Search

The research presented in this article analyzed the performance of the random forest algorithm in predicting the future return of the main indices of the largest stock exchanges in the world, through historical trading prices. A sample composed of the daily quotes of 35 indices of the largest stock exchanges in the world from 2001 to 2019 was used. In addition to the random forest algorithm, models were estimated, based on the decision tree algorithm and using the logistic regression technique. The models were estimated considering maximum and closing prices, as well as the complete period and its division into sub-periods. The results indicated that the performances of the estimated models were superior to the market average, and the random forest presented the best results. All models trained on the maximum prices of the indices performed better than those trained on closing prices. In addition, the subperiod models performed better for the random forest. The efficiency of markets in the weak form has been questioned in the contemporary context of the rise of the use of artificial intelligence (AI) algorithms for forecasting in finance. The study is relevant as it contributes to the literature on the use of AI algorithms in forecasting asset prices in the financial market. The main indices of the largest stock exchanges in the world were analyzed, generating general subsidies that can help guide future research in the area. [ABSTRACT FROM AUTHOR]