Predicting stock market indices movements

This paper examines the extent to which the daily movements of three large emerging markets stock indices are predictable. Lagged technical indicators are used as explanatory variables. In the analysis we employed seven classification techniques and assessed the discriminatory power of the classifiers through the area under the receiver operating characteristic (ROC) curve. The results show that the daily movements of the three indices are better predictable than random. After taking into account the bias induced by non-synchronous price quotations, a trading system with break-even costs is simulated. The non-random classifiers yield returns above those of both random walk and contrarian investment strategies. No inefficiency is found due to the fact that relatively low break-even transaction costs are enough to eliminate the sources of trading profits.