## 4 Discussion and Conclusions  
All the previously presented results enable us to conclude that it is not possible to develop effectively an unbiased company-valuation model by leveraging exclusively on endogenous references, for a specific market-region, sector, company, or year, and by using the different proposed DCF models with different FCF forecasting strategies. One key restriction is the lack of generalized consensus of the application of an effective closed form to obtain a discount rate WACC and its corresponding constituents that can fit the real company valuation in stock markets. With respect to the WACC analysis performed in this paper through the Inverse Method and direct approach for individual companies, although offering relevant findings, it does not allow us to justify the existence of a single value of WACC under a global perspective, and either from the sector, region, or year perspectives. Additionally, two relevant conclusions arise from this analysis: First, FCF forecasting strategies using polynomial fitting are consistently offering better results in modeling companies' valuation; second, it is possible to obtain a WACC that fits every single company and year, and this figure could eventually be persistent in certain companies over the years. Earlier results were evidenced to be improved by further analysis incorporating not only endogenous, but also exogenous information as drivers of external market and business environment trends, and using simple statistical learning methods, specifically linear regression. We found that the MSE and the MAE showed higher magnitudes in the valuations. Better results were obtained through a double normalization of input space, namely a statistical description with zero mean and unit STD, and a standardization (operation-oriented normalization). In particular, input space operative normalization, and the statistical normalization in a lower extent, enhance the generalization capabilities by making them independent from volume or currency. Regression analysis with only \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Variables** & **Coefficients** & **Estimate** & **pValue** \\ \hline \multirow{8}{*}{**Endogenous**} & (Intercept) & 10.9898 & \(3.20\times 10^{-129}\) \\  & \((FC_{n-1})/EBITDA\) & 0.5794 & 0.1240 \\  & \((FC_{n-1})/EBITDA\) & \(-\)0.6179 & 0.1170 \\  & \((FC_{n-2})/EBITDA\) & \(-\)0.7975 & 0.0626 \\  & \((FC_{n-3})/EBITDA\) & 0.4280 & 0.3091 \\  & \((FC_{n-5})/EBITDA\) & 0.4369 & 0.2311 \\  & \((FC_{n-5})/EBITDA\) & 1.1332 & 0.0058 \\  & \((N.Debt)/EBITDA\) & 2.1388 & \(1.43\times 10^{-40}\) \\ \hline \multirow{4}{*}{**Exogenous**} & (Intercept) & 10.9898 & \(2.16\times 10^{-124}\) \\  & WACC & 1.8927 & 0.0002 \\  & RIR & \(-\)1.9801 & 1.42 \(\times 10^{-06}\) \\  & CPI & \(-\)0.8046 & 0.1171 \\  & GDP & 0.5356 & 0.2104 \\ \hline \multirow{8}{*}{**Endogenous and Exogenous**} & (Intercept) & 10.9898 & \(5.16\times 10^{-139}\) \\  & \((FC_{n})/EBITDA\) & 0.7049 & 0.0478 \\  & \((FC_{n-1})/EBITDA\) & -0.2783 & 0.4537 \\  & \((FC_{n}(-2))/EBITDA\) & -0.6622 & 0.0981 \\  & \((FC_{n-3})/EBITDA\) & 0.5440 & 0.1662 \\  & \((FC_{n}(-4))/EBITDA\) & 0.5488 & 0.1822 \\  & \((FC_{n-5})/EBITDA\) & 0.9602 & 0.0110 \\  & \((N.Debt)/EBITDA\) & 2.6698 & \(1.83\times 10^{-11}\) \\  & WACC & 2.3151 & \(1.19\times 10^{-06}\) \\  & RIR & \(-\)1.7905 & \(1.72\times 10^{-06}\) \\  & CPI & \(-\)0.5337 & 0.2596 \\  & GDP & 0.6064 & 0.1161 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Coefficients from linear regression model.