Predicting Goodwill Impairment Using Machine Learning
This paper compares a range of models in the machine learning repertoire,which are widely used in finance and accounting,in their abil-ity to predict the occurrence of goodwill impairment,using a sample of non-financial listed firms in China from 2008 to 2020.The aim is to help investors predict the occurrence of goodwill impairment and then mitigate the impact of goodwill impairment on the stability of market.We find that the two ensemble classifiers,random forest and XGBoost,outperform all other classifiers.Furthermore,we also find that the average buy-and-hold return of the firms that are predicted to make goodwill impairment charges by random forest and XGBoost is significantly lower than the predicted non-impaired firms,and the re-turn gap between the predicted impaired firms and the predicted non-impaired firms is approaching the real return gap with improved forecast accuracy.That indicates that the prediction model of goodwill impairment based on machine learning algorithm can effectively identify the goodwill impairment risk,thus promoting the market to absorb the goodwill impairment risk.Finally,we examine the effect of the scale of the training set on the predictive perform-ance of the two ensemble classifiers and find that the increase of the scale of the training set does not necessarily improve the predictive performance be-cause the factors of goodwill impairment and their importance change over time.
Goodwill ImpairmentMachine LearningModel IntegrationThe Scale of the Training Set
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