In: Finance
Why estimate of cost of equity using SML METHOD is different from cost of equity using Dividend growth model method?
DDM
The dividend discount model bases the current value of your stock shares on the total future value of their dividends. To value a stock using DDM, you combine a company's announced dividends with detailed financial projections to measure the dividend value over the next five years. Beyond that point, you have to make do with less detailed projections. Then you use the model's mathematical formula to discount the value of the future dividends to the present, giving a value for the stock now.
CAPM
Capital asset pricing models base their evaluation on a different set of factors: the expected rate of return, the risk-free rate of return, the market's average rate of return and the sensitivity of the investment to market conditions. Sensitivity is measured based on how past performance compared to the market. With CAPM you can compare your portfolio or your individual investments to the market and see if they come off as high risk or underperforming.
CAPM Vs. DDM
You can use CAPM and DDM together: most DDM formulas employ CAPM to help figure out how to discount future dividends and derive the current value. CAPM, however, is much more widely useful. DDM can't do anything for you if your investments aren't dividend-issuing stocks but you can apply CAPM to any sort of investment. Even on specific stocks, CAPM has an advantage because it looks at more factors than dividends alone.
Weaknesses
Neither DDM nor CAPM is a perfect investment tool, because both rely on assumptions about the future. Long-term financial forecasts are always challenging and DDM is especially so: to be accurate, you have to predict dividend policy five or 10 years down the road. CAPM also makes assumptions. For example, when it measures the relationship between returns and risks, it ignores unsystematic risks -- risks that only affect stocks in one particular industry. If you have a highly specialized portfolio, CAPM may not be as effective a predictor.