[PDF] EXTREME GRADIENT BOOSTING METHOD IN THE PREDICTION





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xgboost: eXtreme Gradient Boosting

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EXTREME GRADIENT BOO

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ABSTRACT

Machine learning methods are increasingly being used to predict company

Key words:

1.

An important issue in

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J4.8, linear discriminant analysis, linear support vector machine, logistic T k- k- homogeneity (in terms of financial condition assessment) of the set of companies Comparative studies carried out on selected methods to determine their suitabi

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the comp

STATISTICS IN TRANSITION new series,

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accuracy of the extreme gradient boosting method in predicting company

Table 1.

Ratio Description Ratio Description

W1 net profit / total assets W33 operating expenses / short-term liabilities W2 total liabilities / total assets W34 operating expenses / total liabilities W3 working capital / total assets W35 profit on sales / total assets W4 current assets / short-term liabilities W36 total sales / total assets

W5 [(cash + short-term securities +

receivables - short-term liabilities) / (operating expenses - depreciation)] * 365

W37 (current assets - inventories) / long-

term liabilities W6 retained earnings / total assets W38 constant capital / total assets W7 EBIT / total assets W39 profit on sales / sales W8 book value of equity / total liabilities W40 (current assets - inventory - receivables) / short-term liabilities W9 sales / total assets W41 total liabilities / ((profit on operating activities + depreciation) * (12/365)) W10 equity / total assets W42 profit on operating activities / sales

W11 (gross profit + extraordinary items +

financial expenses) / total assets

W43 rotation receivables + inventory

turnover in days W12 gross profit / short-term liabilities W44 (receivables * 365) / sales W13 (gross profit + depreciation) / sales W45 net profit / inventory W14 (gross profit + interest) / total assets W46 (current assets - inventory) / short- term liabilities

W15 (total liabilities * 365) / (gross profit

+ depreciation)

W47 (inventory * 365) / cost of products

sold

W16 (gross profit + depreciation) / total

liabilities

W48 EBITDA (profit on operating

activities - depreciation) / total assets

158 :

Table 1.

Ratio Description Ratio Description

W17 total assets / total liabilities W49 EBITDA (profit on operating activities - depreciation) / sales W18 gross profit / total assets W50 current assets / total liabilities W19 gross profit / sales W51 short-term liabilities / total assets W20 (inventory * 365) / sales W52 (short-term liabilities * 365) / cost of products sold) W21 sales (n) / sales (n-1) W53 equity / fixed assets

W22 profit on operating activities / total

assets

W54 constant capital / fixed assets

W23 net profit / sales W55 working capital

W24 gross profit (in 3 years) / total

assets

W56 (sales - cost of products sold) /

sales W25 (equity - share capital) / total assets W57 (current assets - inventory - short-term liabilities) / (sales - gross profit - depreciation)

W26 (net profit + depreciation) / total

liabilities

W58 total costs / total sales

W27 profit on operating activities /

financial expenses

W59 long-term liabilities / equity

W28 working capital / fixed assets W60 sales / inventory W29 logarithm of total assets W61 sales / receivables W30 (total liabilities - cash) / sales W62 (short-term liabilities *365) / sales W31 (gross profit + interest) / sales W63 sales / short-term liabilities

W32 (current liabilities * 365) / cost of

products sold

W64 sales / fixed assets

Source:

2. The data used in this study are derived from the Emerging Markets

STATISTICS IN TRANSITION new series,

159
rarely applied; this is due to the dynamic nat (2016), which adopted a time horizon of one to five years. Due to the fact that the 3.

The first step of our empirical research

2017
error AUC Area under the ROC Curve. Once developed, the models were assessed in terms

Accuracy

Sensitivity

Specificity

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