Using Artificial Neural Networks To Examine Semiotic Theories Of Accounting Accruals

Harlan L Etheridge,Kathy H Y Hsu

doi:10.19030/jber.v2i12.2951

Abstract

Although the primary purpose of accounting is to communicate information, few studies have investigated the communicative nature of accounting.&nbsp; This study uses semiotics, a theory of signs and signals, to examine the purpose and usefulness of accounting accruals with a popular tool in forecasting--artificial neural networks.&nbsp; Two primary theories are proposed by this study.&nbsp; The Theory of the Functions of Accounting Accruals categories accounting accruals by their basic functions and states that two general types of accounting accruals exist: syntactic accruals and semantic accruals.&nbsp; Syntactic accounting accruals reflect incomplete transactions under a system of cash receipts and cash disbursements.&nbsp; Semantic accounting accruals present messages in a different format than their counterparts in a system of cash receipts and cash disbursements.&nbsp; The Theory of the Pragmatic Information of Accounting Accruals states that accounting accruals contain pragmatic information (value) because of their functions.&nbsp; The pragmatic information (value) of accounting accruals is examined by comparing the ability of accrual accounting data to forecast future cash flows compared to that of cash-flow accounting data.&nbsp; Forecasts are made using backpropagation neural networks.&nbsp; The results indicate that both syntactic&nbsp; and semantic accounting accruals contain pragmatic information (have value when forecasting future cash flows).&nbsp; The study provides evidence of the pragmatic information (value) of annual accounting accruals.&nbsp; However, no evidence was found in support of the pragmatic information (value) of quarterly accounting accruals.&nbsp; This result implies that annual accrual accounting data is a better predictor of future cash flows than cash-flow data while quarterly accrual accounting data is not.

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