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Dergiler / 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
Sayfa
1134–1139
Yayın Tarihi
2025-07-15
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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 Maddede Bu Makale

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Amaç & Hipotez: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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Temel Bulgular & Veri: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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Sonuç & Katkı: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
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Machine learning · LightGBM · Tefas

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

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

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Bu Makaleye Atıf Yap

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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Kaynakça (5)

  1. Guennioui O, Chiadmi D, Amghar M. 2024. Global stock price forecasting during a period of market stress using LightGBM. Int J Comput Digit Syst, 15.1: 19-27.
  2. Guo Y, Li Y, Xu Y. 2021. Study on the application of LSTM-LightGBM Model in stock rise and fall prediction. MATEC Web of Conferences, 336: 05011.
  3. Hartanto A, Kholik YN, Pristyanto Y. 2023. Stock price time series data forecasting using the light gradient boosting machine (LightGBM) model. JOIV: Int J Inform Visualization, 7.4: 2270-2279.
  4. Sun X, Liu M, Sima Z. 2020. A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Res Lett, 32: 101084.
  5. Tian L, Feng L, Yang L, Guo Y. 2022. Stock price prediction based on LSTM and LightGBM hybrid model. J. Supercomput, 78.9: 11768-11793.