Ari D.Alagoz B.B.2024-08-042024-08-0420219781665428705https://doi.org/10.1109/ICIT52682.2021.9491652https://hdl.handle.net/11616/92219Umniah and UWallet2021 International Conference on Information Technology, ICIT 2021 -- 14 July 2021 through 15 July 2021 -- 170653A behavioral modeling of financial markets based on daily data is not an easy problem for machine learning algorithms. The social and physiological factors can take effect on market data and result in significant uncertainty in data. This study demonstrates an implementation of tree-based genetic programming (GP) to develop a mathematical model of stock market from the daily stock data of other stock markets to observe relations between global market trends and to consider this effect in market prediction problems. To obtain a prediction model of Istanbul Stock Exchange 100 Index (ISE100), numerical data from ISE100 and seven other international stock market indices are used to produce GP models that can estimate daily price changes in ISE100 according to daily change in other international stock market indices. To reduce negative effects of the data uncertainty on the GP modeling, ensemble average GP modeling performances are investigated and the results are reported for future research direction suggestions. © 2021 IEEE.eninfo:eu-repo/semantics/closedAccessaverage ensemble learningfinancial market data modelingGenetic programmingstock marketstree-based genetic programmingConference Object38238610.1109/ICIT52682.2021.94916522-s2.0-85112189932N/A