The estimation and power of alternative discretionary accruals models
- Gbadeho, Adedeji Daniel, Adekunle, Ahmed Oluwatobi, Akande, Joseph Oluwafeni
- Authors: Gbadeho, Adedeji Daniel , Adekunle, Ahmed Oluwatobi , Akande, Joseph Oluwafeni
- Date: 2023
- Subjects: Earnings management , Discretionary accruals , Jones model , Working capital accruals
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/11260/13119 , vital:77947 , DOI: https://doi.org/10.31098/jgrcs.v3i1.1197
- Description: Discretionary accruals remain decade’s long measures to detect earnings management in empirical accounting research. The correctness of the specifications and test power of the information content for the models remains unexplored based on samples of most emerging market firms. Yet, country’s-based researchers have increasingly used different Jones-based discretionary accruals to proxy earnings management. The paper aims to evaluate four discretionary accruals models and to decide the most appropriate one for the detection of earnings management. For the aim, we apply regression methods to estimate and evaluate four Jones-type discretionary accruals models – simple Jones, modified Jones, extended Jones cash flow model and working capital accruals – based on evidence of a final sample of 1,852 firm-year of 102 firms in Nigeria during 2001–2020. The results disclose that all models are well-specified such that the likelihood of Type I errors is minimum and below the significance level of 5%. In order to demonstrate the power of the test, the simulations completed identify that the modified Jones model exhibits the highest power capability. The implication of this finding is that the modified Jones model is the most appropriate model to detect earnings management based on the Nigerian sample.
- Full Text:
- Authors: Gbadeho, Adedeji Daniel , Adekunle, Ahmed Oluwatobi , Akande, Joseph Oluwafeni
- Date: 2023
- Subjects: Earnings management , Discretionary accruals , Jones model , Working capital accruals
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/11260/13119 , vital:77947 , DOI: https://doi.org/10.31098/jgrcs.v3i1.1197
- Description: Discretionary accruals remain decade’s long measures to detect earnings management in empirical accounting research. The correctness of the specifications and test power of the information content for the models remains unexplored based on samples of most emerging market firms. Yet, country’s-based researchers have increasingly used different Jones-based discretionary accruals to proxy earnings management. The paper aims to evaluate four discretionary accruals models and to decide the most appropriate one for the detection of earnings management. For the aim, we apply regression methods to estimate and evaluate four Jones-type discretionary accruals models – simple Jones, modified Jones, extended Jones cash flow model and working capital accruals – based on evidence of a final sample of 1,852 firm-year of 102 firms in Nigeria during 2001–2020. The results disclose that all models are well-specified such that the likelihood of Type I errors is minimum and below the significance level of 5%. In order to demonstrate the power of the test, the simulations completed identify that the modified Jones model exhibits the highest power capability. The implication of this finding is that the modified Jones model is the most appropriate model to detect earnings management based on the Nigerian sample.
- Full Text:
BTC price volatility: fundamentals versus information
- Gbadebo, Adedeji Daniel, Adekunle, Ahmed Oluwatobi, Adebayo-Oke Abdulrauf Lukman, Adedokun, Wole, Akande, Joseph
- Authors: Gbadebo, Adedeji Daniel , Adekunle, Ahmed Oluwatobi , Adebayo-Oke Abdulrauf Lukman , Adedokun, Wole , Akande, Joseph
- Date: 2020
- Subjects: Bitcoin price volatility , Bitcoin market fundamentals , Autoregressive Distributed Lag (ARDL)
- Language: English
- Type: article , text
- Identifier: http://hdl.handle.net/11260/6978 , vital:52630 , https://doi.org/10.1080/23311975.2021.1984624
- Description: This paper offers a plausible response to “what explains the sporadic volatility in the price of Bitcoin?” We hypothesized that market “fundamentals” and “information demands” are key drivers of Bitcoin’s unpredictable price fluctuation. We adopt the transfer-function [Autoregressive Distributed Lag, ARDL] model and its Bounds testing approach to verify how the volatility of the price of Bitcoin responds to its transaction volume, cryptocurrency market capitalisation, world market equity index and Google search. We found the existence of long-run cointegration relation and observed that all the variables except the equity index positively explain the volatility of Bitcoin price. The result established evidence that market fundamentals drive erratic swing in Bitcoin price than information.
- Full Text:
- Authors: Gbadebo, Adedeji Daniel , Adekunle, Ahmed Oluwatobi , Adebayo-Oke Abdulrauf Lukman , Adedokun, Wole , Akande, Joseph
- Date: 2020
- Subjects: Bitcoin price volatility , Bitcoin market fundamentals , Autoregressive Distributed Lag (ARDL)
- Language: English
- Type: article , text
- Identifier: http://hdl.handle.net/11260/6978 , vital:52630 , https://doi.org/10.1080/23311975.2021.1984624
- Description: This paper offers a plausible response to “what explains the sporadic volatility in the price of Bitcoin?” We hypothesized that market “fundamentals” and “information demands” are key drivers of Bitcoin’s unpredictable price fluctuation. We adopt the transfer-function [Autoregressive Distributed Lag, ARDL] model and its Bounds testing approach to verify how the volatility of the price of Bitcoin responds to its transaction volume, cryptocurrency market capitalisation, world market equity index and Google search. We found the existence of long-run cointegration relation and observed that all the variables except the equity index positively explain the volatility of Bitcoin price. The result established evidence that market fundamentals drive erratic swing in Bitcoin price than information.
- Full Text:
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