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Diagnosing Assets Impairment By Using Random Forests Model

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    (Department of Accounting, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)



    (Department of Accounting, Chaoyang University of Technology, 168 Jifong E. Road, Wufong Township Taichung County, 41349, Taiwan)

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    This study develops a diagnosing model to examine the outcomes of assets write-off in enriching the literatures of assets impairment. Prior studies employed the Logit, linear and Tobit regression models to classify the determination of assets impairment and to diagnose the magnitude of the impairment, respectively. However, the drivers of assets write-off are somewhat complicated explicitly or implicitly, these models are unlikely to provide fairly satisfactory results. To improve the diagnosis, the Random Forests model is used for the classification determining and the magnitude diagnosing of assets impairment in this study. The result reveals that the Random Forests model outperforms the Logit and linear regression models in each case with variables selected by individual wrapping approach. This study also demonstrates diagnostic checks for both models with similar selected variables. The results are robust to these various specifications.

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    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal International Journal of Information Technology and Decision Making.

    Volume (Year): 11 (2012)
    Issue (Month): 01 ()
    Pages: 77-102

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    Handle: RePEc:wsi:ijitdm:v:11:y:2012:i:01:p:77-102
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