## 4 Discussion and Conclusions
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline
**Variables** & **Errors by Sector** & **Cons. Cyclicals** & **Cons. Non-Cyclicals** & **Industrials** & **Technology** & **Utilities** & **Exposure** & **Exposure** & **Exposure** & **Exposure** & **Exposure** & **Exposure** & **Exposure** & **Exposure** & **Exposure** & **Exposure** & **Exposure** & **Exposure** \\ \hline \multirow{2}{*}{**Endogenous**} & 3.77 & 20.82 & 4.56 & 0.21 & 6.89 & 33.30 & 5.77 & 0.17 & 3.56 & 32.19 & 5.67 & 0.41 & 6.94 & 73.72 & 8.59 & 0.50 & 1.23 & 1.53 & 1.24 & 0.81 \\ Exogenous & 3.53 & 20.30 & 4.51 & 0.20 & 5.33 & 27.19 & 5.21 & 0.30 & 7.51 & 38.05 & 6.17 & 0.28 & 6.36 & 130.14 & 11.41 & 0.08 & 2.15 & 5.50 & 2.34 & 0.28 \\ Endogenous and Exogenous & 3.30 & 17.42 & 4.17 & 0.37 & 6.14 & 24.53 & 4.95 & 0.41 & 4.61 & 24.82 & 4.98 & 0.57 & 6.83 & 71.96 & 8.48 & 0.53 & 1.22 & 1.38 & 1.17 & 0.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Estimated EV errors with linear regression by sector. endogenous variables obtained a RMSE of 6.99, whereas when exogenous variables were incorporated to the input space variability is improved, RMSE was reduced to 6.51, and the determination coefficient \(R^{2}\) reached 0.28. Hence, we can conclude from the results obtained that the operational variables and models evaluated in this work have been validated from an intrinsic valuation capabilities perspective, as it is viable to set the free parameters to match stock market pricing. Additionally, and in order to validate a systematic and unbiased model for companies' valuation, we required supplementary exogenous variables complementing the historical financial statements, which can incorporate general market trends and investor business expectations. Furthermore, the application of simple statistical learning techniques for a bundled analysis boosted the results and performance. The main contributions of this work could be considered in three directions. First, although we did not obtain statistically significance in specific cases, the implementation of the Inverse Method to estimate WACC across markets and industries can offer an alternative systematic tool for value assessment. Furthermore, we consider that the information obtained as a result of this effort could constitute additional valid features to be used in advanced valuation models using ML methods. Second, the published reference WACCs are hardly applicable in reality to accurately match market expectation, but offers a valid representation of global market expectation that again could be considered as part of the input space for applications beyond traditional approaches. Finally, as a first step toward a later in-depth study leveraging on ML methods presented in the companion paper, a linear regression scheme is introduced, with or without exogenous variables. This last analysis is carried out incorporating not only straight from the financial reports accounting data, but also the features obtained in the previous experiments. We expect that these three key findings pave the way toward more sophisticated developments in the valuation field. Therefore, we propose a deeper analysis of ML techniques in the companion paper [49], leveraging on the valid results and conclusions of the present work. [PERSON], [PERSON], and [PERSON] conceptualized the problem, elaborated the state-of-the-art, methods and methodology, conducted the experiments and developed the discussion and conclusions. [PERSON], [PERSON], [PERSON] and [PERSON] discussed the results and contributed to write the final manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by Research Grants Finale (TEC2016-75161-C2-1-R, and TEC2016-75161-C2-2-R) by Ministerio de Economia, Industrial y Competitividad-Agencia Estatal de Investigacion and confunded by Fondos FEDER and KERMES (TEC2016-81900-REDT) from Spanish Government.