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Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 8 Sayı: 4

Forecasting Mutual Fund Prices on TEFAS Using LightGBM

Oktay Olmez(Afiniti)0000-0002-9130-0038
Pages
1134–1139
Publication Date
2025-07-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The research outlines the methodology for data preparation, feature implementation, data splitting, model configuration, training, and evaluation, including case studies to demonstrate the practical application and results

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper investigates the application of the Light Gradient Boosting Machine (LightGBM) algorithm for predicting the prices of mutual funds traded on the Türkiye Electronic Fund Trading Platform (TEFAS).
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Key Findings & Data:Given the increasing importance of mutual funds as an investment vehicle in Türkiye, this study explores the effectiveness of a state-of-the-art machine learning approach.
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Conclusion & Impact:The research outlines the methodology for data preparation, feature implementation, data splitting, model configuration, training, and evaluation, including case studies to demonstrate the practical application and results
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Machine learning · LightGBM · Tefas

Abstract

This paper investigates the application of the Light Gradient Boosting Machine (LightGBM) algorithm for predicting the prices of mutual funds traded on the Türkiye Electronic Fund Trading Platform (TEFAS). Given the increasing importance of mutual funds as an investment vehicle in Türkiye, this study explores the effectiveness of a state-of-the-art machine learning approach. Utilizing historical data from TEFAS, the LightGBM model is employed to capture complex patterns and non-linear relationships within the financial time series data. The research outlines the methodology for data preparation, feature implementation, data splitting, model configuration, training, and evaluation, including case studies to demonstrate the practical application and results

Özet

This paper investigates the application of the Light Gradient Boosting Machine (LightGBM) algorithm for predicting the prices of mutual funds traded on the Türkiye Electronic Fund Trading Platform (TEFAS). Given the increasing importance of mutual funds as an investment vehicle in Türkiye, this study explores the effectiveness of a state-of-the-art machine learning approach. Utilizing historical data from TEFAS, the LightGBM model is employed to capture complex patterns and non-linear relationships within the financial time series data. The research outlines the methodology for data preparation, feature implementation, data splitting, model configuration, training, and evaluation, including case studies to demonstrate the practical application and results

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Cite This Article

Olmez, O. (2025). Forecasting Mutual Fund Prices on TEFAS Using LightGBM. Black Sea Journal of Engineering and Science, Cilt: 8 Sayı: 4, 1134–1139. https://doi.org/10.34248/bsengineering.1691043

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References (5)

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