In this project we will analyze risk premia and the cost of capital using a novel approach based on valuation methods for companies. Traditionally, costs of capital have been estimated using asset pricing models where risk premia are estimated from historical stock market data. However, these estimates are biased if the risk premium is not constant over time. E.g., if the risk premium falls, then stock prices increase and ex post realized returns are higher than ex ante expected returns. Our approach is based on more recent research that estimates the cost of capital and risk premia from company valuation models. The central idea is to equate model estimates with market prices in order to infer the equity risk premium. We are going to develop and test a new valuation method, which will then be used for the estimation of the costs of capital. This new method is based on the traditional DCF model and incorporates the predictions from statistical forecasting models for the key value drivers. These include the sales margin, sales growth, and asset turnover. We also want to show that this improved DCF model is superior to the residual income model that has been favored in recent empirical research.
We will also use these cost of capital estimates in order to determine industry, country and time components of the market risk premium. We will then explore the relation of these components of the risk premium with explanatory factors (market beta, Fama-French factors). Moreover, we are going to apply our risk premium estimates and company valuation methods to the analysis of corporate transactions, such as IPOs, mergers and acquisitions, or the issuance of non-voting shares.