Abstract: Several predictive regressions with variables such as dividend to price ratio used as predictors are by now well-established for financial returns in developed markets. Due to the autocorrelation and heterogeneity of the data, the standard approaches to the analysis of statistical significance of predictive regressors and their coefficients based on independent and identically distributed standard errors are not directly applicable in the case of stock returns. At the same time, while there are quite many researches devoted to developed markets in the field of return predictability, there are quite a few for emerging ones based only on conventional methods. This research is focusing on application of several methods to the Russian market data, providing inference about the predictability of stock returns. The methods include widely used heteroskedasticity and autocorrelation consistent (HAC) standard errors, t-statistics robust inference method and recently developed test relying on nonparametric correction of volatility.