The usefulness of the double entry constraint for predicting earnings

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The usefulness of the double entry constraint for predicting earnings. / Khansalar, Ehsan; Kashefi Pour, Eilnaz.

In: Review of Quantitative Finance and Accounting, 11.12.2018.

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@article{7fa5a9d1a279436eb211c5a6ea0e736a,
title = "The usefulness of the double entry constraint for predicting earnings",
abstract = "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.",
keywords = "double entry constraint, accruals, earnings prediction, seemingly unrelated regression",
author = "Ehsan Khansalar and {Kashefi Pour}, Eilnaz",
note = "Khansalar, E. & Kashefi-Pour, E. Rev Quant Finan Acc (2018). https://doi.org/10.1007/s11156-018-00783-3",
year = "2018",
month = dec,
day = "11",
doi = "10.1007/s11156-018-00783-3",
language = "English",
journal = "Review of Quantitative Finance and Accounting",
issn = "0924-865X",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - The usefulness of the double entry constraint for predicting earnings

AU - Khansalar, Ehsan

AU - Kashefi Pour, Eilnaz

N1 - Khansalar, E. & Kashefi-Pour, E. Rev Quant Finan Acc (2018). https://doi.org/10.1007/s11156-018-00783-3

PY - 2018/12/11

Y1 - 2018/12/11

N2 - 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.

AB - 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.

KW - double entry constraint

KW - accruals

KW - earnings prediction

KW - seemingly unrelated regression

U2 - 10.1007/s11156-018-00783-3

DO - 10.1007/s11156-018-00783-3

M3 - Article

JO - Review of Quantitative Finance and Accounting

JF - Review of Quantitative Finance and Accounting

SN - 0924-865X

ER -