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Predicting daily stock prices in Mongolia using time series models
Journal of the Korean Data & Information Science Society 2024;35:285-95
Published online March 31, 2024;
© 2024 Korean Data and Information Science Society.

Solongo Erdenebat1 · Cheolyong Park2

12Department of Statistics, Keimyung University
Correspondence to: 1 Master student, Department of Statistics, Keimyung University, Daegu 42601, Korea.
2 Professor, Department of Statistics, Keimyung University, Daegu 42601, Korea. E-mail:
Received January 31, 2024; Revised February 27, 2024; Accepted March 9, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Unveiling the dynamics of Mongolia’s stock market, this research delves into four critical companies in different sectors within the TOP 20 index in the Mongolian Stock Exchange, based on parameters such as market capitalization, average daily turnover, and concentration status. An in-depth analysis utilizes four diverse forecasting models - ARIMA (autoregressive integrated moving average), Prophet, NeuralProphet, and one of the deep learning model LSTM (Long short-term memory) - to predict stock prices across various terms (5, 22, and 66 business days) horizons. Employing the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics, we compare the models’ effectiveness, revealing their strengths and weaknesses for different prediction windows. The results indicate a significant challenge in achieving precise predictions, yet they also reveal a pattern: the ARIMA model performed better than others for short-term predictions, while the NeuralProphet model outperformed medium-term predictions in most of the companies examined. This study enhances our understanding of the Mongolian stock market and furnishes valuable insights for investors, traders, and analysts seeking optimal forecasting tools tailored to their specific needs and time frames.
Keywords : ARIMA, LSTM, Mongolian stock exchange, NeuralProphet, Prophet