Second Study Empirical Models

4.8.2.1 Empirical Model for Testing the Effect of Earning Quality on Stock Price Synchronicity (H4)

The fourth hypothesis is concerned with the impact of earning quality, as measured by accruals quality, on the ability of stock price to incorporate firm-specific information, as measured by stock price synchronicity. To test H4 the author estimated the following pooled cross-sectional time series regression model:

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𝑆𝑌𝑁𝐶𝐻1𝑖,𝑡= 𝛼0+ 𝛽1𝑀𝐽_𝑀𝑂𝐷𝐸𝐿𝑖,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 + 𝑌𝐸𝐴𝑅 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇 (11)

In this model the dependent variable (SEYNCH1), refers to stock price synchronicity, which represents the part of stock return that can be explained by market return and industry return. The high value of stock price synchronicity indicates that the stock price tends to commove with the market return and the industry return, meaning lower firm-specific information is reflected into the stock price, thus a less informative stock price.

The variable of interest is the coefficient of the MJ_Model variable, which captures the incremental change in stock price synchronicity for UK firms, referring to one-unit increase in discretionary accruals. A positive coefficient on 𝛽₁ is consistent with the encouragement effect of earnings quality on stock price synchronicity, that the higher earnings quality reduce the information cost, which encourage investors to collect and process more firm-specific information, leading to more capitalisation of firm-specific information into the stock price, thus creating a more informative stock price, and this results in lower stock price synchronicity. As a sensitivity test the above model (11) was re-examined using a different measure of stock price synchronicity; where the stock price 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 addition, the researcher re-examines the above model (model number 11) using a different measure of earnings quality; where earnings quality as estimated by the Jones (1991) model is used in the regression model instead of the Modified Jones model (1995).

4.8.2.2 Empirical Model for Testing the Effect of IFRS Adoption on the Relationship between Earning Quality and Stock Price Informativeness (H5+H6)

The fifth and sixth hypotheses are concerned with the effect of mandatory adoption of IFRS in the relationship between earning quality and stock price synchronicity. To test these hypotheses, the model (11) was run for post-IFRS adoption sample and for pre-IFRS adoption sample separately, to see if the coefficient of earnings quality variable differs between the two samples.

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After that, the author examined if the differences between earning quality coefficient for post- IFRS sample and pre-IFRS sample were significant or not.

To do this analysis, a dummy variable was created, called IFRS that coded 1 for post-IFRS sample and 0 for pre-IFRS sample and, and generate a new variable called IFRS_MJM that is the product of the interaction between IFRS and MJ_model variables. The author then used IFRS and IFRS_MJM variables as predictors in the regression equation. Therefore, the following regression model is used to test the sixth and seventh hypotheses:

𝑆𝑌𝑁𝐶𝐻1_𝑖,𝑡 = 𝛼₀+ 𝛽₁𝑀𝐽_𝑀𝑂𝐷𝐸𝐿_𝑖,𝑡+ 𝛽₂𝑆𝐼𝑍𝐸_𝑖,𝑡+ 𝛽₃𝐺𝑅𝑂𝑃𝑃_𝑖,𝑡+ 𝛽₄𝐿𝐸𝑉_𝑖,𝑡+ 𝛽₅𝑅𝑂𝐴_𝑖,𝑡+

𝛽6𝐴𝑁𝐴𝐿𝑌𝑆𝑇𝑖.𝑡+ 𝛽7𝐼𝑁𝐷 − 𝑁𝑈𝑀𝑖,𝑡+ 𝛽8𝐼𝐼𝑁𝐷 − 𝑆𝐼𝑍𝐸𝑖,𝑡+ 𝛽9𝐻𝐸𝑅𝐹_𝐼𝑁𝐷𝑋𝑖,𝑡+ 𝛽10𝐼𝑁𝐷 − 𝑉𝐴𝑅𝑖,𝑡+

𝛽11𝐶𝑅𝐼𝑆𝑖𝑆𝑖,𝑡+ 𝛽12𝐼𝐹𝑅𝑆 + 𝛽13𝐼𝐹𝑅𝑆 ∗ 𝑀𝐽_{𝑀𝑂𝐷𝐸𝐿 𝑖,𝑡}+ 𝐼𝑁𝐷𝑈𝑆𝑇𝑅𝑌 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇 +

𝑌𝐸𝐴𝑅 𝐹𝐼𝑋𝐸𝐷 𝐸𝐹𝐹𝐸𝐶𝑇 + 𝜀𝑖,𝑡 (12)

The variable of interest is the coefficient on the interaction term variable (𝐼𝐹𝑅𝑆 ∗ 𝑀𝐽 − 𝑀𝑂𝐷𝐸𝐿) variable, which tests if the coefficient for earnings quality variable for post IFRS sample is significantly different from that for pre IFRS sample captures. The significant positive coefficient for the interaction term variable ( 𝐼𝐹𝑅𝑆 ∗ 𝑀𝐽 − 𝑀𝑂𝐷𝐸𝐿) suggests that, the mandatory IFRS adoption significantly improves the relationship between earnings quality and stock price synchronicity (the ability of earnings quality to predict stock price synchronicity).

As a sensitivity test the above model was re-examined (model number 12) 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 synchronicity as measured by equation number (1).

In addition, the above model (model number 12) is re-examined using a different measure of earnings quality; where earnings quality as estimated by the Jones (1991) model is used in the regression model instead of the Modified Jones model (1995).

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Table 3-3 Summary of Research Variable and Their Measurement. Variable Name Variable Description

Panel A dependent variable Stock price Synchronicity1 (SYNCH1)

stock price synchronicity as calculated by the following model 𝑅𝐸𝑇𝑖,𝑤= 𝛼 + 𝛽1𝑀𝐾𝑅𝐸𝑇𝑊1+

𝛽2𝑀𝐾𝑅𝐸𝑇𝑊−1+ 𝛽3𝐼𝑁𝐷𝑅𝐸𝑇𝑖,𝑤1+ 𝛽4𝐼𝑁𝐷𝑅𝐸𝑇𝑖,𝑤−1+ 𝜀𝑖, 𝑤

Stock price Synchronicity2 (SYNCH2)

stock price synchronicity as calculated by the following model 𝑅𝐸𝑇𝑖,𝑤= 𝛼 + 𝛽1𝑀𝐾𝑅𝐸𝑇𝑊1+

𝛽3𝐼𝑁𝐷𝑅𝐸𝑇𝑖,𝑤1+ 𝜀𝑖, 𝑤

Panel B independent variables Modefied Jones model

(MJ_Model)

Absolute value of discretionary accruals as estimated by using the Modified Jones (1995) model

Jones Model (J_Model) Absolute value of discretionary accruals as estimated by using the Jones (1991) model Panel C control variables

Firm size (SIZE) Firm’s total asset at the end of fiscal year.

Growth opportunity (M/B) The ratio of market value of equity to the book value of equity.

Return on asset (ROA Firm return on asset as calculated by dividing net income by total assets. Financial leverage (LEV) The firm’s total debt divided by the firm’s total assets.

Financial analysts-following (FOLL)

Natural log of one plus number of analysts providing one year earnings per share (EPS) forecast for a firm.

Industry concentration (HERF_INDX)

Revenue-based Herfindahl index of industry-level concentration.

Industry size (IND_SIZE) Log of year-end total assets of all sample firms in the industry to which a firm belong. And the number of firms in each industry.

Industry number (IND_NUMB)

A total number of firms in the industry to which a firm belong.

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(VAR_IND_RET) The financial crisis (CRISES)

Dummy variable, take the value of one for The Financial Crisis period for the years 2008,2009,2010,2011,2012 and zero otherwise

Adoption Age (ADO_AGE) The number of years since the firm adopts IFRS.

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Chapter Five: First Study Empirical results (Accounting transparency and

stock price informativeness.)

This chapter presents the empirical-analytical tests that were performed to examine the effect of accounting transparency, as measured by the mandatory adoption of IFRS, on stock price informativeness, as inversely measured by stock price synchronicity. The empirical analysis contains several types of tests including descriptive statistics for variables of interest, correlation analysis, bivariate regression, and multivariate regression. In addition, this study conducts some additional robustness tests to chick the validity of results, after reasonable changes in methodology.