## 3 Results
### Company Valuation Based on DCF  
In an attempt to validate DCF as an standalone valuation model, we analyze here how it fits to the actual EV in listed companies. For this purpose, we used Equation (5) to apply the different FCF forecasting strategies mentioned in Table 3, which were singularly based just on historical accounting information. Implied valuation results for the listed companies (NPV) were then benchmarked against market capitalization including the required liabilities adjustment. From an analytical point of view, it is a general consensus that all the variables included in Equation (5) are directly reported in the balance sheet and in the income statement. Due to all the difficulties for the projection of the FCF [62], different projection strategies are proposed to obtain a closer estimate of EV. The only exception to this statement is the WACC, given that there is no simple and direct approach to obtain it, as previously mentioned. Therefore, in this section we took a twofold approach to methodologically overcome this uncertainty: (i) by using the inverse method to back-obtain the effective discount rate that matches the actual market valuation; (ii) by using the reference WACC reported in literature. Inverse Method. In this experiment, we applied the previously described Inverse Method to estimate the implicit WACC that would eventually be applied to forecasted FCF to get the actual valuation for each company, year, and DCF model. As an example of the Inverse Method implementation, Figure 1 shows the effective WACC for 3 MCo in 1992 when applying the five evaluated forecasting strategies. In this example WACC ranged from 6% to 10%. Special mention requires the fact that M4 and M5 provided the very same value. A wider extension of this analysis for the same company over all available years provided a much wider variability of WACC, ranging from values between close to 0% up to 33%. Statistical perspective shows again that methods M4 and M5 offered a more limited variability, closing the figures from 4% to 9%. Figure 2 provides an over-the-years and companies-wide outlook of the implicit effective WACC by forecasting strategies. WACC values over 70% were considered outliers and they are removed from the samples. Panels (d) and (e), corresponding to methods M4 and M5, respectively, show more restricted dynamic behavior for WACCs, exhibiting as a consequence much lower variability, which is consistent with previous visual inspections and with the statistical results shown in Table 4. As a summary, we can argue that although variability, expressed as the Standard Deviation (STD), was high in all methods accounting for over 50% of mean or median, models M4 and M5 significantly outperformed the others. It is relevant to mention that M1 presented the poorest results in terms of stability. An empirical analysis of the previously mentioned companies (Inditex, 3 MCo, and Coca-Cola) for 12 years in a row (see in Table 5) showed a very stable Year-on-Year (YoY) behavior for the M4 model. This result can be recognized as a positive contribution, as it validates the aforementioned forecasting strategies to be considered as a valid feature to be incorporated in subsequent valuation models. Consolidated analysis for sectors, market, and regions are shown in Figures 3 and 4. Visual inspection of these figures does not anticipate any specific pattern for any of the analyzed models. The statistical view of WACC obtained with the Inverse Method are represented in Table 6, which confirms these expectations and the difficulty to set an effective WACC that across companies can fit any region or sector. This fact is numerically verified because the STD accounts for almost over half of the mean or of the median, not offering better results than the global view presented earlier. As for major findings of the application of the Inverse Method of the DCF model, and for a wide set of companies from different regions, sectors, and years, we can state the following ones.