«Equity Valuation LinkedIn Corp José Miguel de Figueiredo Bettencourt Moreira da Silva Student Number: 152414022 Instructor: José Carlos Tudela ...»
Everything else constant except revenue projection shift, the target value for the Bad Case is $ 120,5. This represents a steep $ 66 drop in value in comparison to the base case. Yet it reflects the doubts regarding LinkedIn’s inability to take advantage of its marketing solutions and uncertainty towards the future of the Learning department.
Furthermore, it can also represent the possibility of larger players entering LinkedIn’s markets (Facebook and Alphabet). Or a fragmentation of the market into smaller more
specific job boards and PNS, leaving a smaller space for large “one-size-fits-all” players as LinkedIn.
The Good Case looks at LinkedIn as if most of its plans and management decisions achieved all goals by steady state. It represents a market leader position in Talent Solutions and an extremely relevant position in all other business sectors (especially given its competitors).
This shift in revenues would lead to a per share price of $ 284,14. This price is much more line with the market consensus and the investment note that will be analyzed in the next section.
However as mentioned above this scenario firmly sits in LinkedIn coming out on top as the market leader of the PNS sector and becoming a major online player. In sum it is very optimistic. Especially given the tremendous shifts in market cap it expects in a 10-year time frame.
With these three different scenarios, representing three different looks at LinkedIn’s forecasted revenues, a consensus value can be obtained by applying different probabilities of outcome to each price target.
Given the extreme nature of both the Bad and Good Case this dissertation applies a 20% probability to each and a 60% probability to the Base Case. This therefore leads to a weighted average of approximately $ 193.
A Monte Carlo approach to these values, as in the previous sensitivity analysis, yields the following histogram seen in chart 7.
Therefore, applying a uniform distribution to this sample of 3 values points towards a same probability of occurrence between a value within the $ 115 to $ 198 bracket as between the $ 198 to $ 280 bracket. These large brackets are a symptom of the large standard deviation of this small sample and therefore any analysis of these results must take this is consideration.
LinkedIn Corp Equity Valuation
6 Comparison with Investment Note In this section the valuation achieved will be compared to that of an investment bank in order to add more depth to this analysis. The report chosen was published by J. P. Morgan (JPM) on October 30th 2015. The goal is to compare methodologies, forecasts and outcomes so as to ascertain the robustness of the valuation obtained in the thesis.
6.1 Methodology As with this thesis JPM’s report is based on a DCF analysis. Furthermore, the report relies solely on this method, presenting no other valuation alternative as relative valuation, which further solidifies the conclusions in this thesis regarding the value of a relative valuation for LinkedIn.
However, JPM’s report uses a different DCF approach. While the model used throughout this thesis was the APV based DCF model, JPM used the FCF based on WACC. This should not present an issue since theoretically both models should yield the same results, however the same assumptions where not used.
A fundamental factor of difference between the two valuations is the forecast horizon.
While in this thesis a 10-year window was used in order to account for the current high growth period, JPM opted for a window until 2021.
6.2 Discount rate and stable growth rate As seen previously in the sensitivity analysis section, small fluctuation in the discount factors have extremely relevant impacts in the overall valuation. In the case of the APV model valuation the discount factor used was the unlevered cost of equity of 9,82%. JPM opted for using a WACC of 10% for the range in analysis.
It is of note however that running the WACC with the assumptions in the model of this thesis the discount factor obtained would be in fact 9,4%. Therefore, the key difference here can be in the rest of the CAPM equation (or other method) that JPM used in order to reach its discount factor. The key distinctive variables in this case can be Debt to Equity ratio, cost of equity, cost of debt and effective tax rate. However, given LinkedIn’s debt level in comparison to equity, the main reason is most probably the cost of equity obtained by JPM, which must be higher than 10%.
LinkedIn Corp Equity Valuation
The stable growth rate represents the value at which we expect the company to grow at maturity, when market is fully capitalized and the product is fully innovated and explored.
For this reason, the thesis opted for the average GDP of the past 5 years of 2,8%. Yet in JPM’s report the value chosen was 3,5%, which given its narrower scope of analysis may have intended to leave a wider growth potential for the future and be more in line with growth rate of mature companies in similar sectors.
6.3 Forecasts The key difference in approaches in this area was that while in this thesis the chosen method was to look at LinkedIn’s long-term potential market capitalization. JPM looked at the macro conditions of the market LinkedIn operates in and results and margins shown by the company’s reports, which showed not only absolute growth but also better efficiency.
For the period in JPM’s report they estimated a CAGR of 23% in revenues and 30% in EBITDA. In the same period the forecast developed in this thesis expects a CAGR of 19,85% in revenues and 26,55% in EBITDA.
6.4 Overall comparison summary The following table presents the key factors of both valuations.
Hence the valuation discrepancy, in per share values, is around $113 between this thesis’ $187 valuation and JPM’s $300 valuation. What is clear is JPM’s much more bullish position towards LinkedIn’s stock performance. Much higher expectations towards its revenue growth and margins improvement. Two different positions can be taken from this.
Either JPM expects LinkedIn to be much more of a market leader in the business sectors it operates. Or it estimates far larger growth of LinkedIn’s potential markets (whereas this thesis’ approach took a bearish approach of growth in line with world GDP).
LinkedIn Corp Equity Valuation 7 Conclusion Given the volatile and unpredictable nature of tech companies such as LinkedIn, this dissertation clearly presented the need to run several different methods of valuation. Since each method requires specific assumptions, running just one method would effectively cripple a valuation and result in a biased outcome.
Although hard to present a conclusive result, by presenting several scenarios and different approaches to the valuation of LinkedIn, this dissertation hoped to present an unbiased and complete look at the foreseeable development of LinkedIn’s share price.
One of the main difficulties was to point out LinkedIn’s market and peers, given the company’s diverse business model and unique approach to the social network and professional network universe. This forced a compromise in peer selection for multiples analysis, which had to include companies that specialized in each of LinkedIn’s markets.
Furthermore, while being in the cutting-edge is usually a synonym with success and high margins, it also brings about many valuation issues. The new markets LinkedIn is exploring make any future revenue stream forecast very dependent on assumptions on its success. Therefore, an analyst more bullish and confident will reach a value far from an internet-sceptic analyst.
Thus was it of such importance to run a multiple scenario analysis, this put in contrast different looks at LinkedIn’s future success (or lack thereof). The goal was to present a truly unbiased final outcome, leaving the choice to the investor’s risk profile.
By excluding the extreme results of Transaction and MAU multiples, chart 8 shows summary results from this dissertation. Although Multiple analysis in the specific case of LinkedIn has the limitations already mentioned in previous chapters, the average between the results of Enterprise Value and Equity Multiples yields an average of $ 179,57, which is remarkably close to the base case scenario price.
The comparison with J. P. Morgan put in contrast a more optimistic outlook on LinkedIn’s future. With similar assumptions (in terms of variables such as discount rate and growth rate in perpetuity) J. P. Morgan reached a much higher expected price for LinkedIn due to its expectations on revenues.
Therefore, it is the conclusion of this dissertation that LinkedIn is currently (as of December 1st 2015) Overvalued and the recommendation is of a Sell rating (based on the Base Case scenario developed in this dissertation’s model). Hence it is expected that until December 2016 LinkedIn’s per share price is to drop from it’s current $ 249,46 to the dissertation’s prediction of $ 186,77.
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