Previous Studies on PIN and Interim Reports

316 changes in the cost of capital of firms after the Regulation FD was introduced in 2000. Their main finding was that NASDAQ firms were more strongly affected and costs of capital for these firms increased, suggesting that smaller firms bear more increased costs due to the new disclosure rule. In other PIN related studies, Vega 2006 analyzes both the behavior of the post announcement return drifts and changes in the PIN variable around the earnings announcement dates with U.S. data. She finds that the order arrival rate is more important than degrees of private information in explaining post-announcement drifts. In other studies, the relationship between the PIN variable and the order placement strategy is analyzed by Ellul et al. 2003 with NYSE data and the relationship between the PIN and credit rating as public information is analyzed by Odders-White and Ready 2003, again for U.S. data. For the Paris stock market, Atkas et al. 2003 investigates informational effects of corporate events like mergers and acquisitions using the PIN variable. As for the Japanese evidence, Kubota and Takehara 2009a estimate the PIN values for Tokyo Stock Exchange firms and report the comparable parameter values for Japan with U.S. firms estimated by Easley et al. 2002. These results confirm the robustness of PIN estimates across international markets with different market designs. For empirical evidence on quarterly report disclosure and the effect of the timing of the interim reports, the former goes back to the late 1960s using U.S. data. One of the earliest empirical studies using quarterly reports is Green and Segall 1967. They reach a negative conclusion that the first quarter earnings figure does not help forecast annual EPS figures, while Brown and Niederhoffer 1968 find the contrary. 41 Brown and Kennely 1972 use another Ball and Brown methodology and find that advanced knowledge of quarterly earnings can enhance abnormal returns of portfolios relative to the portfolio strategy based solely on annual earnings. This finding is further supported by Foster 1977, who finds that abnormal returns surrounding the interim report announcements are twice as large as ones obtained from annual reports. McNicholas and Manegold 1983 conduct a study using volatility estimates with a sample of 34 firms listed on the AMEX in 1961 and 1962. These firms began to publish quarterly reports according to the then new rule by the AMEX, and McNicholas and Manegold find that the variance of abnormal returns obtained from annual reports decreased upon the enactment of the new disclosure rule, and thus support the hypothesis that additional disclosure of quarterly reports helps decrease the price uncertainty surrounding announcement dates of annual earnings. On the other hand, as stated above, Mensah and Werner 2008 have shown that price volatility is higher in countries with quarterly reporting, like the U.S. and Canada, than in countries with semi-annual reporting like the U.K. and New Zealand. Also, Atiase et al. 1988 studied timeliness of financial reports and showed that it is strongly related to the size, and that a longer delay is associated 41 See Kaplan 1978 for a review of initial literature in this field which investigated the information content of interim reports. 317 with smaller price reactions, suggesting there is a large inflow of information from other channels. 42 For similar evidence in Japan on interim reports, in particular, semi-annual reports, Kubota et al. 2005 find that revisions of earnings forecasts by analysts are related to disclosure timing of the semi-annual financial statement, using data from 1980 through 2005. They find that abnormal returns generated by accruals information of the previous fiscal year‘s earning numbers get readjusted around this time period. Thus, evidence for both the U.S. and other countries seem to suggest that a pricing impact is triggered when a new interim reporting requirement is imposed on the market. In this paper we investigate these effects from the aspect of private information trades and stock liquidity.

2.4 PIN Model Used

The details of the model originally developed by Easley et al. 1996 and extended in Easley et al. 2002 are summarized in Appendix A and we outline only the estimating equation below. In their original model there are three types of market participants: market makers, informed traders, and uninformed traders. However, based on comparable parameter estimates by Kubota and Takehara 2009a on Tokyo Stock Exchange firms and by Atkas et al. 2003 on Paris Bourse firms to the NYSE firms by Easley et al. 2002, we directly apply the PIN estimation method to the electronic order driven market of the Tokyo Stock Exchange and investigate the relative weight of private information trades and public information trades. 43 In their original model there are two types of traders in the market: informed traders and uninformed traders. First, nature chooses once every day whether there is a new information event with probability  , or not, with probability 1   . The orders arrive according to the Poisson process and the uninformed traders send their orders with the buying order rate of b  and the selling order rate of s  . The order arrival rate by the informed is with rate  whenever the information event arrives, and when the news is good the buy order increases at this rate and when the news is bad it decreases at this rate. In the following equation 1 we put the symbol ―hat‖ in the equation to denote that they are estimates from the estimating equation A-2 in Appendix A. In estimating the 42 The thorough investigation of the relationship between the PIN, liquidity, and the volatility is our future work. 43 We thank Maureen OHara for discussing this point. Kubota and Takehara 2009a, p. 321 discuss why limit orders can play the role of market makers for the Tokyo Stock Exchange data. When Foucault 1999 analyzes the nature of dynamic limit order markets, he refers to the Tokyo Stock Exchange as a representative market of this kind. Moreover, Back and Baruch 2004 prove the equivalence of the floor market and the market with market makers under suitable regularity conditions. 318 parameter vector with tick data we numerically maximize this likelihood function without constraints using a standard computing procedure. 44 s b PIN       ˆ ˆ ˆ ˆ ˆ ˆ    1 This PIN variable, based on Bayes‘s theorem, represents the ex post probability that the trades are triggered by private information among all tick-by-tick trades. In 1 the numerator denotes the number of orders which is composed of the information- based order arrival rate times the occurrence of the information event, and the denominator is the total sum of the information-based trade and the sell and buy trades for the non-information event case. In estimating the necessary parameters as shown in Appendix A‘s equation A-2, we use tick-by-tick records for all the stocks and classify each transaction as either a buy or a sell order without ambiguity with the following method. 45 That is, all previous and current bid and ask quotes are recorded in our dataset, and based on these quotes, we classify all transactions as either buy or sell depending on whether each market-cleared transaction is determined either above or below the middle point of the most recent bid and ask price. We impose further conditions, in that at least 45 days of trading data are available to compute the quarterly PIN for each firm.

3. Definition of the Variables Used and the PIN Estimates

The data we use for this study is as follows. First, the sample is firms listed in the first and the second section of the Tokyo Stock Exchange from 1996 through the third quarter of 2008. To estimate the PIN variable we use tick-by-tick quote and transaction data provided by Nikkei Media Marketing Co., Ltd. For financial data, the source is again Nikkei Media Marketing Co., Ltd. Two variables we use are: lnMV, which is a natural logarithm of market value of equity in million yen, and BM, which is the book-to-market ratio of the firm in percent. These financial attributes of the firm, lnMV and BM, are computed from the Nikkei Portfolio-Master Database. As for the record of quarterly disclosure by firms listed in the Tokyo Stock Exchange, we use the ―eol‖ on-line database provided by eol, Inc. This data is originally constructed by the TD-Net of the TSE, and the data is automatically transmitted to eol, who construct their database from HTML and PDF files of quarterly financial statements from the TSE. We conduct the c ontent search using eol‘s search engine to collect necessary data. 44 We estimate the parameters by using the function min_uncon_mulvar in the IMSL CMATH Library. This function uses a quasi-Newton method to minimize the multivariate function and the details of our algorithm as explained in Dennis and Schnabel 1983. The resulting estimates of the PIN variable belong to a class of asymptotically efficient estimators Amemiya, 1985. 45 Hence, we do not have to use the conventional ―tick test‖ which is the case for markets with specialists.