Definition of the Variables Used and the PIN Estimates

319 Based on the collected records from eol, we construct a dummy variable called the ―QDDummy‖ variable and this variable is assigned value 1 if we find quarterly financial statements, and it assigned value 0 otherwise. As for the number of analysts following the firm, which we call ―NAnalysts,‖ this figure is computed based on the IBES International Summary History File provided by Thomson Reuters Markets KK. To construct the liquidity measures, we compute two alternatives: ILLIQ by Amihud 2002 and the turnover ratio. The data source is the Nikkei Portfolio-Master Database. The definition of these measures is as follows. First, let NSTD j,t denote the number of shares of firm j traded in month t, and NS j,t denote the number of shares outstanding for firm j at the end of month t . Then the ‗Turn j,t ‘, turnover ratio of firm j in month t is defined as t j t j t j NS NSTD Turn , , ,  2 This variable measures the degree of liquidity by looking at the trading volume, which is a standard measure used in microstructure studies. Next, the ―illiquidity‖ measure proposed by Amihud β00β is defined as the average ratio of the daily absolute return to the trading volume on that day. Let D j,t denote the number of days in which trading volume of firm j is strictly positive, r j,d,t denote the daily return of stock, and v j,d,t denote the trading volume in million yen. Then, ILLIQ j,t , the illiquidity measure by Amihud 2002 for firm j in month t is defined as follows.    t j D d t d j t d j t j t j r D ILLIQ , 1 , , , , , , | | 1  3 This measure is widely used in asset pricing theory tests in financial economics for example, Avramov et al. 2006 for U.S. data and Kubota and Takehara 2009b for Japanese, and we choose to use this measure. The a dditional two variables that we use as control variables in our regression analysis are the number of analysts following each firm and the number of outstanding series of corporate bonds. The first variable is directly taken from the IBES data and we count the number of earnings‘ forecasts at the end of each quarter for each firm. The second variable is from the Nikkei NEEDS Database and the counts the number of series of outstanding bonds. These are to control for the general inflow of firm-related public information. Analysts contribute to increased information about firms‘ future profitability and thus, on stock returns, and the new issuance of corporate bonds needs new credit ratings each time by credit rating agencies. Next, we report the basic PIN estimation results in Table 2, Figures 1, and Figure 2. TABLE 2 ABOUT HERE FIGURE 1 ABOUT HERE FIGURE 2 ABOUT HERE 320 In Table 2 we report average values of estimated PINs for all quarters from fiscal year 1996 through 2007. First of all, we find that general tendencies for PIN values decline over time, although not uniformly. In earlier years, we find the PIN for the fourth quarter is the largest with 22.782 and 22.278 for the first quarter. The numbers for the second and third quarter are smaller at 21.617 and 20.976, respectively. In recent years after the majority of quarterly reports became publicly available to analysts and investors, however, we find that the patterns become almost indistinguishable: i.e., 15.389, 15.356, 15.225, and 15.260 for the first, second, third, and fourth quarter, respectively, in 2007. So we conjecture that quarterly disclosure has something to do with the seasonal differences of the PIN value. The above pattern can also be easily seen from Figure 1 where each horizontal line is the overall mean of PIN values for each quarter, and the data is stacked by quarters so that one can find time-series patterns of quarterly PINs. From the figure we can clearly read the declining pattern of PIN values for each quarter. In Figure 2, the same data are plotted in the year scale graph on which all four quarter values are stacked on the same time year scale. By comparing the former year data with the recent year, one can confirm that seasonal differences of the PIN, as read as vertical representation of each quarter‘s estimates, began to decrease from fiscal year 2003 and 2004. Thus, we infer that the new quarterly disclosures had something to do with the decrease in PIN values. This is in conjunction with our hypothesis H1. We will conduct further tests in the next section to pinpoint further the firm-wise behavior of these PIN values after controlling for firm-specific variables. Table 3 reports PIN values as well as other firm characteristic variables by dichotomously splitting the sample into sets of quarterly report disclosing and non- disclosing firms. In each panel the second column reports the average values for disclosing firms and the third column reports the same for non-disclosing firms. The fourth column computes the mean difference and the fifth column reports corresponding p-values. TABLE 3 ABOUT HERE Note we report in Table 3 only the results for the second and fourth quarter, at which time quarterly earnings reports for the previous first and third quarter are formally reported. Note that the second and fourth quarter correspond to the periods when either the fiscal year-end financial statements or the semi-annual financial statements are still computed in-house and then published in the succeeding first and third quarter, respectively. The quarterly report for the second and fourth quarter which are to be reported in the third and the first quarter are thus nothing but the subset of the semi- annual and fiscal year full financial statement. This is why we do not report the results for these two quarters, as we are only interested in investigating the impact of new quarterly interim reporting, which did not exist in earlier years for Japan. In Panel A of Table 3 we find that unanimously disclosing firms have higher average PIN values than non-disclosing firms for all quarters in fiscal year 2003 to 2007. For the fourth quarter of 2007, for example, the numbers are 0.148 vs. 0.166 and the difference 321 is -0.017 with p-value 0.000. Except for three cases, the differences are significant. That is, there is a clear-cut difference in degrees of private information-based trades in their quarterly reports between disclosing and non-disclosing firms. We find more cases of insignificant results in earlier years of the sample, and after the second quarter of 2002 they become unanimously significant and p-values become zero. We believe it is a very strong result that supports the possible impact of the new quarterly reporting requirement to private information-based trades. In Panel B we report the corresponding ―illiquidity‖ measures by Amihud β00β and again the signs are uniformly negative, suggesting that disclosing firms are more liquid. In the fourth quarter of 2007 the numbers are 0.270 vs. 0.431. Again, except for four cases, the differences are significant at a 5 level. Particularly, it is notable that illiquidity differences between the two groups are strongly significant in all of the fourth quarters of four years of our sampling period. This is not necessarily the case with the former PIN case Panel A and it shows that these two variables may be related, but may contain different information. Note the fourth quarter case is from January to March in Japan when the fiscal year-end is approaching and news is flowing to the media regarding annual performance of firms, which may affect trading volume as well as stock liquidity. Panel C and Panel D report firm characteristics of disclosing and non-disclosing firms. The size matters Atiase et al., 1988, and disclosing firms are larger than non-disclosing firms, and the differences are all strongly significant with p-values zero. For the fourth quarter of 2007 the numbers are 10.695 vs. 10.248. Although disclosing firms have higher book-to-market ratios than non-disclosing firms for the fourth quarter of 2007, 123.670 vs. 115.218 the differences are not significant in four cases out of ten, and we do not particularly conclude here. Panel E reports the average number of analysts who follow firms measured in the final month of each quarter, and again, disclosing firms get more analyst attention for the fourth quarter of 2007, 3.090 people vs. 1.925 people, and most importantly, the differences are significant for all quarters. Panel F reports the number of different series of corporate bonds issued, and because we find in general that disclosing firms are larger in size than non-disclosing firms, we also find that disclosing firms have more frequent issuances with credit ratings attached anew each time.

4. Empirical Results

We report our empirical results in this section. Table 4 reports the correlation coefficients of the pertinent variable which we use for the pooled OLS regression analyses. The upper right hand off-diagonal elements report Pearson correlations and the lower left hand off-diagonal elements report Spearman rank correlations. Panel A is the result for disclosing firms and Panel B is the result for non-disclosing firms. TABLE 4 ABOUT HERE 322 Because some of the variables are not continuous variables, we interpret primarily the results from Spearman correlations. In Panel A we find that the PIN value is positively correlated with illiquidity at 0.484 and in Panel B we find that the same number is smaller for non-disclosing firms at 0.220. So the illiquidity of non-disclosing firms may contain other underlying economic factors other than private information-based trade. On the other hand, we find for disclosing firms the degree of private information-based trade has a stronger direct relationship with the illiquidity measure. Duarte et al. 2008 finds that the impact of Regulation FD on the cost of capital was different between NYSE firms and NASDAQ firms, using the PIN variable. We also use both the first and second section firm samples of the Tokyo Stock Exchange, and it is not a surprise that the smaller non-disclosing firms from the second section have more illiquidity problems, which do not result from private information-based trade per se. For example, Kubota and Takehara 2009a, Table 4 find that smaller firms have smaller information arrival rates and larger bad information arrival rates than larger firms for the first section TSE firms. Again, we will not conclude with just this correlation number between PIN and ―illiquidity‖ and we will test this relationship in a formal multivariate analysis below. In Table 4, for both disclosing and non-disclosing firms, the PIN value is negatively related to size, number of analysts, and amount of different series of bonds issued, which are quite intuitive, and positively related to book-to-market ratios, which means depressed firms have higher PIN values. We find the correlations are overall larger for disclosing firms and we suspect there is more room for unexplainable elements for non- disclosing firms. A caveat here is that the clearer-cut relationship between public information and the PIN among disclosing firms may either be an indication that quarterly reporting helps, or that there is a self-selection of good firms who keep sending signals to markets and thus the financial attributes and quality of firms may be more opaque than non-disclosing firms. We cannot distinguish between these two different scenarios judging only from this correlation table and because it suffers from a typical endogeneity problem. 46 Next, Table 5 reports the regression results, in which the dependent variable is the PIN, and regression analyses are conducted for all pooled samples and for the second and third quarters. Panel A is for all samples including all four quarters, Panel B is for the second quarter, and Panel C is for the third quarter. Note all sample cases include the time periods of both semi-annual financial statement disclosure and fiscal year-end financial statement disclosure. TABLE 5 ABOUT HERE As explained above, in the regression, QDDum is the dummy variable which takes value 1 if a firm discloses quarterly reports and 0 otherwise. The ―illiquidity‖ measure we use is the Amihud 2002 measure defined in 2. The regression coefficient of QDDum, 46 This is our ongoing research. First, we entangle the fixed effects and the random effects. Second, the firm ‘s choice to disclose or not to disclose can be incorporated, for example, by Heckman‘s two step procedure. Finally, entangling the joint effects of the liquidity and the private information trade within a simultaneous equation framework is for our future endeavor.