## 1 Introduction  
Estimating the fair price of a company or the Enterprise Value (EV) is a major issue in academic and business literature, as it has relevant impact in a number of situations. Global corporate operations, mergers and acquisitions, alliances and joint ventures, corporate bond issuing, international public offerings (commonly IPOs), banking credit, portfolio management, risk assessment, and in some cases the top-management bonus, require fair valuations compelling with well-founded and generally accepted systematic assessments [1; 2; 3; 4; 5; 6; 7]. Investors, analysts, and sometimes academics tend to approach this objective focusing in public companies, for which a large amount of information and knowledge is available. However, this working line conveys the development of tight tailored models, with relevant chances to overfit the selected target, and subsequently lacking generalization capability over time, sector, market, or region. In this category we can include the investment banks' Investor Research (IR) reports and the Technical Analysis (TA) reports [8; 9; 10; 11; 12; 13; 14; 15]. Starting with this second group, TA reports support their models on market behavior and heuristic estimates, solely funded on historical price-volume data. In this case, forecasting estimates rely on trading rules attached to chart patterns fully dissociated from fundamental business data. Alternatively, investment banks and consultancy firms base their IR reports on deep industry knowledge and on available business information. This way of proceeding is generally known as Fundamental Analysis (FA), as it uses historical and solid financial statements (fundamentals), as well as commercial business forecasts [1; 7; 16; 17; 18]. This methodology offers a much more rigorous and deeper understanding of the company real activity, although such a deep knowledge may limit the generalization capabilities. Additionally to these very well established and known approaches, academics have also proposed statistical and mathematical methods with the aim of dissociating the tailoring and tight-fitting approach of IR and FA reports. This particular setting is especially noticeable in recent studies where machine learning techniques are applied to credit risk assessment and stock pricing forecast [19; 20; 21; 22]. Under this view, in an attempt to categorize these methods, and attending to the nature of the supporting variables, we propose to classify them into three basic categories, namely, balance-sheet statements, Income Statements (also known as multiples), and value-creation approaches [23], as shown in Table 1.  
The first set of methods, or balance-sheet based methods, are based on pre-existing accounting information in company records and they are strongly supported by balance-sheet statements and book value related ratios [24]. These methods usually define and apply specific corrections to actual statements by incorporating contextual factors, such as future taxes, good-will, unrealized capital gains, or project finance associated variables. A remarkable reference, and probably the most cited academic work in this area, is the Adjusted Book Value proposed by [PERSON] in its different editions [14; 25]. The second set of methods are the multiples based methods, also known as comparable methods, which use the effective value of other operations as a benchmark to extrapolate to current valuations. Examples of references for benchmarking purposes are stock price, comparable corporate operations, balance-sheet ratios, and consensus investor-related valuation reports. IR reports always incorporate this information as a back reference for validation and support for the valuations stated in the reporting document [7; 26; 27]. The third set, or value-creation based methods, includes the Discounted Cash Flow (DCF) as the most remarkable and frequently used tool for large-company valuation [16; 28; 29; 30; 31; 32]. Another method also included in this set is the now popular Residual Income Model (RIM) [33; 34; 35; 36].