IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 5 2024
DOI: 10.56201/ijcsmt.v10.no5.2024.pg47.80

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Data Mining Framework for Nigerian Stock Market Price Prediction

ILIYA, Samuel Yohanna, Dr Murtala Muhammad, Dr. Yusuf Musa Malgwi, Jean Raphael Biyyaya

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Abstract

The stock market has become an integral part of the modern financial system, playing a significant role in shaping the global economy. The stock market prices serve as a key indicator of market sentiment, economic performance and investor confidence, with a growing demand for accurate stock market predictions. This research explores a data mining framework for Nigerian stock market predictions using Decision Tree, Support Vector Regression, and Artificial Neural Network techniques. Data from Dangote Sugar Refinery is used to create new variables for models. The models are evaluated using MAPE and MSE, and show promising outcomes. The combined models have the highest accuracy at 85%, capturing both short-term and long-term trends effectively. The study recommends further development of advanced sentiment analysis and dynamic model adaptation for better implementation.

keywords:

Stock market prediction, Data mining framework, Decision Tree, Support Vector

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