One of the MM theorem assumptions, trading frictionless, is the starting point in this


 Lewis Stone
 2 years ago
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1 V. Conclusion One of the MM theorem assumptions, trading frictionless, is the starting point in this study. In the U.S., Banerjee, Gatchev, and Spindt (2007) provide evidence that the NYSE commission rate varies negatively with the share turnover. Moreover, share turnover ratio is also negatively correlated with paydividend firms. In Thailand, Sareeviwattana, el. at (2006) suggests that the commission rate is determined by turnover ratio in capital market. Thus, the commission fee of securities firms comes from the turnover of the stock in the market. In 1975, turnover ratio is 0.1 times at the lowest. Therefore, SET commission rate start at 0.5% of Baht volume to survive the securities firm. After that the commission rate reduces to 0.25% because turnover ratio has much enough to make profit and capital market is developed. However, no previous study provides the direct empirical evidence about the relationship between stock market liquidity and dividend policy except Banerjee, Gatchev, and Spindt (2007). Therefore, the objective of the study is to examine the relationship between stock market liquidity and dividend policy. To test whether stock market liquidity has relationship to the propensity to pay dividend, the four liquidity proxies which are turnover ratio, illiquid Amihud ratio, the number of no trading day ratio, and daily average Baht volume to measure stock market liquidity are used and employed LOGIT model to predict the propensity to pay in the study. The result shows that the liquidity can improve prediction of the propensity to pay dividend especially turnover ratio while illiquid Amihud ratio is inconsistent with the other liquidity proxies. The two possible explanations for illiquid ratio are; First, if illiquid ratio is high in nonpayer group, it means that the changing price more in nonpayer group should be a positive return or a capital gain in order to be indifferent between capital gain and dividend yield. Second, actually, the liquid stock should be more inclined pay dividend. Lee S. Redding (1997) suggests that large companies and liquid companies are indeed the ones that are more likely to pay dividends. This model derives its result from assumptions that have been empirically tested in the literature, First, markets are imperfectly liquid. Trading 29
2 therefore incurs transaction costs. Second, institutional investors, for fiduciary and tax reason, are more likely to buy companies that pay dividends, as these large companies are led to declare dividends on their companies stocks. From the first hypothesis, it can be concluded that owner with less (more) liquid stock are more (less) propensity to pay dividends because the turnover ratio (liquidity) is the determinant of with the SET commission rate (trading friction). Moreover, it is the best (liquidity proxies) to predict the propensity to pay dividend. The implication of the study is owners less liquid stock are more the propensity to pay dividend to compensate with the higher trading friction (commission rate). On the other hand, owners with more liquid stock are less the propensity to pay dividend because investors can buy or liquidate their stock with less trading friction. Furthermore, the control variables illustrate that firm size and profitability are positively correlated with dividend policy. On the other hand, the growth opportunity has negative relationship with dividend policy. The implication of the control variables in Thailand is shown as the follows;  Firm size: Larger firms are more likely to pay dividends. It is consistent with the role of dividends to mitigate the agency cost according to Easterbrook (1984) and Jensen (1986).  Growth Opportunity: Regarding to Myer and Majluf (1984) and Myers (1984), the pecking order model of firm avoids issuing securities because of higher information asymmetries costs. Thus, the firms with higher growth opportunities are less likely to pay dividend.  Profitability: Dividends vary positively with earnings but negatively with investment according to Higgins (1972). Thus firms with the higher profitability are more propensity to pay. From the second hypothesis, I employ LOGIT model to predict the probability to pay dividend. According to table 9, it shows how better liquidity proxies predict the propensity to pay dividend. Count R 2 is the percentage of correct prediction. In other words, if the model predicts the probability to pay more than 0.5, it is implied that the firm will pay dividend. While the model predicts the probability to pay below than or equal to 0.5, it is implied that 30
3 the firm will not pay dividends. After the prediction, the actual results compare to the predicted results. The count R 2 can explain how accurate prediction both payer and non payer group. The most accuracy rate of prediction is the model with turnover ratio (the most accurate prediction 5 from 9 years), while the model without liquidity proxies cannot explain as better as the model with liquidity. Among liquidity proxies, turnover ratio is the best liquidity proxy to predict the propensity to pay dividend more accurate. In the conclusion, this study is consistent with Fama and French (2001). The results illustrate that the propensity to pay dividend increases when firms are larger, more profitable, and less growth opportunity in the Stock Exchange of Thailand. Furthermore, it is also consistent with Banerjee, Gatchev, and Spindt (2007) which investigated the market liquidity. The result is that owners of less (more) liquid common stock are more (less) likely to receive cash dividends in the Stock Exchange of Thailand. The characteristics of paying firm are used as the investment strategy not only investors, but also management of the firm. The implication for investors According to Fama French (2001), if investors prefer capital gain to dividend, investors should choose high growth opportunity and small firm size. They can use the liquidity as the indicator to receive capital gain. In this case, they should invest in the more liquid stock. The implication for management Management can use the characteristics of the firm to make decision in dividend policy. If you are the management of larger firm, you should know that investors expect dividends from your firm in order to mitigate agency cost. In term of the liquidity of the stock, management can know whether investors want dividend or capital gain (one of liquidity is turnover ratio). If their stock is high liquid, management can omit or cut dividend to invest more because of the lower propensity to pay. In the contrast, if their stock is low liquid, the expected dividends are higher than capital gain. 31
4 Table 1: Summary Statistics of Non Financial Sector: Firm Size, Growth Opportunity, Profitability, and Liquidity Proxies during Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max
5 Table 1: Summary Statistics of Non Financial Sector: Firm Size, Growth Opportunity, Profitability, and Liquidity Proxies during (Continue) 2004 Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max Non Financial sector Var. Y SET i (%) Tobin Q i (%) ROA i (%) Turn i (%) Illiquid i No trading i Bvol i (Mbaht) No.of Obs Mean SD Min Max
6 Table 2: The correlation matrix: Dependent and independent variables of nonfinancial sector sample during Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol
7 Table 2: The correlation matrix: Dependent and independent variables of nonfinancial sector sample during (Continue) 2003 Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol Non Financial Sector Y SET Tobin Q ROA Turn Illiquid No trading Bvol Y 1 SET Tobin Q ROA Turn Illiquid No trading Bvol
8 Table 3: Summary of Univariate Test for the characteristics of payer Univariate test is conducted to show the relationship between each characteristic and dividend policy whether dividend payer can be explained by investigating a single characteristics of nonfinancial sector sample. There are 4 characteristics of firm in this study which are 1. Firm size is the percent of firm i that lower market capitalization at the end of that year (SET i ). 2. Growth Opportunity is (Total Liabilities + Market Value of equity) / Total Asset of firm i at the end of year (Tobin Q i ) 3. Profitability Proxy is Earning before Interest and Tax (EBIT) divided by Total Asset of firm i at the end of year (ROA i ). 4. Liquidity Proxies are 4 measures; Turnover Ratio, Illiquid Amihud Ratio, The Number of No Trading Ratio, and Daily Average Baht Volume  Turnover Ratio is Daily Average Common Shares Traded divided by Common Shares Outstanding of firm i  Illiquid Amihud Ratio is Daily Average Absolute Return divided by Daily Average Baht Volume of firm i  The Number of No Trading Ratio is the proportion of days with no trading volume of firm i  Baht Volume is Daily Average Baht Volume of firm i 1998 NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 * X 2 X 3 * X 4 * X 5 * X 6 ** X tstat Combined NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 ** X 2 * X 3 * X 4 * X 5 * X 6 * X 7 * tstat Combined NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 * X 2 * X 3 * X 4 * X 5 * X 6 ** X tstat Combined NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 * X 2 ** X 3 * X 4 * X 5 ** X 6 * X tstat Combined NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 * X 2 X 3 * X 4 * X 5 * X 6 ** X tstat Combined * At 99% significant level ** At 95% significant level *** At 90% significant level Interpretation method of the results in Table 3: The example of Univariate test on firm size (X 1 ) in 1998 illustrates that the mean value of X 1 or firm size (SET i ) of nonpayer group is In other words, firm size of nonpayer group on average is % of SET firms with lower market capitalization of common stock (186 firms are nonpayer out of 264, where Y=0) while the mean value for payer group of 78 firms out of 264 firms (Y=1) is It can be implied that the payer group has bigger firm size than nonpayer group. The tstat for the independent test of higher +/ 2.33, +/ 1.96, and +/ imples that the variable can effectively explain each characteristic at 99%, 95%, and 90% level of confidence respectively. 36
9 Table 3: Summary of Univariate Test for the characteristics of payer (Continue) Univariate test is conducted to show the relationship between each characteristic and dividend policy whether dividend payer can be explained by investigating a single characteristics of nonfinancial sector sample. There are 4 characteristics of firm in this study which are 1. Firm size is the percent of firm i that lower market capitalization at the end of that year (SET i ). 2. Growth Opportunity is (Total Liabilities + Market Value of equity) / Total Asset of firm i at the end of year (Tobin Q i ) 3. Profitability Proxy is Earning before Interest and Tax (EBIT) divided by Total Asset of firm i at the end of year (ROA i ). 4. Liquidity Proxies are 4 measures; Turnover Ratio, Illiquid Amihud Ratio, The Number of No Trading Ratio, and Daily Average Baht Volume  Turnover Ratio is Daily Average Common Shares Traded divided by Common Shares Outstanding of firm i  Illiquid Amihud Ratio is Daily Average Absolute Return divided by Daily Average Baht Volume of firm i  The Number of No Trading Ratio is the proportion of days with no trading volume of firm i  Baht Volume is Daily Average Baht Volume of firm i 2003 NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 * X 2 X 3 * X 4 * X 5 * X 6 ** X tstat Combined NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 * X 2 ** X 3 * X 4 * X 5 ** X 6 X tstat Combined NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 * X 2 * X 3 * X 4 * X 5 * X 6 X tstat Combined NonFinancial SET i Tobin Q i ROA i Turn i Illiquid Ratio i No trading i Bvol i No. of Obs. Pay_NoPay X 1 * X 2 * X 3 * X 4 * X 5 * X 6 X tstat Combined * At 99% significant level ** At 95% significant level *** At 90% significant level Interpretation method of the results in Table 3: The example of Univariate test on firm size (X 1 ) in 2003 illustrates that the mean value of X 1 or firm size (SET i ) of nonpayer group is In other words, firm size of nonpayer group on average is % of SET firms with lower market capitalization of common stock (69 firms are nonpayer out of 233, where Y=0) while the mean value for payer group of 164 firms out of 233 firms (Y=1) is It can be implied that the payer group has bigger firm size than nonpayer group. The tstat for the independent test of higher +/ 2.33, +/ 1.96, and +/ imples that the variable can effectively explain each characteristic at 99%, 95%, and 90% level of confidence respectively. 37
10 Table 4: Logistic Regression Model to Explain Dividend Payers Table 4 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduce by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i Panel A. : Index Function where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year Non Financial Constant * (0.003) (0.818) (0.79) (0.306) (0.572) (0.591) (0.41) (0.113) (0.698) SET i * * * * * ** (0.003) (0.007) (0.000) (0.006) (0.296) (0.851) (0.493) (0.045) (0.03) Tobin Q i * * * * * *** ** ** (0.01) (0.000) (0.000) (0.000) (0.004) (0.062) (0.047) (0.343) (0.027) ROA i * * * * * * * * * (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Log likelihood Psuedo R_ LR test: Prob > chi Panel B. Marginal Effect SET i * * * * * ** (change in 1 %) (0.002) (0.005) (0.000) (0.006) (0.297) (0.851) (0.495) (0.046) (0.031) Tobin Q i * * * * * *** ** ** (change in 1 %) (0.007) (0.000) (0.000) (0.000) (0.005) (0.066) (0.05) (0.347) (0.029) ROA i * * * * * * * * * (change in 1%) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Actual Payer 29.55% 37.10% 45.08% 53.69% 63.60% 70.39% 74.66% 74.25% 71.68% Count R % 78.63% 83.61% 77.05% 78.00% 81.97% 82.43% 86.23% 82.66% 38
11 Table 5: Logistic Regression Model to Explain Dividend Payers Table 5 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduces by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R 2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i +β 4 TURN i where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year TURN i = (Common Shared Traded / Common Shares Outstanding) or Daily Average Turnover ratio of firm i Panel A. : Index Function Non Financial Constant * * (0.022) (0.611) (0.753) (0.038) (0.149) (0.878) (0.638) (0.144) (0.994) SET i * * * * *** (0.001) (0.001) (0.000) (0.008) (0.212) (0.67) (0.275) (0.079) (0.153) Tobin Q i ** * * * * (0.011) (0.000) (0.000) (0.000) (0.007) (0.481) (0.231) (0.539) (0.157) ROA i * * * * * * * * * (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Turnover i * * ** * * * * *** * (0.001) (0.006) (0.011) (0.001) (0.000) (0.001) (0.001) (0.074) (0.002) Log likelihood Psuedo R_ LR test: Prob > chi
12 Table 5: Logistic Regression Model to Explain Dividend Payers (Continue) Table 5 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduces by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R 2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i +β 4 TURN i where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year TURN i = (Common Shared Traded / Common Shares Outstanding) or Daily Average Turnover ratio of firm i Panel B. Marginal Effect Non Financial SET i * * * * *** (change in 1 %) (0.001) (0.001) (0.000) (0.008) (0.214) (0.671) (0.276) (0.08) (0.155) Tobin Q i ** * * * * (change in 1 %) (0.014) (0.000) (0.000) (0.000) (0.008) (0.484) (0.236) (0.541) (0.163) ROA i * * * * * * * * * (change in 1%) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Turnover i * * * * * * *** * (change in 1 %) (0.000) (0.000) (0.006) (0.001) (0.000) (0.002) (0.001) (0.077) (0.003) Actual Payer 29.55% 37.10% 45.08% 53.69% 63.60% 70.39% 74.66% 74.25% 71.68% Payer(+) 54.76% 58.77% 65.00% 79.48% % 83.60% 82.05% 78.15% 81.67% Payer() 4.33% 15.43% 25.17% 27.89% 26.42% 57.17% 67.28% 70.35% 61.69% Count R
13 Table 6: Logistic Regression Model to Explain Dividend Payers Table 6 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduces by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R 2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i +β 4 ILLIQ i where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year ILLIQ i = Daily Average (Absolute Return / Baht Volume) or how much Price changes with 1 Baht transacted of firm i Panel A. : Index Function Non Financial Constant (0.137) (0.141) (0.431) (0.243) (0.375) (0.656) (0.369) (0.658) (0.928) SET i ** ** (0.25) (0.719) (0.014) (0.016) (0.673) (0.825) (0.629) (0.456) (0.113) Tobin Q i ** * * * * *** *** ** (0.033) (0.001) (0.000) (0.000) (0.005) (0.063) (0.053) (0.329) (0.023) ROA i * * * * * * * (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Illiquid R. i ** * ** ** (0.047) (0.005) (0.026) (0.547) (0.213) (0.896) (0.697) (0.029) (0.287) Log likelihood Psuedo R_ LR test: Prob > chi
14 Table 6: Logistic Regression Model to Explain Dividend Payers (Continue) Table 6 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduces by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R 2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i +β 4 ILLIQ i where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year ILLIQ i = Daily Average (Absolute Return / Baht Volume) or how much Price changes with 1 Baht transacted of firm i Panel B. Marginal Effect Non Financial SET i ** (change in 1 %) (0.265) (0.015) (0.016) (0.673) (0.826) (0.627) (0.452) (0.114) Tobin Q i ** * * * * *** *** ** (change in 1 %) (0.034) (0.001) (0.000) (0.000) (0.006) (0.068) (0.055) (0.333) (0.025) ROA i * * * * * * * * (change in 1%) 0 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Illiquid R. i ** * ** ** (change in 1 time) (0.002) (0.017) (0.547) (0.216) (0.896) (0.702) (0.046) (0.29) Actual Payer 29.55% 37.10% 45.08% 53.69% 63.60% 70.39% 74.66% 74.25% 71.68% Payer(+) 47.61% 44.32% 55.09% 56.46% 72.36% 74.86% 86.54% % 82.49% Payer() 0.03% 0.17% 7.53% 48.03% 59.98% 76.10% 73.22% 60.17% 77.69% Count R
15 Table 7: Logistic Regression Model to Explain Dividend Payers Table 7 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduces by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R 2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i +β 4 NOTRD i where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year NOTRD i = The proportion of days with no trading volume of firm i Panel A. : Index Function Non Financial Constant * *** ** (0.000) (0.064) (0.041) (0.343) (0.59) (0.569) (0.936) (0.119) (0.786) SET i * * * * *** ** ** (0.000) (0.000) (0.000) (0.000) (0.054) (0.359) (0.162) (0.045) (0.048) Tobin Q i * * * * * *** ** (0.001) (0.000) (0.000) (0.000) (0.005) (0.114) (0.059) (0.38) (0.027) ROA i * * * * * * * * * (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) No Trading i * * * * ** *** *** (0.000) (0.000) (0.002) (0.000) (0.013) (0.074) (0.08) (0.707) (0.871) Log likelihood Psuedo R_ LR test: Prob > chi
16 Table 7: Logistic Regression Model to Explain Dividend Payers (Continue) Table 7 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduces by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R 2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i +β 4 NOTRD i Panel B. Marginal Effect where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year NOTRD i = The proportion of days with no trading volume of firm i Non Financial SET i * * * * *** ** ** (change in 1 %) (0.000) (0.000) (0.000) (0.000) (0.055) (0.362) (0.163) (0.046) (0.05) Tobin Q i * * * * * *** ** (change in 1 %) (0.001) (0.000) (0.000) (0.000) (0.005) (0.12) (0.062) (0.384) (0.029) ROA i * * * * * * * * * (change in 1%) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) No Trading i * * * * ** *** *** (change in 1 time) (0.000) (0.000) (0.001) (0.000) (0.012) (0.072) (0.078) (0.707) (0.871) Actual Payer 29.55% 37.10% 45.08% 53.69% 63.60% 42.07% 33.94% 34.68% 39.52% Payer(+) 43.26% 50.99% 60.16% 73.29% 74.02% 48.88% 38.92% 35.55% 39.91% Payer() 15.83% 23.21% 30.01% 34.09% 53.18% 35.27% 28.95% 33.81% 39.12% Count R
17 Table 8: Logistic Regression Model to Explain Dividend Payers Table 8 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduces by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R 2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i +β 4 BVOL i where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year BVOL i = Daily Average Baht volume of firm i Panel A. : Index Function Non Financial Constant * ** (0.000) (0.623) (0.46) (0.267) (0.873) (0.872) (0.671) (0.03) (0.734) SET i * * * ** *** * *** (0.000) (0.000) (0.000) (0.014) (0.088) (0.37) (0.172) (0.004) (0.056) Tobin Q i * * * * * *** ** (0.038) (0.000) (0.000) (0.000) (0.006) (0.108) (0.071) (0.517) (0.026) ROA i * * * * * * * * * (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Bvol i * ** * ** * (0.029) (0.012) (0.116) (0.451) (0.067) (0.122) (0.028) (0.000) (0.742) Log likelihood Psuedo R_ LR test: Prob > chi
18 Table 8: Logistic Regression Model to Explain Dividend Payers (Continue) Table 8 reports the estimated coefficient for a given period the pvalue (in parentheses). Payers is the actual percent of payers in the sample for a given period. Payers(+) is the estimated percent of payers if the examined liquidity proxy improve by one standard deviation while Payers () is the estimated percent of payers if the examined liquidity proxy reduces by one standard deviation. *, **, *** indicate significance at 1%, 5%, and 10% levels from a twotailed ttest. Count R 2 is the accuracy rate of prediction. D i =β 0 +β 1 SET i +β 2 Tobin Q i +β 3 ROA i +β 4 BVOL i Panel B. Marginal Effect where SET i = The percent of SET firms with lower market capitalization of common stock of firm i at the end of year Tobin Q i = (Total Liabilities + Market Value of Equity) / Total Assets of firm i at the end of year ROA i = Earning before Interest and Tax (EBIT) / Total Assets of firm i at the end of year BVOL i = Daily Average Baht volume of firm i Non Financial SET i * * * ** *** * *** (change in 1 %) (0.000) (0.000) (0.000) (0.088) (0.373) (0.174) (0.005) (0.059) Tobin Q i * * * * * *** ** (change in 1 %) (0.033) (0.000) (0.000) (0.000) (0.007) (0.113) (0.075) (0.52) (0.028) ROA i * * * * * * * * * (change in 1%) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) 0 (0.000) (0.000) Bvol i * ** *** ** * (change in 1 M. Baht) (0.024) (0.011) (0.114) (0.451) (0.066) (0.123) (0.027) (0.000) (0.742) Actual Payer 29.55% 37.10% 45.08% 86.26% 57.23% 42.07% 33.94% 34.68% 39.52% Payer(+) 41.04% 50.64% 55.34% 89.29% 64.64% 47.50% 39.30% 34.68% 38.22% Payer() 18.05% 23.56% 34.83% 83.23% 49.83% 36.65% 28.57% 34.68% 40.81% Count R
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