Case Study Written Report
Week 8 Valuation: Laura Martin
Karen Chan z3242429 20
Yifeng Chen (Nino) z3283995 20
Tony Richardson z3253113 20
Weitao Wu (Tony) z3284666 20
Wendy (Wenyu) Yan z3241580 20
Multiples versus DCF analysis
Multiples analysis is simple to understand and apply. The inputs for the multiple are publicly available, though are vulnerable to accounting manipulation. Also, it is difficult to obtain a truly comparable large sample of firms. Multiples analysis is backward-looking, reliant on historical/current data to obtain multiples. It reflects relative value rather than the intrinsic value which DCF valuation produces.
DCF analysis generates an intrinsic value as it relies on data specific to the firm. DCF analysis factors in time value of money, and thus is a forward-looking measure. However, there is uncertainty in forecasting future revenues, especially for private firms and those firms that produce little or no cash flows.
Assumptions of multiples analysis
General assumptions of multiples analysis are that the other firms in the industry are comparable to the firm being valued. The market, on average, prices these firms correctly, but makes errors on the pricing of individual stocks. Exhibit 2 shows a selection of comparable firms, assuming that these firms have the same growth, risk and return as Cox Communications. There is also the assumption that financial fundamentals such as EBITDA are defined identically in all firms, with the same accounting methods and reporting periods. Exhibit 5 assumes a positive linear relationship between ROIC and the multiple Adjusted Enterprise Value/Average Invested Capital.
Regression analysis and traditional multiples analysis – similarities and differences
The two analyses both predict the underlying value of the firm. Also, both regression and multiples analyses reflect the past. The future value of the firm is obtained using historical inputs. Both analyses assume that firms in the same industry are comparable.
Traditional multiples analysis is more arithmetic in its approach. It is based on finding the average multiple among comparable firms, and then applying it to the firm’s fundamentals. How accurate the valuation is depends on the degree of comparability of the firms in the industry.
Regression analysis produces a statistical regression line of each comparable firm’s multiple against the fundamentals that affect the value of the multiple. The R-squared of the regression indicates how well that multiple works in the sector. After running the regression and establishing the multiple, then it is applied to fundamentals in order to arrive at a firm valuation price.
Interpretation of regression results
Martin’s regression results produced a higher share price of $50 (see A1), indicating that shares were currently undervalued and so Cox Communications does have growth potential.
Martin’s heavy reliance on the projection of the ROIC value is troubling. The 0.8% seems to be an arbitrary numerical projection. Any inaccuracy in this projection would result in a misleading outcome.
R-squared is 70%. The percentage variation in ROIC cannot be totally explained by the variation in Adj. EV/Ave. Invested Capital. However, it does tell us that ROICs are a substantive prediction of value. The linear relationship between ROIC and the multiple in the regression shows there is a strong relationship. Martin’s DCF analysis
Martin’s weighted cost of equity is 10.5%. We have calculated the cost of equity to be 13.61% (see A2) using a levered beta and a risk premium calculated over a longer historical period. Using the synthetic rating method, after-tax cost of debt is 4.51% (see A3), which is close to Martin’s estimate. Thus, with a WACC of 12.53% the stock price becomes a more conservative $41.70 (see A4).
Martin’s projected EBITDA growth of 16% seems high since her