Modeling and predicting earnings per share via regression tree approaches in banking sector: Middle East and North African countries case

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The regression tree approach is an effective and easy to interpret technique where it utilizes a recursive binary partitioning algorithm that divides the sample into partitioning variables with the strongest correlation to the response variable. Earnings per share can be considered as one of the main factors in making the investment decision. This study aims to build a predictive model for earnings per share in the context of the Middle East and North African countries (MENA) . The sample of the study consists of sixty-three banks, which were chosen from eight countries, with a total of six-hundred thirty observations. The simple regression, regression tree, and its pruned regression tree, conditional inference tree, and cubist regression are used to build the predictive model for earnings per share that depends on total assets, total liability, bank book value, stock volatility, age of the bank, and net cash. The results show that the cubist regression is outperforming other approaches where it improves root mean square error for the predictive model by approximately double in comparison with other methods. More interesting results are obtained from the important scores, where it shows that the total assets of the bank, bank book value, and total liability have the biggest impact on the prediction of earnings per share. Also, the cubist regression gives an improvement in R-squared over other methods by at least 30% and 23% using training and testing data, respectively.

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    • Figure 1. The correlation matrix, histogram, and scatter plots for the study variables
    • Figure 2. Linear regression variable importance scores for EPS model
    • Figure 3. Basic regression tree for EPS model
    • Figure 4. Basic regression tree variable importance scores for EPS model
    • Figure 5. Pruned regression tree for EPS model
    • Figure 6. Pruned regression tree variable importance scores for EPS model
    • Figure 7. Conditional inference tree for EPS model
    • Figure 8. Conditional inference tree variable importance scores for EPS model
    • Figure 9. Cubist regression variable importance scores for EPS model
    • Table 1. Descriptive statistics for the study variables
    • Table 2. Linear regression analysis for EPS model
    • Table 3. Linear regression variable importance scores and performance metrics for EPS model
    • Table 4. Basic regression tree variable importance scores and performance metrics for EPS
    • Table 5. Pruned regression tree variable importance scores and performance metric for EPS
    • Table 6. Conditional inference tree variable importance scores and performance metrics for EPS model
    • Table 7. Cubist resampling results across tuning parameters for 566 samples and 6 predictors
    • Table 8. Cubist regression approach variable importance scores and performance metrics for EPS model
    • Table 9. Performance metrics for the study methods
    • Data curation
      Elsayed A. H. Elamir
    • Formal Analysis
      Elsayed A. H. Elamir
    • Methodology
      Elsayed A. H. Elamir
    • Software
      Elsayed A. H. Elamir
    • Writing – original draft
      Elsayed A. H. Elamir