Predicting Market Direction With Deep Learning: An Application on E-7 Country Stock Markets

Nazif Ayyıldız
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Nazif Ayyıldız: Harran University

Journal of Finance Letters (Maliye ve Finans Yazıları), 2025, vol. 40, issue 123, 92-111

Abstract: This study aims to examine the prediction performance of the deep learning method on the stock indices of e-7 countries, known as emerging market economies. In this context, the daily movement directions of the stock indices of ipc (mexico), sse (china), bist 100 (turkey), rts (russia), bovespa (brazil), idx (indonesia), and nifty 50 (india) were predicted using the h2o deep learning model. Technical indicators calculated based on the daily closing prices between 01.01.2015 and 31.12.2024 were used as inputs for the model. The data was split into 80% training and 20% test sets during the prediction process. The calculated accuracy rates were 88.47% for the ipc index, 78.13% for sse, 77.29% for bist 100, 76.05% for rts, 75.81% for bovespa, 75.05% for idx, and 74.34% for nifty 50. The findings demonstrate that deep learning methods can predict stock index movements with a certain level of accuracy.

Keywords: Deep Learning; H2O Deep Learning Model; Classification; Developing Countries (search for similar items in EconPapers)
JEL-codes: C38 C55 G15 G17 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:acc:malfin:v:40:y:2025:i:123:p:92-111

DOI: 10.33203/mfy.1442589

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