## 4 Discussion and Conclusions  
In the companion paper we evaluated different techniques to develop an unbiased valuation model, leveraging especially on accounting information and on the widely accepted DCF method. In this second work, we pursued the very same objective, but throught an intensively ML approximation, and evaluating the performance capabilities up to 18 different learning algorithms using the following set of techniques: LR Algorithms, Regression Trees, Ensembles of Trees, SVR, and GPR. Figure 4: Paired trend analysis of significant variables: (**a**) FCF & Deb Net; (**b**) FCF & GDP. As a main conclusion of this work, we could state that it is possible to create an unbiased valuation model that narrows the more than relevant uncertainty by strictly following market expectation, based on state of the art ML techniques. These algorithms could eventually support, contribute, or validate valuation ranges that are especially useful in corporate developments and risk assessment. The methods and variables incorporated in different analysis showed an uneven generalization capacity, attending to markets region and industry sectors, and thus suggesting the use of specialized learning processes for specific purposes. SVR, BagTree, and GPR were identified as the methods providing the top performance over the different implemented scenarios. None of these three was identified as a universal top performer for all evaluated scenarios, although SVR can be said to remain as the one that most regularly followed the top performing method. Under a full consolidated valuation perspective, ML techniques exhibited a limited ability to generalize for blind valuation when historical intrinsic accounting information was exclusively implied. Moreover, the incorporation of exogenous information offered just a slender improvement in the estimation capability for this global consolidated approach. As far as business activities are concerned, we can argue that SVR, BagTree, and GPR closely followed market expectation for the utilities sector, and up to a certain extent for consumer products, cyclical and non-cyclical. And even more, in both cases, the valuation capacity of the models was invariant against macroeconomic differences, showing high stability across markets. In this case, RMSE reached the minimum uncertainty with an average error lower than 1.5 times EBITDA, and also the lowest entropy, revealed in STD values not higher than 0.4. Therefore, it can be said that the availability of sector relative and qualified information contributes significantly to ML models in utilities and consumer products across regions, which is especially true for utilities. In the opposite side we found the industrial and technological sectors, which yielded much higher statistical errors, both in terms of the mean and the STD. Therefore, we can say that these two sectors, from a business perspective might be considerably bundled to other realities not represented in a cross-region industry view. We can argue also that local markets dynamics, country specifics economic, and their much more complex underlying business model, make non-viable the concept of a general consolidated industry-focus scenario. Limited prediction accuracy and unparalleled results are shown when the analysis is performed by market regions, particularly if we split them attending to maturity into emerging and consolidated. A meager dependence was found when exogenous variables were included in emerging markets, and the other way around, this relationship was more intense in consolidated markets. Along with the difference in results and behavior of the valuation models in this regional setup, and for these two separated space-sets, it might be suggested to elaborate more on expanding even further the methods and algorithms, and to consider different sampling structure of the analysis combining markets and sectors for a deeper and closer correlation among peers.