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A temática da manipulação de resultados e a qualidade da informação têm vindo a assumir um papel fulcral na literatura de contabilidade e finanças. As estratégias utilizadas com vista a empolarem, ou atenuarem, resultados contabilísticos são frequentemente designadas como práticas de manipulação de resultados. A literatura tem procurado, ao longo dos tempos, desenvolver modelos para identificar estas práticas. O presente trabalho teve como foco principal avaliar a capacidade de detecção, em situações reais, de um dos primeiros e mais importante destes modelos: o modelo de Jones (1991).
Jones (1991) propõe um modelo baseado na utilização de procedimento econométrico para estimar os accruals discricionários que, ao contrário da literatura anterior, considera não serem constantes ao longo do tempo. Para tal, Jones (1991) estima uma equação que explica os accruals totais como base em duas variáveis explicativas dos accruals não discricionários: a variação das vendas e os ativos fixos tangíveis. O modelo foi aplicado a uma amostra composta por 150.425 observações empresa-ano obtidas na base de dados Computstat para o período compreendido entre 1985 a 2015.
Através do desenvolvimento de três indicadores foi possível medir e avaliar a capacidade do modelo em detetar manipulação de resultados via accruals discricionários (valores estimados). A aplicação destes indicadores à amostra tem como objetivo efetuar uma correspondência das observações tidas como manipuladas pelo SEC, via Audit Analytics.
Tanto o indicador 1 como o 2 foram capazes de detetar 15% dos casos de manipulação de resultados da amostra do SEC, num total de 11.882 observações empresa-ano. Já o indicador 3 sobressai dos restantes por apresentar 16% de capacidade de identificação dos casos de manipulação.
The issue of earnings management and the quality of information have come to play a central role in accounting and finance literature. Strategies used to dampen, or mitigate, accounting results are often referred to as earnings management practices. Literature has sought, over time, to develop models to identify these practices. The main objective of this study was to evaluate the ability to detect, in real life situations, one of the first and most important of these models: the Jones model (1991). Jones (1991) proposes a model based on the use of econometric procedure to estimate discretionary accruals that, contrary to previous literature, considers not to be constant over time. For this, Jones (1991) estimates an equation that explains total accruals as the basis for two explanatory variables of non-discretionary accruals: sales variation and fixed assets. The model was applied to a sample composed of 150,425 company-year observations obtained in the Computstat database for the period 1985 to 2015. Through the development of three indicators it was possible to measure and evaluate the model's ability to detect manipulation of results via discretionary accruals (estimated values). The application of these indicators to the sample aims to match the observations manipulated by SEC, via Audit Analytics. Both indicator 1 and 2 were able to detect 15% of the cases of manipulation of results of the SEC sample, in a total of 11,882 observations per year. The indicator 3 stands out from the rest because it presents 16% of ability to identify the manipulation cases.
The issue of earnings management and the quality of information have come to play a central role in accounting and finance literature. Strategies used to dampen, or mitigate, accounting results are often referred to as earnings management practices. Literature has sought, over time, to develop models to identify these practices. The main objective of this study was to evaluate the ability to detect, in real life situations, one of the first and most important of these models: the Jones model (1991). Jones (1991) proposes a model based on the use of econometric procedure to estimate discretionary accruals that, contrary to previous literature, considers not to be constant over time. For this, Jones (1991) estimates an equation that explains total accruals as the basis for two explanatory variables of non-discretionary accruals: sales variation and fixed assets. The model was applied to a sample composed of 150,425 company-year observations obtained in the Computstat database for the period 1985 to 2015. Through the development of three indicators it was possible to measure and evaluate the model's ability to detect manipulation of results via discretionary accruals (estimated values). The application of these indicators to the sample aims to match the observations manipulated by SEC, via Audit Analytics. Both indicator 1 and 2 were able to detect 15% of the cases of manipulation of results of the SEC sample, in a total of 11,882 observations per year. The indicator 3 stands out from the rest because it presents 16% of ability to identify the manipulation cases.
Descrição
Palavras-chave
Manipulação de resultados Accruals SEC Earnings management