Abstract
Capital Asset Pricing Model (CAPM) is the most widely used model in asset pricing. This model evaluates the asset return in relation to the market return and the sensitivity of the security to the market. However, the evidence supporting the CAPM is mixed. Alternatives to the CAPM in determining the expected rate of return on portfolios and stocks was introduced through the Arbitrage Pricing Theory and through the Intertemporal CAPM. The introduction of these more general models raised the following important question: how should the risk factors in a multifactor pricing model be specified? Since the multifactor model theory is not very explicit regarding the number or nature of the factors the selection of factors has, to a large extent, become an empirical issue. In the first and the second chapters, we conduct an exhaustive evaluation of multifactor asset pricing models based on observable factors. In the first chapter we find strong evidence that a multifactor pricing model should include the market excess return, the size- , and the value premium. In the second chapter we relax the assumption of normal distributed returns. Even if this new setup does not alter the selected factors, we found strong evidence of deviation from normality which makes our approach more appropriate. In contrast to the first two chapters, the third chapter takes the approach of using latent factors. Using data from the US market, 4 to 6 pervasive factor were generally found. Furthermore, the data speaks in favor of an approximate factor structure with time series dependence across assets. In the final chapter, we examine if a momentum strategy, is superior to a benchmark model once the effects of data-snooping have been accounted for. Data snooping occurs when a given set of data is used more than once for inference or model selection. The result shows that data-snooping bias can be very substantial. In this study, neglecting the problem would lead to very different conclusions. For the US data there is strong evidence of a momentum effect and we reject the hypothesis of weak market efficiency. For the Swedish data the results indicates that momentum strategies based on individual stocks generate positive and significant profits. Interestingly, a very weak or none at all, momentum effect can be found when stocks are sorted by size, book-to-market and industry.