate possible loss from informed trading activities. We find that the T&D rankings are significantly and negatively related to the information asymmetry component, implying the worse disclosure practice lower the equity liquidity by increasing the information asymmetric cost requested by liquidity suppliers under the fact that the order processing cost are usually fixed. Fourth, our study indirectly examines the quality of S&P T&D ranking, and we suggest that it may have some measurement error in assessing firm’s disclosure practice. Therefore, investors should be more careful about making use of this ranking directly to assess the extent of financial transparency and disclosure practice of a company. Finally, the results of our study have some important meaning for corporate governance: the managers should endeavor to conform to various disclosure regulations and investor protection codes by disclosing firm’s information
to the best of their abilities. When a firm can provide better disclosure and transparency, the information asymmetry and agency problem will be mitigated, and the quality of firm’s corporate governance improves. Consequently, the firm will have smaller information asymmetry component, effective spread, and quoted spread, which implies better market liquidity of its stock.
References
Agrawal, V., Kothare, M., Rao, R.K.S., and Wadhwa, P., 2004, “Bid-ask spreads, informed investors, and firm’s financial condition,”The Quarterly Review of Economics and Finance 44, 58–76.
Alves, C., and Mendes, V., 2004, “Corporate governance policy and company performance: the Portuguese case,” Corporate Government: An International Review 12, 290-301.
Barclay, M.J., Christie, W.G., Harris, J.H., Kandel, E., and Schultz, P.H., 1999, “Effects of market reform on the trading costs and depths of Nasdaq stocks,” Journal of Finance 54, 1-34.
Botosan, C.A., 1997, “Disclosure level and rs, and the the cost of equity capital,” the Accounting Review 72:3, 323-350.
Brockman, P., and Chung, D.Y., 2003, “Investor protection and firm liquidity,” Journal of Finance 58, 921-937
Brown, L.D., and Caylor, M.L., 2004, “Corporate governance and firm performance,” Georgia State University, Working Paper, 1-30.
Cheng, A.C.S., Collins, D., Huang, H., 2003, “The effect of the S&P T&D rankings on market beta, abnormal returns and earnings response coefficients in the period surrounding the report release date,” University of Houston C.T. Bauer College of Business, Working Paper, 1-44.
Conyon, M.J. and Peck, S.I., 1998, “Board size and corporate performance: evidence from European countries,” The European Journal of Finance 4, 291-304.
Copeland, T.E., and Galai, D., 1983, “Information effects of the bid-ask spread,” Journal of Finance 38, 1457-1469.
Cremers, K.J.M, and Nair, V.B., 2004, “Governance mechanisms and equity prices,” Journal of Finance, forthcoming.
Demsetz, H., 1968, “The cost of transacting,” Quarterly Journal of Economics 82, 33-53.
Durnev, A., and Kim, E., 2002, “To steal or not to steal: Firm attributes, legal environment, and valuation,” University of Michigan Business School, Working Paper,
Dye, R.A., 1985, “Disclosure of nonproprietary information,” Journal of Accounting Research, 123-145.
George, T.J., Kaul, G., and Nimalendran, M., 1991, “Estimation of bid-ask spread and its components:
A new approach,” The Review of Financial Studies 4, 623-656.
Glosten, L.R., and Milgrom, P.R., 1985, “Bid, ask and transaction prices in a specialist market with heterogeneously informed traders,” Journal of Financial Economics 14, 71-100.
Gompers, P.A., Ishii, J.L., and Metrick, A., 2003, “Corporate governance and equity prices,”
Quarterly Journal of Economics 118, 107-155.
Himmelberg C.P., Hubbard R.G., and Palia, D., 1999, “Understanding the determinants of managerial ownership and the link between ownership and performance,” Journal of Financial Economic 53, 353-384.
Ho, S.S.M., and Wong, K.S., 2001, “A study of the relationship between corporate governance structures and the extent of voluntary disclosure,” Journal of International Accounting, Auditing
& Taxation 10, 139-156.
Huang, R.D., and Stoll, H.R., 1994, “Market microstructure and stock return predictions,” Review of Financial Studies 7, 179-213.
Huang, R.D., and Stoll, H.R., 1996, “Dealer versus auction markets: A paired comparison of execution costs on NASDAQ and the NYSE,” Journal of Financial Economics 41, 313-357.
Johon, S., Boone, P., Breach, A., and Friedman, E., 2000, “Corporate governance in the Asian financial crisis,” Journal of Financial Economics 58, 141-186.
Klapper, L.F., and Love, I., 2004, “Corporate governance, investor protection, and performance in emerging markets,” Journal of Corporate Finance 10, 703-728
Kyle, A.S., 1985, “Continuous auctions and insider trading,” Econometrica 53, 1315-1335.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1997, “Legal determinants of external finance,” Jounal of Finance 52, 1131-1150.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1998, “Law and finance,” Jounal of Political Economy 106, 1115-1155.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 1999, “Corporate ownership around the world,”
Jounal of Finance 54, 471-517.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R., 1998, “Investor protection and corporate
governance,” Jounal of Financial Economic 58, 3-27.
Lang, M. and Lundholm, R., 1993, “Cross-sectional determinants of analyst ratings of corporate disclosures,” Journal of Accounting Research, 246-271.
Lang, M. and Lundholm, R., 1999, “Corporate disclosure policy and analyst behavior,” The Accounting Review 71, 467-493.
Lee, T.-S., and Yeh, Y.-H., 2004, “Corporate governance and financial distress: evidence from Taiwan,” Corporate Government: An International Review 12, 378-388.
Leuz, C., Nanda, D, and Wysocki, P.D., 2003, “Earnings management and investor protection:an international comparison,” Journal of Financial Economics 69, 505–527
Lin, J.-C., 1992, “Order persistence, adverse selection, and gross profits earned by NYSE specialists,”
Journal of Finance 48, 1108.
Lin, J.-C., Sanger, G.C., and Booth, G., 1995, “Trade size and components of the bid-ask spread,” The Review of Financial Studies 8,1153-1183.
Lippman, S.A., and McCall, J.J., 1986, “An operational measure of liquidity,” American Economic Review 76, 43-55.
Lowenstein, L., 1996, “Financial transparency and corporate governance,” Columbia Law Review 96, April.
Mitton, T., 2002, “A cross-firm analysis of the impact of corporate governance on the East Asian financial crisis,” Journal of Financial Economics 64, 215-241.
Nelson, J., 2005, “Corporate governance practices, CEO characteristics and firm performance,”
Journal of Corporate Finance 11, 197-228
Patel, S.A., and Dallas, G., 2002, “Transparency and Disclosure: Overview of Methodology and Study Results – United States,” Standard & Poor’s, http://governance.standardandpoors.com., 1-29.
Stoll, H.R., 1978a, “The supply of dealer services in securities markets,” Journal of Finance 33, 1122-1151.
Stoll, H.R., 1978b, “The pricing of security dealer services: An empirical study of NASDAQ stocks,”
Journal of Finance 33, 1153-1172.
Stoll, H.R., 2000, “Friction,” Journal of Finance 55, 1479-1514.
Vafeas, N., 1999, “Board meeting frequency and firm performance,” Journal of Financial Economics 53, 113-142.
Van Ness, B.F., Van Ness, R.A., and Warr R.S., 2001, “How well do adverse selection components measure adverse selection?” Finacial Management, 77-98.
Welker, M., 1995, “Disclosure policy, information asymmetry, and liquidity in equity markets,”
Contemporary Accounting Research 11, 801-827.
Wooldridge, J. M., 2002, Econometric Analysis of Cross Section and Panel Data, Massachusetts Institute of Technology, Cambridge, Massachusetts, the MIT press.
TABLE 1
Descriptive statistics and Pearson correlations coefficients of the selected liquidity measures and their control variables
This table contains descriptive statistics and Pearson correlations coefficients of our five liquidity measures and their control variables. Our samples are the S&P 500 constituents stocks listed in NYSE from January 1 2002– December 31 2002, and the sample size is 341.
Panel A :
Descriptive statisticsN Mean Std Dev Minimum Maximum
QSP (cent’s) 341 2.2046 0.0060 0.7839 4.3059
PSP (%) 341 0.0716 0.0362 0.0281 0.3220
ESP (cent’s) 341 1.6166 0.0046 0.6652 3.6069
RESP (%) 341 0.0519 0.0246 0.0218 0.2170
INF (cent’s) 341 0.6577 0.2170 0.2172 1.3402
CLP 341 38.0115 18.7849 4.5627 121.7333
DOLVOL (million’s) 341 66.6429 80.4993 4.4059 610.3353
RETSTD 341 0.0264 0.0098 0.0138 0.0768
QSP= the quoted half-spread
PSP= the proportional quoted half-spread ESP= the effective spread
RESP= the relative effective spread
INF= the dollar value of the information asymmetry component of the effective spread CLP = the closing price
DOLVOL = the daily dollar volume
RETSTD= the return standard deviation in prior year
TABLE 1 (continued)
PANEL B: Pearson correlation coefficients
QSP PSP ESP RESP INF CLP DOLVOL RETSTD
QSP 1
PSP -0.4015** 1
(<0.0001)
ESP 0.9694** -0.4732** 1
(<0.0001) (<0.0001)
RESP -0.4260** 0.9891** -0.4729** 1
(<0.0001) (<0.0001) (<0.0001)
INF 0.9668** -0.4278** 0.9441** -0.4469** 1
(<0.0001) (<0.0001) (<0.0001) (<0.0001)
CLP 0.8107** -0.6738** 0.8834** -0.6669** 0.8010** 1
(<0.0001) (<0.0001) (<0.0001) (<0.0001) (<0.0001)
DOLVOL -0.0281 -0.3437** 0.1328** -0.2948** -0.09032** 0.3494** 1
(0.6045 ) (<0.0001) (0.0141) (<0.0001) (0.0959) (<0.0001)
RETSTD -0.3301** 0.6798** -0.3123** 0.7232** -0.3643** -0.4590** -0.0107 1
(<0.0001) (<0.0001) (<0.0001) (<0.0001) (<0.0001) (<0.0001) (0.8445)The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
TABLE 2
Descriptive statistics and Pearson correlations coefficients of the S&P T&D final rankings and their control variables
This table contains descriptive statistics and Pearson correlations coefficients of two S&P T&D final rankings and their control variables. Our samples are the S&P 500 constituents stocks listed in NYSE from January 1 2002– December 31 2002, and the sample size is 341.
Panel A :
Descriptive statisticsN Mean Std Dev Minimum Maximum
CFR 341 7.5455 0.5161 7.0000 9.0000
AFR 341 4.7771 0.9986 1.0000 8.0000
SIZE (million’s) 341 39391 107730 669 887515
AIP (%) 341 30.5985 23.2217 0.0000 93.2126
RETSTD 341 0.0264 0.0098 0.0138 0.0768
CFR= the composite basis S&P T&D final ranking AFR= the annual basis S&P T&D final ranking SIZE= the firm’s total asset at the end of 2002
AIP= the asst-in-place defined as the book value of fix asset divided by total asset RETSTD= the return standard deviation in prior year
Panel B: Pearson correlation coefficients
CFR AFR SIZE AIP RETSTD
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 3
OLS, 3SLS and GMM estimation results of the quoted half-spread and composite basis S&P T&D final ranking
The empirical results show that under 3SLS and GMM estimations the composite basis T&D ranking is significantly and negatively with the quoted half-spread in the first equation. In the second equation, the quoted half-spread does not reveal significant negative relation to the composite basis T&D ranking, indicating that there might be no simultaneity existing in the determination of spread and disclosure practice.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 6.1712** 9.4445** 9.2985**
(<.0001) (<.0001) (<.0001)
CFR － -0.0171 -0.5168** -0.4688**
(0.4963) (<.0001) (0.0002)
CLP ＋ 0.0356** 0.0339** 0.0335**
(<.0001) (<.0001) (<.0001)
lnDOLVOL － -0.3138** -0.2793** -0.2911**
(<.0001) (<.0001) (<.0001)
RETSTD ＋ 11.8189** 10.1672** 10.4431**
(<.0001) (<.0001) (<.0001)
Adj.
R
2 0.8435 0.6550 0.6903Obs. 341
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 6.3579** 6.0121** 6.2441**
(<.0001) (<.0001) (<.0001)
QSP － -0.0275 -0.0454 -0.0502
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 4
OLS, 3SLS and GMM estimation results of the quoted half-spread and annual basis S&P T&D final ranking
The empirical results show that under 3SLS and GMM estimations the annual basis T&D ranking is significantly and negatively with the quoted half-spread in the first equation. In the second equation, the quoted half-spread does not reveal significant negative relation to the annual basis T&D ranking, indicating that there might be no simultaneity existing in the determination of spread and disclosure practice.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 6.0431** 7.1516** 6.9888**
(<.0001) (<.0001) (<.0001)
AFR － -0.0006 -0.3249** -0.2931**
(0.9638) (<.0001) (0.0003)
CLP ＋ 0.0357** 0.0339** 0.0330**
(<.0001) (<.0001) (<.0001)
lnDOLVOL － -0.3140** -0.2740** -0.2735**
(<.0001) (<.0001) (<.0001)
RETSTD ＋ 11.9265** 4.6206 6.3046
(<.0001) (0.0926) (0.0524)
Adj.
R
2 0.8432 0.5534 0.6061Obs. 341
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 2.8155** 2.4873* 2.7059**
(0.0085) (0.0194) (0.0055)
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 5
OLS, 3SLS and GMM estimation results of the effective spread and composite basis S&P T&D final ranking
The empirical results show that under 3SLS and GMM estimations the composite basis T&D ranking is significantly and negatively with the effective spread in the first equation. In the second equation, the effective spread does not reveal significant negative relation to the composite basis T&D ranking, indicating that there might be no simultaneity existing in the determination of spread and disclosure practice.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 3.1752** 5.3428** 5.3952**
(<.0001) (<.0001) (<.0001)
CFR － -0.0218 -0.3508** -0.3302**
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 6.4060** 6.0807** 6.3222**
(<.0001) (<.0001) (<.0001)
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 6
OLS, 3SLS and GMM estimation results of the effective spread and annual basis S&P T&D final ranking
The empirical results show that under 3SLS and GMM estimations the annual basis T&D ranking is significantly and negatively with the effective spread in the first equation. In the second equation, the effective spread does not reveal significant negative relation to the annual basis T&D ranking, indicating that there might be no simultaneity existing in the determination of spread and disclosure practice.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 2.9877** 3.7785** 3.6907**
(<.0001) (<.0001) (<.0001)
AFR － 0.0039 -0.2222** -0.2025**
(0.6752) (<.0001) (0.0005)
CLP ＋ 0.0275** 0.0260** 0.0258**
(<.0001) (<.0001) (<.0001)
lnDOLVOL － -0.1536** -0.1259** -0.1275**
(<.0001) (<.0001) (<.0001)
RETSTD ＋ 9.8913** 4.5792** 5.9191**
(<.0001) (0.0172) (0.0094)
Adj.
R
2 0.8646 0.6275 0.6662Obs. 341
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 2.8441** 2.5891** 2.6474**
(0.0048) (0.0089) (0.0037)
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 7
OLS, 3SLS and GMM estimation results of the proportional quoted half-spread and composite basis S&P T&D final ranking
The empirical results show that under all estimation methods the estimated coefficients of the composite basis T&D ranking are insignificant in the first equation. The estimated coefficients of the proportional quoted half-spreads under all estimation methods are also insignificant in the second equation.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 0.3258** 0.3187** 0.2881**
(<.0001) (<.0001) (<.0001)
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 6.2965** 6.0806** 5.9648**
(<.0001) (<.0001) (<.0001)
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 8
OLS, 3SLS and GMM estimation results of the proportional quoted half-spread and annual basis S&P T&D final ranking
The empirical results show that under all estimation methods the estimated coefficients of the annual basis T&D ranking are insignificant in the first equation. The estimated coefficients of the proportional quoted half-spreads under all estimation methods are also insignificant in the second equation.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 0.3045** 0.3029** 0.2779**
(<.0001) (<.0001) (<.0001)
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 2.2607* 1.4168 1.1862
(0.0238) (0.1820) (0.2266)
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 9
OLS, 3SLS and GMM estimation results of the relative effective spread and composite basis S&P T&D final ranking
The empirical results show that under all estimation methods the estimated coefficients of the composite basis T&D ranking are insignificant in the first equation. The estimated coefficients of the relative effective spreads under all estimation methods are also insignificant in the second equation.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 0.1928** 0.1819** 0.1687**
(<.0001) (<.0001) (<.0001)
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 6.3411** 6.1241** 6.0248**
(<.0001) (<.0001) (<.0001)
RESP － -0.6822 0.4814 0.9616
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 10
OLS, 3SLS and GMM estimation results of the relative effective spread and annual basis S&P T&D final ranking
The empirical results show that under all estimation methods the estimated coefficients of the annual basis T&D ranking are insignificant in the first equation. The estimated coefficients of the relative effective spreads under all estimation methods are also insignificant in the second equation.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 0.1764** 0.1742** 0.1592**
(<.0001) (<.0001) (<.0001)
AFR － -0.0001 0.0005 0.0004
(0.8668) (0.8748) (0.8713)
CLP － -0.0003** -0.0003** -0.0003**
(<.0001) (<.0001) (<.0001)
lnDOLVOL － -0.0088** -0.0089** -0.0076**
(<.0001) (<.0001) (<.0001)
RETSTD ＋ 1.5717** 1.5891** 1.3423**
(<.0001) (<.0001) (<.0001)
Adj.
R
2 0.7477 0.7471 0.7359Obs. 341
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 2.3242* 1.4764 1.2000
(0.0185) (0.1578) (0.2102)
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 11
OLS, 3SLS and GMM estimation results of the information asymmetry component and composite basis S&P T&D final ranking
The empirical results show that under 3SLS and GMM estimations the composite basis T&D ranking is significantly and negatively with the information asymmetry component in the first equation. In the second equation, the information asymmetry component does not reveal significant negative relation to the composite basis T&D ranking, indicating that there might be no simultaneity existing in the determination of spread and disclosure practice.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 2.3224** 3.6756** 3.6636**
(<.0001) (<.0001) (<.0001)
CFR － -0.0040 -0.2119** -0.1964**
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 6.2964** 5.8641** 6.0649**
(<.0001) (<.0001) (<.0001)
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.
Table 12
OLS, 3SLS and GMM estimation results of the information asymmetry component and annual basis S&P T&D final ranking
The empirical results show that under 3SLS and GMM estimations the annual basis T&D ranking is significantly and negatively with the information asymmetry component in the first equation. In the second equation, the information asymmetry component does not reveal significant negative relation to the annual basis T&D ranking, indicating that there might be no simultaneity existing in the determination of spread and disclosure practice.
Panel A: Simultaneous estimation results of the first equation
Prediction OLS 3SLS GMM
Intercept 2.2846** 2.7353** 2.7148**
(<.0001) (<.0001) (<.0001)
Panel B: Simultaneous estimation results of the second equation
Prediction OLS 3SLS GMM
Intercept 2.6666* 2.3048* 2.6203**
(0.0136) (0.0303) (0.0076)
The p-value is showed in the parentheses below each coefficient estimate.
*: the coefficient estimate is statistically significant at alpha = 0.05 level.
**: the coefficient estimate is statistically significant at alpha = 0.01 level.