Predicting stock market trends using random forests: A sample of the Zagreb stock exchange

Abstract

Stock market prediction is considered to be a challenging task for both investors and researchers, due to its profitability and intricate complexity. Highly accurate stock market predictive models are very often the basis for the construction of algorithms used in automated trading. In this paper, 5-days-ahead and 10-days-ahead predictive models are built using the random forests algorithm. The models are built on the historical data of the CROBEX index and on a few companies listed at the Zagreb Stock Exchange from various sectors. Several technical indicators, popular in quantitative analysis of stock markets, are selected as model inputs. The proposed method is empirically evaluated using stratified 10-fold cross-validation, achieving an average classification accuracy of 76.5% for 5-days-ahead models and 80.8% for 10-days-ahead models.

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