## 3 Experiments and Results
### Trend Analysis  
As mentioned in the preceding sections, the DCF method is undoubtedly the most commonly used method when the potential value of a company is analyzed. It was also explained that the main drawback of this method is that it is highly sensitive to certain precise parameters. In order to estimate this dependence, and to validate the model when the valuation ranges around any targets value, a sensitivity analysis is commonly incorporated as part of the equity valuation study [4; 15]. However, this type of analysis or technique allows the underlying business model to be validated, especially when the complex interrelationships among the variables in the model, make it necessary to confirmthat the expected trend is consistent around a valuation set point. To this end, we created a trend analysis to appraise the consistency of the addressed models.  
The model must be valid over a wide range of input values, and there is the additional complexity of establishing the evenness point or simultaneous valuation set point for all companies, regions and sectors. Consequently, double normalization (operational and statistical) of the input variables had to be applied. Once both normalizations had been applied, all variables in the input space presented null mean and unit variance, and the trend analysis could then be carried out in the range of one time the STD, in another words, input variables ranging from \(-0.5\) to \(0.5\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Oper. Nor.**} & \multicolumn{2}{c}{**Oper. \& Stat. Nor.**} & **Extended** & **Key** \\ \cline{2-7}  & **End.** & **End. \& Exo** & **End.** & **End. \& Exo** & **Features** & **Features** \\ \hline
**Linear Regression** & 6.47 & 6.02 & 6.47 & 6.02 & 6.16 & 6.12 \\
**Tree** & 6.22 & 5.19 & 6.54 & 5.63 & 5.50 & 5.32 \\
**Bagtree** & 5.44 & 4.45 & 5.33 & 4.50 & 4.63 & 4.75 \\
**Boosting Tree** & 7.49 & 5.45 & 7.49 & 5.46 & 5.81 & 7.20 \\
**SVR** & 5.75 & 5.42 & 6.00 & 5.37 & 6.29 & 5.39 \\
**GPR** & 5.85 & 4.44 & 5.85 & 4.44 & 4.57 & 5.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result comparison based on RMSE for better performing ML implemented techniques. Columns from left to right: (i) endogenous variables with operational normalization; (ii) endogenous and exogenous variables with operational normalization; (iii) endogenous variables with operational and statistical normalization; (iv) endogenous and exogenous variables with operational and statistical normalization; (v) extended features, incorporating additionally to previously defined endogenous and exogenous, the NPVs developed in previous paper; and (vi) the most relevant features in double-normalized endogenous and exogenous variables, namely: first year FCF, net debt, RIR and GDP.  
Figure 2: Feature relevance based on decision trees for normalized EV estimation when using: (**a**) Only endogenous variables; (**b**) Both endogenous and exogenous variables.  
Figure 3 shows the local trend of widely understood key variables, for a better performing method (BagTree, SVR and GPR). The abscissa axis corresponds with the double-normalized variables, and the ordinate axis represents the operationally normalized output, EV/EBITDA. Visual inspection of these charts are recurrently coherent with expected trends, as the output showed a positive response versus the different FCF, CPI and GDP, whereas this response was inverse versus net debt, WACC and RIR. Individual exceptions were found in certain realizations and for a limited number of variables. In that direction and from illustrative proposes, the outcomes depicted in these figures, acknowledged this reality for variable RIR in Figure 3b and FCF3 and WACC in Figure 3c.