Financial Performance Evaluation of Turkish Construction Companies in Istanbul Stock Exchange (BIST)

Önder, Emrah; Altıntaş, Abdurrahman

doi:10.6007/ijarafms/v7-i3/3237

Cited by 11 publications

(1 citation statement)

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“…In recent years, there are a lot of concerns to have accurate and reliable prediction of earnings, growth and firm performance especially with the huge advancements in machine learning algorithms and the availability of large data. These predictions not only measure financial performance of a firm but also helping financial and operation managers with investment, production and financing decision making and outside investors to understand the performance of the firm; see, [1], [2], [3], [4], [5], [6] and [7]. Return on equity (ROE) is calculated as a net profit after tax over the total shareholder's equity.…”

Section: Introductionmentioning

confidence: 99%

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

Elamir¹

2021

IJCDS

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This study aims to use the boosting algorithms especially gradient boosting and its extension extreme gradient boosting in predicting firm performance in terms of return on equity that could be considered as a measure of profitability and to use the partial dependent plot and local interpretable model-agnostic explanation techniques to explain the model and its prediction.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. By taking over-fitting in consideration the gradient boosting model is a better choice than extreme gradient boosting. The important scores suggest that the total liability, beta coefficients and the total assets have the most effect on return on equity.

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