Dai Fattori di Rischio a Factor Investing: Analisi dei modelli multifattoriali sul mercato Italiano ed Europeo

In this thesis I will evaluate the different asset pricing models on the Italian and European stock market, in order to see which one best explains the variation in stock returns. The starting point of asset pricing theoryy in literature is the capital asset pricing model (CAPM), where Sharpe (1964) and Lintner (1965) found that the only relevant risk in asset pricing is systematic risk. The only explanatory return employed by the CAPM to estimate asset returns is the market risk premium, i.e the market return in excess of the risk-free rate of return. However, the CAPM is criticized as being unrealistic and empirical research has identified several anomalies relating to pricing assets using the CAPM. Fama and French (1993) proposed a three-factor model for expected return. They added the factors firm size and book-to-market-equity ratio, as an extension to the CAPM, and found that the three-factor model does a better job in explaining stock returns. Carhart(1997) further studied the effect of momentum and he proposed a four factor model, by using Fama and French's 3-factor model plus the momentum factor.Adding a fourth factor would has improve the explanatory power of the model. In view of these findings, I will test all three models on the Italian and European stock market to see if there exists a preferred dominant model in explaining stock returns. An interesting result that we will see is the firm size effect obtained in the two markets. What we will observe in these two markets is the big firm effect, in contrast to the small firm effect observed by Fama and French (1996). Meanwhile the value effect (HML) observed is consistent with the findings in the U.S. market. Furthermore, I extend my analysis also to industry-specific factor models, analyzing the relationship between the three different models and stock returns in various economy. I will compare monthly returns of the various sectors, in the Italian indexes and European indexes to the monthly returns for the different factors of the models, to see whether the models work for a specific sector. I look separately at the relationship of excess industry returns to each of the three factors in the model ¿ excess market return, size factor and book-to-market equity factor. The excess market return is the most significant variable in explaining the average industry returns. What I am trying to see is if it would have a drop or an increase of the R^{2} when the models are applied to industry based portfolio returns. As we will see the degree in which industry returns are sensitive to changes in risk loadings is not the same for all industry portfolios. If this is indeed the case then studying the differences between industries could lead to a better understanding of the underlying true risk factors of the proxy variables used in the FF3 and FFC models, which in turn could lead to a better explanation of the variation in stock returns. The sector analysis will give mostly the same results for the two markets. What I found is that a local factor model is more capable of explaining the industry returns for sectors like Energy, Financial and Consumer Disce. than a Euro area version of this model. In this three cases Italy indexes outperform euro area indexes. But as we will see this is not the case for the other sectors.