Full sample

This study begins by analysing the complete sample provided in Table 5.19. This sample covers all industries except financial firms. Stock returns (SR) are computed over all months from 1/2004 to 12/2009 using the return index for each half-year interim reporting period49. Furthermore, SIZE is the natural logarithm of the year-end market capitalisation (Worldscope#08001), is determined by multiplying closing price by number of shares. Book-to-market (BM) is the ratio of common equity to market capitalisation (Worldscope#09704). Sales growth (SG) is measured as reported interim period change in sales deflated by total assets at the end of the current period (Worldscope#01001). The current accrual (CA) is the six-monthly change in net current operating assets, i.e. current assets (Worldscope#02201), excluding cash (Worldscope#02003), minus current liabilities (Worldscope#03101), excluding the current portion of long-term debt (Worldscope#03051) deflated by Total assets at the end of the current period (Worldscope#02999)50. The discretionary current accrual (DACC) is the residual from the cross-sectional regression of

49

SR may also be computed directly from the Return index of Datastream, but with less accuracy

50

Even though CA is referred to Current Accrual, it is in fact effectively a net amount comprising revenue accruals, expense accruals, revenue deferrals and expense deferrals.

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ACC on a constant scaled by the Total assets six months earlier51. The earnings surprise variable is the standard unexpected earnings (SUE) calculated as the six-monthly change in earnings scaled by the standard deviation of the firm’s six-monthly earnings series, where earnings are before extraordinary items (Worldscope#05202).

Table 5.19 presents the descriptive statistics for the variables used in the models for the UK firms. The average (median) value of accruals is -0.002 with a standard deviation of 0.085, which is similar to the findings in Xie (2001). Inter quartiles for accrual varies from -0.034 to 0.033. The average (mean) and standard deviation for stock returns are -0.003 and 0.386, similar to figures quoted in Dechow (1994). She presents mean and standard deviation of stock returns of -0.007 and 0.160. As can be seen in Table 5.19, SIZE has an average of 17.908 and a standard deviation of 2.167. The average for SUE is 0.039 (SD 0.915; IQR: -0.545 to 0.654) and BM has an average of 0.708 (SD 0.796; IQR: 0.279 to 0.925). Sales growth (SG) has an average of 0.026 (SD 0.128; IQR: -0.019 to 0.071) is demonstrated in table 5.19

145 Table 5.19 Descriptive statistics Mean Std. Dev. 25th Percentiles Median 75th

Percentiles Skewness Kurtosis

Stock return SR -0.003 0.386 -0.253 -0.019 0.192 0.851 5.081

Stock return(Lag) SRt-1 -0.004 0.373 -0.238 -0.019 0.179 1.028 6.019

Firm size SIZE 17.908 2.167 16.300 17.721 19.450 0.242 2.407

Book to market value BM 0.708 0.796 0.279 0.516 0.925 1.688 9.549

Sales growth SG 0.026 0.128 -0.019 0.012 0.071 0.424 7.098

Current accruals ACC -0.002 0.085 -0.034 -0.001 0.033 -0.309 8.299

Discretionary current accruals DACC -0.013 0.117 -0.061 -0.016 0.036 0.094 6.494

Discretionary current accruals with ROA DACC_ROA 0.001 0.081 -0.032 0.001 0.034 -0.070 7.521

Standard unexpected earnings SUE 0.039 0.915 -0.545 0.041 0.654 -0.091 2.753

146 5.5.2 Correlation matrix

Summary statistics and correlation coefficients are computed for all variables. The variables are reported as follows. The stock return from months one to six is calculated from the return index. The natural logarithm of the market value of equity (year-end market capital # WS # 8001) at the end of the interim period, and the ratio of the book-to-market (BM) from the end of interim period are calculated by dividing common equity (WS#3501) by year-end market capitalisation. This study follows Fama and French (1993) when computing the size and book-to-market values.

The past six months’ return and the interim sales are calculated by taking the differences between past sales and current sales and then dividing them by the lag of total assets, therefore growth in sales is covered from months one to six. In addition, unexpected earnings are considered for six months; as mentioned in this chapter, unexpected earnings are calculated by taking differences of income before extraordinary items and dividing by year-end market capital.

Table 5.20 reports the Pearson and Spearman correlations and their significance levels (in italics) between the selected variables for the set of UK companies. A preliminary indication of the association between discretionary accruals and the earnings and stock returns for firms can be obtained by looking at the simple (Pearson) correlations between variables presented Table 5.20. As mentioned earlier, managers use discretionary accruals to drive stock returns via accounting earnings. The stock return is correlated with standard unexpected earnings as the variable used to measure earnings surprises; a positive correlation between SUE and stock returns is expected. Table 5.20 reveals that the SUE

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the 0.01 level (0.092, p-value <0.001). This is consistent with the finding of Jegadeesh and Livant (2006). There is a positive correlation between sales growth and stock returns which is expected according to the literature reviews. Similarly, the lag of Stock returns has a negative correlation with discretionary accruals (0.061, p-value <0.001) and there is positive correlation between DACC and SUE (0.248, p-value <0.001). Also the correlation between DACC_ROA and SUE is significant (0.082, p-value <0.001). In this study, discretionary accruals are divided into dummy variables52. According to this table, the correlation coefficient demonstrates that discretionary accruals have a negative relationship with the lag of stock returns; the coefficient is -0.061 and it is significant. However, at the same time the discretionary accruals have a negative relationship with stock return. In the present study, the standard unexpected earrings are divided into two dummy variables which are standard unexpected earnings high and low. The former is expected to have positive earnings surprises and be significantly correlated with stock returns. Regardless, the positive correlation between stock returns and standard unexpected earnings demonstrates that future stock return can be explained by positive earnings surprises. SIZE

has a positive correlation with SR (0.147, p-value <0.001) while it has a positive correlation with SUE (0.033, p-value 0.013). Correlations between SIZE and SR are also relatively high. This high correlation between accounting-based control variables is consistent with prior studies on earnings management (see Li, 2011).

As a result, it is unlikely that multicollinearity will be a problem for our estimated regressions. In addition, the magnitude of the Spearmen correlation coefficients is

52

DCA_H is defined as high discretionary accruals and shows the positive accruals. DAC_L presents low discretionary accruals and it is negative.

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sometimes different when compared with the results reported for the Pearson correlation coefficients. This hints at the possibility of extreme observations influencing the correlations and, also, any subsequent analyses using untreated data.

149 Table 5. 20

Pearson and Spearman correlation coefficients between variables.

SR LSR Size BM SG LSG ACC DACC DACC_ROA SUE

SR 0.117 0.185 -0.129 0.015 0.034 -0.006 -0.023 -0.040 0.093 0.000 0.000 0.000 0.285 0.017 0.669 0.104 0.005 0.000 LSR 0.063 0.251 -0.232 0.142 0.023 -0.007 0.085 -0.014 0.091 0.000 0.000 0.000 0.000 0.113 0.601 0.000 0.323 0.000 SIZE 0.147 0.180 -0.333 0.128 0.110 0.034 0.050 0.007 0.028 0.000 0.000 0.000 0.000 0.000 0.019 0.000 0.626 0.053 BM -0.107 -0.176 -0.270 -0.136 -0.133 -0.014 -0.054 -0.012 -0.077 0.000 0.000 0.000 0.000 0.000 0.339 0.000 0.386 0.000 SG 0.009 0.091 0.073 -0.093 -0.178 0.023 0.611 0.165 0.329 0.507 0.000 0.000 0.000 0.000 0.100 0.000 0.000 0.000 LSG 0.025 0.004 0.043 -0.057 -0.167 0.063 -0.154 -0.002 -0.168 0.078 0.775 0.003 0.000 0.000 0.000 0.000 0.864 0.000 ACC 0.007 -0.001 0.028 0.011 0.001 0.024 0.460 0.945 0.040 0.592 0.948 0.035 0.400 0.929 0.093 0.000 0.000 0.005 DACC -0.023 0.061 0.025 -0.043 0.668 -0.138 0.508 0.630 0.248 0.091 0.000 0.065 0.001 0.000 0.000 0.000 0.000 0.000 DACC_ROA -0.018 -0.004 -0.002 -0.004 0.152 -0.023 0.960 0.669 0.082 0.176 0.793 0.909 0.750 0.000 0.107 0.000 0.000 0.000 SUE 0.092 0.075 0.033 -0.077 0.283 -0.079 0.049 0.216 0.080 0.000 0.000 0.013 0.000 0.000 0.000 0.000 0.000 0.000

Pearson correlation (below diagonal) and Spearman correlation (above diagonal) are reported. The sample consists of 5,616 firm- period observations. In addition, P-value of each variable is reported regarding the coefficient to show the level of significance. Stock Return (SR) is computed over all months from 1/2004 to 12/2009 using Datastream closing prices (Datastream#UP#S) and dividend (Datastream #DI), In addition, it is defined as the difference between the closing price (plus dividends) at the end of each half year interim reporting period and the natural logarithm of the price at the beginning of the interim reporting period (SR may also be computed directly from the DataStream Total Return Index, but with less accuracy). Note; their significance levels is shown in italics. The upper right triangle data contains Spearman coefficients and the lower of triangle contains

Pearson coefficient. Two reported correlation coefficients, linear (eg, Pearson) and rank (eg, Spearman), that are commonly used to measure linear and general relationships between two variables. This thesis focuses on Pearson (linear correlation).

150 5.5.3 Winners and losers

In the literature review, it was noted that managers are extremely interested in maintaining growth in earnings because their compensation is often tied to firm profits. The fact that a firm has falling earnings expectations can immediately affect its stock price. On the other hand, firms that beat expectations are rewarded by investors (Chan et al., 2006). Research suggests that the market fixates on firms’ bottom line income to the exclusion of other indicators of operating performance. With regard to these hypotheses it will be important to follow up winner and loser firms.

Winner firms are defined as firms for which the short term stock return is high, and they are in the top quintiles. Loser firms are defined as firms stabilised in the bottom quintiles of returns. As mentioned in the data definition, return is based on share price and dividend amount during the specified period. Much research focuses on stocks and the impact of accounting performance by examining winner and loser firms, as documented in the literature review. Sloan (1996) finds a return anomaly associated with discretionary accruals. He shows that stocks with large positive accruals in a given year tend to have low returns in the next year, and then these stocks have an average size-adjusted return in the following year. This finding is confirmed by Collins and Hribar (2000) with quarterly accruals. These results demonstrate that large positive accruals are a sign of managed earnings. It is not expected that investors realize this; therefore they believe that firms will retain their profitability in the future.

151 5.7 Summary

This chapter explains the data collection process and discusses the research methodology of the study. This chapter also describes the criteria used to select the full sample and the characteristics of the sample. Interim data problems in the Thomson One Banker data base and particularly Worldscope and Datastream are described. In addition, this chapter discusses reported accounting data for interim periods and for fiscal year-end in Worldscope. Finally, this chapter gives a brief explanation of the variables employed in the study.

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Chapter 6