## 3 Experiments and Results
### Prediction Results Based on Non-linear Supervised Learning Methods  
The objective of this subsection was to predict the EV, calculated as the sum of the stock value and the net debt, based on endogenous and exogenous variables examined individually and jointly. Regarding endogenous variables, the projection of FCF for 5 years and the net debt were considered, whereas WACC, GDP, CPI and RIR were evaluated as exogenous variables. Since we explored a range of markets and sectors, operative normalization was applied to each feature, which consisted of dividing the EV by Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) (the gross cash flow that the company can generate). Subsequently, the dataset was split into two new subsets, namely training (70% of samples in the dataset) and test (30% of samples in the dataset). We applied several non-linear supervised ML techniques, such as decision trees, bagging trees, boosting trees, SVR and GPR, all to predict the EV based on endogenous and both endogenous and exogenous variables. Figure 1 shows the actual normalized EV values (in blue) and their estimated EV (in red) for the described ML methods. The panels on the left ([a], [c], [e] and [g]) show the predictions based on  
endogenous variables obtained from the DCF method. The panels on the right ([b], [d], [f] and [h]) represent the real and estimated EV when the exogenous variables are also included in the analysis as input data. Visual inspection of charts indicates a tighter relation among estimates and real values when exogenous variables are included in the sample bases. A good example of it is illustrated in panels (**g**) and (**h**) corresponding to the GPR technique. These results are also reflected in Tables 1 and 2, where we also evaluated the impact of the predictions depending on sectors and markets, respectively, considering the RMSE as a key merit of the figure. Figure 1: Representation of actual values for operational normalized EVs (in blue) and estimated EVs based on developed ML methods (in red), when using endogenous normalized variables (left panels) and both for endogenous normalized and exogenous variables (right panels). The benchmarked algorithms were decision trees (**a**,**b**), bagging trees(**c**,**d**), SVR (**e**,**f**), and GPR (**g**,**h**). Table 1 summarizes the results in terms of MAE, MSE, RMSE and the D-index for all companies, and separately for each industrial sector; consumer cyclical, consumer non-cyclical, industrial, technology and utilities companies. This analysis was developed in two different scenarios: (i) just incorporating endogenous variables, and (ii) taking into account both endogenous and exogenous variables. As a general result, it can be observed that the GPR model showed a certain predominance as the best model when it was considered both internal and external variables. The other way around when only endogenous variables were considered, we could not quite set a best performing configuration. As a general result, we can visualize that prediction error decreased across all methods when exogenous variables were added. A special interest should arouse the utilities sector, as this sector presented the best ratios in terms of errors (RMSE of 1.21 in best case) and also in contrast with the previous generalized statement, contour information interest should arouse the utilities sector, as this sector presented the best ratios in terms of errors ( did not improve results in any implemented model (RMSE of 1.22 in best case).