In the absence of an income statement, earnings can be calculated as cash flow from operating activities (CFO) plus accruals, rather than being stated as the difference between income statement revenues and expenses. Following the study by Christodoulou and McLeay (Contemp Account Res 31:609:328. https://doi.org/10.1111/1911-3846.12038, 2014), this paper uses a system of structural regressions with a framework of two simultaneous linear models, allowing the most basic property of accounting—double entry bookkeeping—to be incorporated as a constraint. The paper aims to investigate whether the constrained seemingly unrelated regression (SUR) estimator with two simultaneous models, produces lower out-of-sample prediction errors than each standalone model. We also examine if CFO and accruals are more capable of predicting future earnings than income statement earnings and expenses. Our findings show that in predicting earnings: (1) a system of structural regressions with two constrained simultaneous models produces significantly smaller out-of-sample prediction errors than each separate regression; and (2) accruals and CFO produce smaller out-of-sample prediction errors than earnings and expenses.
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Financial Accounting Standard Board (FASB), International Accounting Standard Board (IASB).
Following Fairfield et al. (2003), we do not use invested assets as a deflator. Their findings suggest that the lower persistence of accruals, compared to cash flows, is due to accruals being highly correlated with the growth of invested assets, as used in prior research as the denominator to scale future earnings.
As our matrix has 18 variables, for brevity, we only report the outcomes. Correlation coefficients between REC and PAY as well as SAL and CGS were particularly high, which could signal potential multicollinearity problems. To test for multicollinearity, we ran a diagnostic test: variance inflation factor (VIF). Chatterjee and Hadi (2015) and Baum et al. (2003) recommend a maximum VIF of 10, above which the estimates are too sensitive to even small changes in the data (i.e., unstable). We found the highest VIFs to be 3.21 for SAL and 3.01 for CGS, which are well below the aforementioned maximum recommended VIF. Binkley (1982) notes that some degree of multicollinearity is unavoidable, especially in accounting models that rely on such highly structured information.
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Khansalar, E., Kashefi-Pour, E. The usefulness of the double entry constraint for predicting earnings. Rev Quant Finan Acc 54, 51–67 (2020). https://doi.org/10.1007/s11156-018-00783-3
- Double entry constraint
- Earnings prediction
- Seemingly unrelated regression