NDAit = β0 [1/Ait-1] + β1 [ΔREVit/Ait-1] + β2 [PPEit/Ait-1] (4) DAit = TAit – NDAit (5) Where: NDAit = non-discretionary accruals in year t for firm i; DAit = discretionary accruals in year t for firm i; Modified Jones Model The original Jones model has provided a relevant understanding regarding the detection and the measurement of the use of earnings management, many researchers using it in other studies about the use of earnings management. However, over time, it became subject to criticism. Consequently, other researchers have performed efforts to upgrade the original model and make it more effective and accurate. To the original Jones model two types of adjustments have been performed: data adjustment and variable adjustment. The first type, i.e. data adjustment, addresses the data to be used in the model for estimating accruals. The most significant data adjustment was performed by DeFond and Jiambalvo (1994), who, to improve the original model that used time-series data, introduced the usage of cross-sectional data. The variable adjustment type of modification addresses the variables used in the model. The modified Jones models attempt to improve the original model by adding extra variables, or by substituting the original variables with items that have the potential to be more relevant. Dechow et Al. (1995) introduced the modified Jones model, by adjusting the change in revenues with the change in the account receivables. The purpose of this modification was to eliminate the tendency of the original model to erroneously measure the discretionary accruals when discretion was applied by managers over revenues. NDAt = a1 (1/At-1) + a2 (∆REVt - ∆RECt) + a3 PPEt (6) Where: ∆REVt = change in revenues, i.e. revenues in year t less revenues in year t-1, scaled by total assets at t-1; ∆RECt = change in net receivables, i.e. net receivables in year t less net receivables in year t-1, scaled by total assets at t-1;
PPEt = gross property, plant and equipment in year t, scaled by total assets at t-1; At-1 = total assets at t-1; a1, a2, a3 = firm-specific parameters; This modified version of the Jones (1991) model assumes that the changes in the sales result from the use of earnings management. The Industry Model The Industry Model was used by Dechow and Sloan (1991), and in addition was considered by Dechow et Al. (1995). This model, just like the original Jones model, relaxes the premise that the non-discretionary accruals would be constant over time. In addition, it assumes that variations in the factors that affect the non-discretionary accruals are standard for firms in the same industry. Consequently, the Industry Model is: NDAt = γ1 + γ2 median(TAt) (7) Where: median(TAt) = the median value of total accruals scaled by lagged assets for all non-sample firms in the same 2-digit SIC code; γ1, γ2 = specific parameters estimated using OLS on the observations in the estimation period; Regarding the Industry Model, as Dechow et Al. (1995) presented one problem exists: “If changes in non-discretionary accruals largely reflect responses to changes in firm-specific circumstances, then the Industry Model will not extract all non-discretionary accruals from the discretionary accrual proxy” . The Healy Model Introduced by Healy (1985), this model’s approach for detecting and measuring earnings management is by comparing mean total accruals scaled by lagged total assets across the earnings management partitioning variable. The Healy Model is: NDAτ = ∑ TAt / T (8) Where: NDA = estimated nondiscretionary accruals;
Appendices Appendix 1: Summary table of the literature review Outcomes No conclusive evidence regarding the correlation between the economic dependence and the probability of issuing a going-concern audit report both periods. Significant evidence showing a positive effect those auditors’ incentives to maintain reputation have on the probability of issuing going-concern opinions in the rotation period. When using the first measure, i.e. the propensity of issuing a going concern opinion, the findings indicate a positive correlation between audit tenure and audit quality. When using the second measure, i.e. discretionary accruals, evidence show no association between audit tenure and audit quality. Methodology Regression analysis that explores the propensity of the going concern opinions in two periods: mandatory rotation period and rotation period. Audit quality is measured using two proxies: the propensity of a going concern opinion being issued by the auditor, and discretionary accruals. Regarding the first measure, a model that includes the client’s financial risk, liabilities to assets ratio, net cash flow and other ratios as variables, is used. Concerning the discretionary accruals, they are estimated using the performance-matched modified Jones model. Data sources Database of the Spanish Securities and Exchange Commission (Comisio´n Nacional del Mercado de Valores, CNMV). Craswell’s “Who audits Australia” database, ASX website, Aspect Huntley’s DatAnalysis, Connect 4, Thompson’s SDC Platinum New Issues Database, CRIF. Missing data manually collected from other relevant sources. Sample 1326 financial distressed firms from Spain from the period 1991-2000, with a total of 3.119 firm year observations. Companies from the financial services and insurance industry, as well as already in liquidation companies, were excluded from the sample. 205 auditor switches in Australian public listed ASX companies from the period 1995-2003, with a total of 1.750 firm year observations. Companies from the financial sector, companies reporting in foreign currencies, and companies with incomplete data were excluded from the sample. Subject The effect mandatory audit firm rotation has on the independence of the auditor. The effect of the tenure in auditor-client relationship on the quality of audit.
Authors Cameran, Prencipe & Trombetta (2012) Becker, DeFond, Jiambalvo & Subramanyam (1998) Davis, Soo & Trompeter (2000) Outcomes Negative association between the auditor tenure and the dispersion of the current and the discretionary accruals. Negative correlation between the audit partner tenure and the level of the discretionary accruals. Negative correlation between the audit firm tenure and the level of the discretionary accruals, after controlling for partner tenure. Methodology Two proxies are used for measuring the earnings quality, current accruals and discretionary accruals. Both univariate and multivariate tests are performed for testing the hypotheses. Discretionary accruals are measured using a version of the modified Jones model. Two hypotheses are tested, one regarding the effect of partner tenure, and one regarding the effect of audit firm tenure, after controlling for the partner tenure. Models are designed for testing these hypotheses, including the age of the company, its size and growth, as variables. Data sources Compustat database. Taiwan Economic Journal (TEJ) database Sample 42.302 firm year observations from the US from the period 888 Taiwanese companies from the period with a total of 5.213 firm year observations. Items that have an initial public offering in the current or previous year are excluded from the sample. Subject The effect of the relationship in terms of tenure on the quality of earnings. The effect of audit firm tenure on the quality of the published earnings.
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 ,434a ,188 ,179 ,1147137729125 a. Predictors: (Constant), ROAt-1/At-1, (REVt-RECt)/At-1, PPEt/At-1, 1/At-1 Table 5 – BETAs Model Summary ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1,104 4 ,276 20,969 ,000a Residual 4,764 362 ,013 Total 5,867 366 a. Predictors: (Constant), ROAt-1/At-1, (REVt-RECt)/At-1, PPEt/At-1, 1/At-1 b. Dependent Variable: TAt/At-1 Table 6 – BETAs ANOVA Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -,062 ,013 -4,699 ,000 1/At-1 33,842 27,338 ,081 1,238 ,217 (REVt-RECt)/At-1 ,161 ,021 ,399 7,538 ,000 PPEt/At-1 ,017 ,015 ,062 1,137 ,256 ROAt-1/At-1 -714,490 408,728 -,112 -1,748 ,081 a. Dependent Variable: TAt/At-1 Table 7 – BETAs Coefficients Homoscedasticity test:
Coefficientsa Model Collinearity Statistics Tolerance VIF 1 Audit tenure ,976 1,025 ctr_size ,985 1,015 ctr_growth ,968 1,033 ctr_leverage ,974 1,027 a. Dependent Variable: ctr_PriorLoss Coefficientsa Model Collinearity Statistics Tolerance VIF 1 ctr_PriorLoss ,978 1,022 Audit tenure ,967 1,034 ctr_size ,997 1,003 ctr_growth ,968 1,033 a. Dependent Variable: ctr_leverage