Predicting Stock Market Trends Using Random Forests: A Sample of the Zagreb Stock Exchange (CROSBI ID 624448)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Manojlović, Teo ; Štajduhar, Ivan
engleski
Predicting Stock Market Trends Using Random Forests: A Sample of the Zagreb Stock Exchange
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 crossvalidation, achieving an average classification accuracy of 76.5% for 5-days- ahead models and 80.8% for 10-daysahead models.
Stock market; Trend prediction; Technical indicators; Random forests; Machine learning
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Podaci o prilogu
1436-1440.
2015.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of MIPRO CIS - Intelligent Systems Conference, 2015
Biljanović, Petar
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO
978-953-233-083-0
1847-3938
Podaci o skupu
MIPRO 2015
predavanje
25.05.2015-29.05.2015
Opatija, Hrvatska