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
Keywords
References
- 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.
- 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.
- 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.
- Sun X, Liu M, Sima Z. 2020. A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Res Lett, 32: 101084.
- 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.
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
Keywords
References
- 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.
- 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.
- 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.
- Sun X, Liu M, Sima Z. 2020. A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Res Lett, 32: 101084.
- 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.
Details
| Primary Language | English |
|---|---|
| Subjects | Financial Mathematics |
| Journal Section | Research Articles |
| Authors | |
| Early Pub Date | July 9, 2025 |
| Publication Date | July 15, 2025 |
| Submission Date | May 4, 2025 |
| Acceptance Date | June 11, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 4 |
