Unilever has a valuation €177.09 billion. This figure has started by the initial Operating Profit used to analyse Unilever’s DCF model of €8 billion. The DCF valuation model is only one of many valuation models, section B will include an evaluation of the DCF model plus an analysis of alternative valuation models including the P/E Ratio, Book Value and EV/EBITDA. Mauboussin (1997) identifies that a five-year horizon period is acceptable for valuation from companies or investors. However, Mauboussin (1997) argues that competitive advantage period has a fundamental role in the length of the horizon period chosen as it involves how long can the company generate excess returns from investment, therefore, how do the change in variables in the industry affect horizon period? There are three main factors that need to be considered, Mauboussin (1997) identifies the three as Barriers of entry, current rate of return, and industry dynamics. Using Porters five forces, there is significant threat to Unilever in regard to the significant level of competition of the industry as shown on the Q3 statement (Unilever, 2017:1), including firms such as Kraft and Nestle (Reuters, 2017). And a threat of consumer markets remaining fragile (Unilever, 2017). Therefore, a five-year horizon period is suitable as Clement-Grancourt and Fraysse (2015) risk and contingency of decision making will be overcomplicated from a ten-year horizon period. The spread between ROIC and Cost of Capital in the horizon year is 11.51% and decreased significantly to 5.35% in Perpetuity, this therefore had a decrease of 6.16%. Unilever’s ROIC was calculated at an average of 17.63% (Reuters, 2017. FT, 2017. MorningStar, 2017). According to Koller and Goedhart (2010) and Damodaran (2007) companies that have high a ROIC compared to cost of capital tend to create more value. Unilever has 400 brands (Anon, 2017), showing the vast size, therefore, Koller and Goedhart (2010) identify that in Unilever’s case finding good, high value creating projects are difficult because of their size, and their challenging industry as stated on the Q3 statement (Unilever, 2017:1). The lower spread figure of 5.35% in Perpetuity correlates to the general theory of state force competition (Mauboussin,1997). The Growth rate for Unilever was measured at 3.81% at horizon period. This was calculated by taking the average of the operating profit for Unilever in 2015 and 2016 in the annual statement (Unilever, 2017:25). In terms of the perpetuity growth figure, a figure of 0.87% was calculated. This figure was calculated from the theory in which Macabacus (2017) stated. Perpetuity growth rate was calculated by using a five-year average on Germany’s inflation rate and GDP, the decision was made due to different factors, Moody’s (2017) emphasises Germany’s strong economic conditions and very high fiscal strength. Furthermore, it is essential matching Unilever’s valuation model currency to the country, therefore, allowing the results to be more viable. Therefore, Germany has a five-year average inflation of 1.8% (Trading Economics, 2017), and five-year GDP average of -7.74% (World Bank, 2017), therefore, concluding with the final perpetuity growth figure. Berkman et al. (2000), Sinclair (2010), MorningStar (2017) and Gajek and Kuciński (2017) identify that DCF implements discounting cash flow forecasts based on information in listing prospectuses. DCF is regarded as forward looking and dependant on future expectations (Macabacus, 2017) (Fisher (1930). Kiss (2015), Copeland (1999) Berkman et al. (2000) and Kaplan and Ruback (1995) evaluate the method in being identify DCF to be the best measure for estimating value. Kiss (2015) analyses DCF as a suitable model for different plans, and strategies in the planning process, this is because DCF allows businesses to value and modify plans on the business’ expected strategies if assumptions and conditions have been fulfilled (Schwenker and Spremann, 2008. Macabacus, 2017. Steiger, 2008). Furthermore, Macabacus (2017) and Hill (1998) identifies DCF allows the opportunity for firms to achieve full potential in regard to performance, this is because DCF allows capital budgeting in relation to the flow of monetary. Macabacus (2017) emphasizes the problem of terminal value representing a large amount of the DCF valuation, this is because in valuation, it is largely dependant on the assumptions of terminal value, rather than operating assumptions for the asset or firm. Mauboussin (2006) implies that students who do learn and practice building the DCF model in the business school, it is vital for investors in real life to ensure that the model is economically sound and transparent, this is because of the complications with regards to the changes of inputs, in which inevitable leads to different value figure. Ruback (2011) and Macabacus (2017) analyse DCF as being reliant on the quality of assumptions in expected cash flows, terminal value and discount rate. these figures often ignore low probability downside events; therefore, forecasts could be either excessively optimistic due to overvaluation (Damodaran, 2006), or biased. Ruback (2011) identifies that the problem of forecasts only will be solved if appropriate adjustment has occurred. Damodaran (2006), Jaffee (2012) and Steiger (2008) review DCF as an opportunity to be manipulated or misused. However, Gode and Ohlson (2006) and Fisher (1930) analyse DCF as being directly focused on the generation of cash flow, therefore, being more difficult to manipulate due to the presence of FCF (Hope, 2006). Thus, the use of FCF strengthening the DCF valuation method as it becomes more credible due to the fewer assumptions and accounting practices (Macabacus, 2017). Berk and DeMarzo (2014), Tatnall (2007), Watson and Head (2013), Gottwald (2014), Wu (2014) and Cox (2016) identify that P/E is a clear indicator of share value. Watson and Head (2013) state that this ratio shows how much investors are willing to pay for a company’s shares. Wu (2013), Thomas and Zhang (2013) and Zarowin (1990) outline that investors can identify market expectations of future growth. Gottwald (2014) states that P/E ratio is still most widely used as a valuation tool. Chisholm (2009), Gottwald (2014) and Cox (2016) argue that comparability of similar companies is possible. Berk and DeMarzo (2014) relate P/E as varying significantly across different industries and tend to be highest for industries with higher growth. Gottwald (2014) and Cox (2016) states a number of different P/E ratios that exist, which are calculated using different inputs, including Trailing P/E and Historical P/E, therefore, it is vital that clarity is present. Furthermore, Mauboussin and Callahan (2014) identified that managements can manage or manipulate earnings. Moreover, Mauboussin (1997) argues that there are three key significant criticisms to this ratio. Firstly, multiples exclude risk. Secondly, it excludes capital needs. And thirdly, the ratio does not incorporate the time value of money. Twain (2012), ACCA Global (2012), and Fernandez (2007) review this method of valuation as subtracting the book value of assets by the book value of liabilities. These figures as are easily found on the balance sheet (Fernandez, 2007) found on the annual report. Furthermore, Twain (2012) reviews the simple concept to be mostly acceptable by analysts. Twain (2012) and ACCA Global (2012) analyse several flaws regarding the method. Firstly, ACCA Global (2012) and Walther and Skousen (2009) argue BV being practically useless as it relies on historical (sunk) costs and relatively arbitrary depreciation. Furthermore, Twain (2012) analyses that the method is under GAAP, rates of long term liability are not adjusted to reflect market changes. Mauboussin (1997) states that EV is the sum of the firm’s market value of equity and debt less excess cash, whereas EBITDA is the operating income plus all non-cash charges. In other words, Damodaran (2006) explains this method as a function of the same variables that determine the operating earnings multiples. Gray and Vogel (2012) and Loughran and Wellman (2009) review this method as being the best valuation method to use as an investment strategy. Furthermore, Mauboussin (1997) analyses this method as it gets closer to reflecting the economics of the business, furthermore, he relates this model to being simple (Fabozzi et al., 2008) and useful for mergers and acquisition analysis. Furthermore, Platt (2004) and Arnold (2013) review the method as excludes non-cash items such as depreciation and amortization. Mauboussin (1997) suggests that the critics of EBITDA are the same as suggested in the P/E ratio. Multiples exclude risk, it excludes capital needs, and no incorporation of time value of money. Harvard Business Review (2017) suggest that EBITDA could be misleading and manipulated, the change of regular expenses to asset results in the reduction of expenses and inflated depreciation. Furthermore, Forbes (2017) argues that working capital is ignored and does not reflect changes in working capital requirements. Fama and French (2004) identified that Harry Markowitz (1959) provided a foundation in which William Sharpe (1964) and John Linter (1965) built a widely renown model called the Capital Asset Pricing Model (CAPM). Watson and Head (2013) identify the assumptions as a Perfect Capital market, meaning all information is freely available to investors and arrive at similar expectations. Furthermore, it is implied that no taxes and transactions are involved (Damodaran,2006). Investors are able to borrow and lend at a risk-free rate. Investors hold diversified (market) portfolios, therefore eliminating unsystematic risk. Investments occurs over a single, standardised horizon period; therefore, two sets of data cannot be compared over different years. Homogenous expectations, Fernandez (2014) analyses this as all investors have equal expectations about asset returns. Fernandez (2014) analyses the assumptions as unrealistic compared to the real world, especially, the Homogenous expectations as shown in appendix A, whereas, investors are regarded as heterogeneous expectations. Fama and French (2004) and Lai and Stohs (2015) identify the simplification of assumptions, resulting in being very problematic to formulate a good proxy for the market portfolio. Fama and French (1996) name the market proxy inefficient, therefore, applications that use them rely on the same flawed estimates that undermine empirical tests of CAPM. Shah et al. (2011), Douglas (1968), Black et al. (1972), Miller and Scholes (1972), and Fama and MacBeth (1973) found that CAPM underestimates the true risk premium on securities. Moreover, a major problem identified by Fama and French (2004) is that in order to produce a wide range of average returns, portfolios are formed by sorting stocks on price rations, this is a major problem as the average returns are not positively related to market betas. Fama and French (2004) also emphasize the issues that it has never been an empirical success, giving an example of funds which are concentrated on low beta stocks, small stocks, or value stocks tend to a positive abnormal return relative to the CAPM predictions. Furthermore, Benson and Faff (2013) identify Fama and French (1992) stating that beta has no ability to explain cross sectional variation in equity returns, following their lead, the case against beta has been forcefully presented by Grinold (1993), Davis (1994), He and Ng (1994), and, Fama and French (1993,1995,1996,1998). Lai and Stohs (2015) and Berg (1992) agree with Fama’s strong claim that the sole variable of beta with regards to return on stocks as being dead. Moreover, Cai et al. (2012) and Dempsey (2013) make a proposition that investors can expect to attain a risk-free rate plus a market premium which is multiplied by the exposure from the market where the beta identifies the exposure of an asset to the market, Dempsey (2013) highlights the idea that investors impose a single expectation of return and that beta is largely ineffective. ACCA Global (2017) identify the constant changes of beta over time, therefore, increasing uncertainty. ACCA Global (2017) identify that the model generates a theoretically derived relationship between the required rate of return and systematic risk. Damodaran (2006) argues that CAPM is a remarkable model at capturing an asset’s exposure to all market risk in one figure, known as the beta. Watson and Head (2013) identified beta is a measure of systematic risk, therefore, since investors diversify portfolios, eliminating unsystematic risk. Furthermore, Brown and Walter (2013) and Fama and French (2004) state that it is still extensively used by corporations, and by academics to provide an introduction to the fundamental concepts of portfolio theory and asset pricing. Moreover, Shah et al. (2011), Fama and French (2004) and Baker and Riddick (2013) suggest the main reason of usage is the simplicity and convenience of CAPM. Damodaran (2006) gives importance that cash flows should match Risk Free Rate, therefore, since the valuation model is in euros, it is only fair that a five-year German government bond of 0.30% was used to represent the risk free in the horizon period along with a rate of 1.62% in perpetuity (Bloomberg, 2017). Furthermore, Damodaran (2008) and EY.com (2015) state that government bond yields are favoured as they offer low risk in return for the rate of risk, suggesting that it is a good proxy. Damodaran (2008) identifies that bonds should have the lowest chance of default, Germany has a credit rating of AAA, due to strong economic conditions and very high fiscal strength (Moody’s, 2017). Arnold (2013) suggests that horizon period should match the year of the yield, therefore a bond yield of five years was used. Damodaran (2008) suggests a thirty-year bond yield for perpetuity. The Risk Premium has been calculated using the Equity Risk Premium from Damodaran (2017) figure for Germany, therefore, in the horizon period, a risk premium of 5.76% was used. This is because since Risk Free Rate has been calculated using German bonds, it seems fair to do the same also for Risk Premium. Furthermore, KPMG (2017) indicates utilising a global Risk Premium of 5.5% whereas Norges Bank (2016) identify the global Risk Premium of 5.9%, therefore, our German Risk Premium is in line with estimates. Moreover, Fernandez et al. (2017) reviews his German Risk Premium Figure as 5.7, again, coinciding with the figure from Damodaran (2017). During Perpetuity, Fernandez et al. (2017) and Johnson et al. (2007) predict a Risk Premium of 3-7% during the next thirty years, therefore, a mean figure of 5% has been used. However, it is important to evaluate that surveys are optimistic and estimate the hoped market return. (Fernandez et al., 2016). Damodaran (2010) states that a proxy that could be a suitable input for beta in perpetuity is the industry beta. Therefore, the beta figure in perpetuity is 0.79 (Reuters, 2017), Unilever’s industry beta comes from competitors such as Kraft, Nestle, and more (Reuters, 2017). Beta in the horizon period is 1.01, calculated average from Beta figures in Morningstar (2017), Yahoo Finance (2017), Reuters (2017) and FT (2017), therefore, giving a consensus and more reliability to the figure, since that websites do not offer a complete analysis to their working out for the figure. Anon (2005), Fernandez (2014) and Damodaran (1998) analyse that beta is a measure of systematic risk, and since that beta is volatile when compared to market fluctuation, it is vital that it is constantly reviewed, since that the beta figure does not exist (Fernandez, 2014).