Asset pricing is one of the most crucial aspects of finance. For many decades the economists have been struggling to find a model which would best describe the returns of the stocks, however, there is still no consensus on which model to use. I was able to test the five-factor asset pricing model in Russian stock exchange for the first time ever.

In this paper I took thirty Russian stocks of the non-financial companies with the highest capitalization and tested different asset-pricing models. I was able to construct all the factors by myself, because these calculations are not published for the Russian stocks. The traditional CAPM model yields unstable and poor results. The average explaining power of the CAPM model is equal to 0.51 for the size-value portfolios. Moreover, the results of the model violated one of its main properties - higher betas did not bring higher returns.

Next I tested Fama-French three-factor model, adding the size factor and the value factor to the risk-premium factor from the CAPM model. The stocks were grouped into six portfolios by size and value, and the three-factor model explained the excess returns much better than the CAPM model. On average the explaining power was equal to 0.78, and the factors were significant.

The four-factor Fama-French model added the momentum factor, and for size-value portfolios, the model worked better than the three-factor model. The average explaining power was equal to 0.80, but the momentum effect was insignificant in half of the portfolios. When the stocks were grouped by size and momentum, the momentum effect appeared, but the value effect disappeared, and on average the model had low explaining power.

The five-factor Fama-French model adds profitability and investment factors to the three-factor model. When I tested the size-value portfolios, the profitability and investment effects were difficult to find, however, the explaining power of the model improved compared to the four-factor model and on average was equal to 0.81. When I tried to find the profitability effect and the investment effect, I regrouped the stocks by size-profitability and size-investment types, and the corresponding effects appeared, however, the value effect disappeared from the model. The size-value portfolios again displayed the highest explaining factor.

There were common patterns in all models - the small stocks experienced bigger effects for any factor. Also, the small stocks always have a positive size effect and the big stocks have a negative size effect. Another common pattern is that the value effect decreases with the stocks book-to-market ratio and even becomes negative for the stocks with low BE/ME. The momentum factor had a positive effect for the winner stocks and a negative effect for loser stocks, which is in line with common sense that the stocks with high returns in the last eleven month will have higher returns than the stocks with low returns in the last eleven months. The profitability effect was negative for the stocks with weak profitability and positive for the stocks with robust profitability. Again, this makes sense that the stocks of the companies with higher profit earn more. The investment effect was negative for the firms with aggressive investment policy and positive for the firms with conservative investment policy. As this effect is measured in the short period, it is logical that when the company decided to invest into assets, it suffered losses in the beginning and the stocks had lower returns.

There are drawbacks to the methodology of these tests. First of all, the sample of thirty biggest stocks does not capture the entire market, so the results do not represent the full population. Moreover, the market is skewed very hardly, so that the real median of market equity would be much lower than in my sample. In the full population, these top30 stocks would all belong to the group of “big” stocks. In all of the models I found evidence of heteroscedasticity, so it is important to take this effect into account and use Newey-West standard errors. In the four-factor model there is a threat of multicollinearity, because the model is autocorrelated. In the future this research could be extended by splitting the stocks into more groups, however, one should increase the number of stocks to be sure that each group contains enough of them. It is also possible to increase the time period, however, before 2008 there was a lack of stocks and a lack of financial information in the market. So the period is better extended by adding the latest returns.

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