First Study Empirical Models

4.8.1.1 Empirical Model for Testing the Effect of IFRS Adoption on the Stock Price Synchronicity (H1).

The first hypothesis is concerned with examining whether mandatory IFRS adoption leads to a more informative stock price, as measured by the firm-specific return variation.

To examine the relationship between the mandatory adoption of International Financial Reporting Standards (IFRS) and stock price informativeness, this research will estimate the following pooled cross-sectional time series model:

𝑆𝑌𝑁𝐶𝐻1𝑖,𝑡= 𝛼0+ 𝛽1𝐼𝐹𝑅𝑆𝑖,𝑡+ 𝛽2𝑆𝐼𝑍𝐸𝑖,𝑡+ 𝛽3𝐺𝑅𝑂𝑃𝑃𝑖,𝑡+ 𝛽4𝐿𝐸𝑉𝑖,𝑡+ 𝛽5𝑅𝑂𝐴𝑖,𝑡+ 𝛽6𝐴𝑁𝐴𝐿𝑌𝑆𝑇𝑖.𝑡+ 𝛽7𝐼𝑁𝐷 −

𝑁𝑈𝑀𝑖,𝑡+ 𝛽8𝐼IND − SIZE𝑖,𝑡+ 𝛽9𝐻𝐸𝑅𝐹_𝐼𝑁𝐷𝑋𝑖,𝑡+ 𝛽10IND − VAR𝑖,𝑡+ 𝛽11𝐶𝑅𝐼𝑆𝑖𝑆𝑖,𝑡+ 𝐼𝑁𝐷𝑈𝑆𝑇𝑅𝑌 + 𝜀𝑖,𝑡

(7)

Where, 𝑆𝑌𝑁𝐶𝐻1 is stock price synchronicity for firm i in year t as calculated by e.q. (1). IFRS is an indicator variable that takes the value of 1 if the firm use IFRS and the value of 0 otherwise. Note that IFRS is not strictly a time-indicator variable: it varies on the firm’s mandatory adoption of IFRS, which can occur effective 2005, 2006, 2007, and 2008. Table 4.3 provides full description for the variables.

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The variable of interest in this model is the IFRS. If mandatory IFRS adoption facilitates the incorporation of firm-specific information into the stock price, then we predict that SYNCH1 is negatively related with IFRS variable. That is, improved transparency after mandatory adoption of IFRS; facilitates the incorporation of firm-specific information into the stock price, leading to more firm-specific return variation (i.e. lower stock price synchronicity), and thus increase the informativeness of the stock price. This will lead to acceptance of the first hypothesis. Using a single dummy variable to examine the effect of IFRS adoption is consistent with the methodology used by Bissessur and Hodgson (2012), Brochet et al. (2013), and Moscariello et al. (2014).

As a sensitivity test, re-examination of the above model is undertaken using a different measure of stock price synchronicity; where the stock piece synchronicity, as measured by regressing firm’s weekly return with weekly market return and weekly industry return, is used in the regression model instead of using stock price synchronicity as measured by equation number (1).

4.8.2 Empirical Model for Testing the Effect of IFRS Adoption on the Stock Price Synchronicity (H2).

The second hypothesis H2 is concerned with examining whether if there is an initial decrease in synchronicity at the time of IFRS adoption, followed by a subsequent increase in the latter periods. To test this effect Houqe et al. (2014), and Li (2010) research is followed, by excluding the transition period from the analysis. More specifically, the data for the years from 2005 to 2007 was excluded, because these are years of transition to IFRS with different adoption dates. In addition, the data for the year 2008 is excluded to avoid the effect of lack of IFRS history and knowledge on which investor can make their decisions, as suggested by Ball (2006).

In addition, the methodology of Bissessur and Hodgson (2012) and Landsman et al. (2012) is adopted, by adding year dummies on the IFRS period from 2009 until 2013. Whereas these dummy variables take the value of 1 if the observations occur in 2009,2010,2011,2012, and 2013, respectively and zero otherwise. It’s worth mentioning that, the estimated coefficient for the constant term

𝛼

₀represents the base level of stock price synchronicity for the pre IFRS adoption period, and each of the coefficients on the IFRS years’ dummies present the incremental change relative to the baseline level of synchronicity after the adoption.

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Therefore, the model to test the second hypothesis will be as follow:

𝑆𝑌𝑁𝐶𝐻1𝑖,𝑡= 𝛼0+ 𝛽1𝐷_2009 + 𝛽2𝐷_2010 +𝛽3𝐷_2011 + 𝛽4𝐷_2012 + 𝛽5𝐷_2013 + 𝛽6𝑆𝐼𝑍𝐸𝑖,𝑡+

𝛽7𝐺𝑅𝑂𝑃𝑃𝑖,𝑡+ 𝛽8𝐿𝐸𝑉𝑖,𝑡+ 𝛽9𝑅𝑂𝐴𝑖,𝑡+ 𝛽10𝐴𝑁𝐴𝐿𝑌𝑆𝑇𝑖.𝑡+ 𝛽11𝐼𝑁𝐷 − 𝑁𝑈𝑀𝑖,𝑡+ 𝛽12𝐼IND − SIZE𝑖,𝑡+

𝛽13𝐻𝐸𝑅𝐹_𝐼𝑁𝐷𝑋𝑖,𝑡+ 𝛽14IND − VAR𝑖,𝑡+ 𝛽15𝐶𝑅𝐼𝑆𝑖𝑆𝑖,𝑡+ 𝐼𝑁𝐷𝑈𝑆𝑇𝑅𝑌 + 𝜀𝑖,𝑡 (8)

The variable of interest in this model is the coefficients for the year dummies. If the coefficients for the early years of the adoption are negative and significant and the coefficients for the later years of the adoption is positive and significant, then this provide a suggestion that higher transparency that caused by mandatory IFRS adoption lead to initial decrease in stock price synchronicity (compared with the pre-IFRS adoption period), then it leads to a subsequent increase in stock price synchronicity during the later periods.

As a sensitivity test, the above model is re-examined using a different measure of stock price synchronicity; where the stock piece synchronicity as measured by regressing firms weekly return with the weekly market return and weekly industry return is used in the regression model instead of using stock price synchronicity as measured by equation number (1).

As an additional robustness test, the researcher generates a dynamic variable to capture if mandatory IFRS adoption leads to an initial decrease in synchronicity, followed by a subsequent increase in the latter period. To do so, a new variable is generated called adoption age (ADO_AGE) which represents the number of years since the firm adopt IFRS. Therefore, the robustness test for hypothesis number (2) is as follows:

𝑆𝑌𝑁𝐶𝐻1𝑖,𝑡= 𝛼0+ 𝛽1𝐴𝐷𝑂_𝐴𝐺𝐸𝑖,𝑡+ 𝛽2𝑆𝐼𝑍𝐸𝑖,𝑡+ 𝛽3𝐺𝑅𝑂𝑃𝑃𝑖,𝑡+ 𝛽4𝐿𝐸𝑉𝑖,𝑡+ 𝛽5𝑅𝑂𝐴𝑖,𝑡+ 𝛽6𝐴𝑁𝐴𝐿𝑌𝑆𝑇𝑖.𝑡+

𝛽7𝐼𝑁𝐷 − 𝑁𝑈𝑀𝑖,𝑡+ 𝛽8𝐼IND − SIZE𝑖,𝑡+ 𝛽9𝐻𝐸𝑅𝐹_𝐼𝑁𝐷𝑋𝑖,𝑡+ 𝛽10IND − VAR𝑖,𝑡+ 𝛽11𝐶𝑅𝐼𝑆𝑖𝑆𝑖,𝑡+

𝐼𝑁𝐷𝑈𝑆𝑇𝑅𝑌 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇 + 𝜀𝑖,𝑡 (9)

If higher transparency, which associated with mandatory IFRS adoption, leads to an initial reduction in stock price synchronicity followed by a subsequent increase in stock price synchronicity during the latter periods, then one could expect a positive relation between adoption age variable and stock price synchronicity. However, if the higher transparency associated with mandatory IFRS adoption leads to a consistent reduction in stock price synchronicity, then one could expect a negative relation between adoption age and stock price synchronicity.

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4.8.3 Empirical Model for Testing the Effect of Financial Analysts on the Relationship Between IFRS

Adoption and Stock Price Synchronicity (H 3)

To examine whether the effect of IFRS adoption on stock price synchronicity differs systematically between firms with high analysts’ activities and those with low activities, the researcher follows the methodology of Kim and Shi (2012a), by adding interaction term of IFRS*ANALYST to the model number (7). The interaction term explains how the effect of one predictor variable (IFRS) on the response variable (SYNCH1) is different at different values of the other predictor variable (ANALYST), (Fitzmaurice, 2000). Therefore, the resulting empirical model to test hypothesis number 3 is as follows:

𝑆𝑌𝑁𝐶𝐻1𝑖,𝑡= 𝛼0+ 𝛽1𝐼𝐹𝑅𝑆𝑖,𝑡+ 𝛽2𝑆𝐼𝑍𝐸𝑖,𝑡+ 𝛽3𝐺𝑅𝑂𝑃𝑃𝑖,𝑡+ 𝛽4𝐿𝐸𝑉𝑖,𝑡+ 𝛽5𝑅𝑂𝐴𝑖,𝑡+ 𝛽6𝐴𝑁𝐴𝐿𝑌𝑆𝑇𝑖.𝑡+ 𝛽7𝐼𝑁𝐷 −

𝑁𝑈𝑀𝑖,𝑡+ 𝛽8𝐼IND − SIZE𝑖,𝑡+ 𝛽9𝐻𝐸𝑅𝐹_𝐼𝑁𝐷𝑋𝑖,𝑡+ 𝛽10IND − VAR𝑖,𝑡+ 𝛽11𝐶𝑅𝐼𝑆𝑖𝑆𝑖,𝑡+𝛽12 𝐼𝐹𝑅𝑆 ∗ 𝐹𝑂𝐿𝐿+

𝐼𝑁𝐷𝑈𝑆𝑇𝑅𝑌 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇 + 𝜀𝑖,𝑡 (10)

The variable of interest in this model is the interaction term between IFRS and ANALYST. The significant positive coefficient for the interaction term variable, 𝐼𝐹𝑅𝑆 ∗ 𝐹𝑂𝐿𝐿, means that within the mandatory IFRS adopters, the firms that are followed by a higher number of financial analysts have higher stock price synchronicity, than those followed by lower number of financial analysts.

As a sensitivity test the above model (10) is re-examined using a different measure of stock price synchronicity; where the stock piece synchronicity as measured by regressing firms weekly return with weekly market return and weekly industry return is used in the regression model instead of using stock price synchronicity as measured by equation number (1).

In document Do IFRS adoption, financial analysts and earnings quality affect the informativeness of stock price? Evidence from the UK (Page 127-130)