MANDAKH-2017 3rd INTERNATIONAL CONFERENCE
This table shows that the correlation coefficients between BVPS, EPS and RDCPS are relatively high except between EPS and RDCPS. The highest value of the correlation coefficients is 0.5389 (between BVPS and EPS). This value is less than ―Rule of thumb‖ criterion 0.7. Contrary to our initial guess, the correlation coefficient between RDCPS and lnPATA is sufficiently low(0.1581). The facts above suggest that there is no serious multicollinearity problem in Model 3. Thus, it can be concluded that our results from Model 3 are reliable. 4.4 Analysis of Dummy Variables As mentioned in the previous section, we add two kinds of dummy variables (YearDummy and IndustryDummy) into regression model. Let us consider the case to account for the meaning that the estimated coefficient of a dummy variable is statistically significant. For example, suppose that the coefficient of year 2012 is statistically significant. This means that there are some differences between the data in 2012 and the rest of this period‘s data.14 Analysis of dummy variable gives us insight into the nature of data. Thus, this analysis may provide us with the useful suggestion for our future research. Let us now turn to the analysis of fiscal year‘s dummy variable. In general, stock price is volatile and susceptible to economic condition. 15 As Japanese economy is globalized, stock price in Japan is also influenced by global economic condition. There was the financial crisis in USA(2008). If this event had an enormous effect on Japanese stock price market, our result here may be lose plausibility. We examine this by the dummy variable of fiscal year(YearDummy). As the effect of stock market on YearDummy seems to be common in Model 1, 2, and 3, we examine t-values of YearDummy‟s coefficients in these three models. We can specify the fiscal year where its estimated coefficient is statistically significant at significance level less than 5%, namely, 2006, 2007, 2011, and 2012. This finding may represent that it is necessary to investigate economic events in these four years. Furthermore, it is possible to obtain useful information about fiscal year‘s data by changing non-dummy variable year. For example, if non-dummy year is fiscal year 2007, all the coefficients except year 2006 are statistically significant. When non-dummy year is fiscal year 2012, all the coefficients except year 2011 are statistically significant. These results show that four significant years above can be divided into two groups, namely, Group 1(2006 and 2007) and Group 2(2011 and 2012). See the table in Table 5. This table summarizes the average stock prices in Japan from year 2004 to 2013. Table 5. Average Stock Prices in Japan from year 2004 to 2013 Year Average Stock Price Difference from the next year
2004 2005 2006 2007 11,488.76 16,111.43 17,225.83 15,307.78
Year Average Stock Price
2009 2010 10,546.44 10,228.92
4,622.67
1,114.40
14
-1,918.05 -6,448.22
2008 8,859.56 1,686.88
2011 2012 2013 8,455.35 10,395.18 16,291.31
This interpretation holds for the dummy variable of industry(IndustryDummy). Stock prices may be influenced by another factors. For example, Fung(2006) examines the impact of R&D and knowledge spillovers in stock volatility. 15
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