Earnings Response Coefficient (ERC)

A measure of investor responsiveness to earnings mainly includes studies that examine an earnings response coefficient (ERC). Extant studies explicitly state that investor responsiveness to earnings is a straight-forward proxy for earnings quality (Holthausen and Verrecchia, 1988; Liu and Thoma, 2000). Academic accounting researchers have employed a return-based earnings response coefficient as a measure of earnings quality (e.g. Beaver, 1968; and Ball and Brown, 1967; 1968). Imhoff (1992) suggests that a strong earnings response coefficient is an indication of higher-quality earnings by using judgments obtained from security analysts who were members of the Financial Analysts Federation. The results of DeFond and Park (2001) are also consistent with the interpretation of the ERC as a measure of earnings quality. They conclude higher ERCs when abnormal accruals restrain the magnitude of earnings surprises and lower ERCs when abnormal accruals exaggerated the magnitude of earnings surprises.

In accounting research, there is a basic premise that earnings with more persistency and relevant value will have stronger ERCs. Some significant results on ERCs provide insights into earnings persistence. It is noteworthy that Liu and Thomas (2000) recognize the extent to which the ERC captures decision usefulness is influenced by the degree of heterogeneity in the correlation between unexpected earnings and earnings forecast revisions within the sample: this heterogeneity results in low values of the regression R2_{. Therefore, sample}

specific characteristics, such as growth, that affect within- sample heterogeneity, are crucial. Consistent with the findings from Liu and Thomas (2000), Dechow et al. (2010) conclude that a correlation between ERCs and its availability indicates that the ERC can be viewed as a

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reasonable proxy for earnings quality only when the availability of other information is homogeneous within the sample. Dechow et al. (2010) emphasize that ERC as a proxy for earnings informativeness potentially suffer from an omitted variable bias if the variable of interest is correlated with a firm’s information environment.

ERC is defined as the estimated b from the firm-level regression of annual returns on earnings:

𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙 𝑅𝑒𝑡𝑢𝑟𝑛

= 𝛼 + 𝑏 ∗ (𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑒

) + 𝜀

(Equation 3.6)

Where α is the intercept

𝒃 = firm j’s earnings response coefficient (ERC);

Abnormal Return = Stock abnormal return as the market-adjusted return

𝑬𝒂𝒓𝒏𝒊𝒏𝒈𝒔 𝑺𝒖𝒓𝒑𝒓𝒊𝒔𝒆_𝒋,𝒕 =Firm-specific unexpected earnings, equals to firm j’s fiscal year-end reported earnings per share minus the consensus (median) analyst forecast EPS at the period of t, scaled by stock closing price at the end of period t-1; or using a time series expectation of annual earnings to obtain Earnings Surprise;

𝜺𝒋,𝒕 is a disturbance term.

All earnings per share are adjusted for stock splits and stock dividends. More informative components of earnings will have a higher b, indicating that earnings surprise has greater valuation implication. Earnings Response Coefficient (ERC) measures the weight of earnings in price movements, which is regarded as a function of ‘market-based’ earnings quality via detecting earnings surprise. (scaled by stock closing price at the end of period t-1) is measured in two ways: (a) the deviation of actual earnings from a predicated amount based on a time-series model of earnings and (b) the deviation of actual earnings from the consensus (median) analyst forecast (analyst forecast error). The median analyst forecast is computed using each analyst’s latest forecast before the earnings announcement. Collins and Kothari (1989) suppose that the ERC varies cross-sectionally with the holding period return interval and conclude that a conventional 12-month return period understates the earnings/returns association, particularly for larger firms. The association is maximized when returns are measured over 15 months. Hence, all further analysis is performed using returns measured over the 13-month, 15-month and 18-month intervals correspondently in ERC model for comparison (i.e. 1 month, 3 months and 6 months after the fiscal year end).

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Kothari and Sloan (1992) indicate that the limitation of a time-series predicted earnings is that the market’s expectation is based on a richer information set. Therefore, earnings surprise will be measured with error and the slope coefficient on earnings surprise will be biased towards zero. Because ‘stock price adjustment to some factors reflected in annual earnings may have occurred in previous years’. This is supported by previous studies (e.g., Watts and Zimmerman, 1986). According to Collins and Kothari (1989), if firm size is a proxy for information environment differences, then different size firms will exhibit different ERCs on measuring over a fixed holding period for all firms. Many previous studies suggest a relationship between firm size and several earnings attributes but with mixed results. Some predict that firm size is negatively associated with earnings quality because larger firms would make income-decreasing accounting method choices in response to greater political and regulatory scrutiny (Jensen and Meckling, 1976; and Watts and Zimmerman, 1986). Therefore, this study controls firm size (natural logarithm of total assets) which may affect earnings quality in this analysis in Equation 3.6. Earnings Response Coefficient (ERC) measures the weight of earnings in price movements. When earnings are more value relevant, stronger investor response will be expected (Ronen and Yaari, 2008). The relationship between stock return and earnings has been examined since the publication of Ball and Brown (1968). A larger ERC indicates that a dollar of earnings surprise has greater valuation implications.