University of Bahrain
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Boosting Algorithms to Analyse Firm’s Performance Based on Return on Equity: An Explanatory Study

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dc.contributor.author Elamir, Elsayed A. H.
dc.date.accessioned 2020-07-20T12:34:24Z
dc.date.available 2020-07-20T12:34:24Z
dc.date.issued 2020-07-01
dc.identifier.issn 2210-142X
dc.identifier.uri https://journal.uob.edu.bh:443/handle/123456789/3971
dc.description.abstract This study aims to use the boosting techniques especially gradient boosting and its extension extreme gradient boosting in predicting firm performance in terms of return on equity that may be considered as a measure of profitability. The models are evaluated using R-squared, root mean square error, and mean absolute error. The global interpretations in terms of partial dependent plot and local interpretations in terms of local interpretable model-agnostic explanations are performed to interpret the prediction for any individual or group of cases. The results show that the extreme gradient boosting is improving the model by about 39% for training set and about 4% for testing set in terms of R-squared. Interesting results are given by the partial dependent and local model-agnostic explanation plots where they are suggesting that the total assets, the total liability and the board size have the most effect on the predicting and interpreting return on equity. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Business analytics, financial management, global model, gradient boosting, machine learning, Rsquared. en_US
dc.title Boosting Algorithms to Analyse Firm’s Performance Based on Return on Equity: An Explanatory Study en_US
dc.volume 10 en_US
dc.pagestart 1 en_US
dc.pageend 17 en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US


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