A Comparative study of Average-Based FTSMC, GBM, and LSTM for JCI Forecasting
Abstract
Stocks are high-risk investment instruments, so a model is needed to assist investors in decision-making. The Jakarta Composite Index (JCI) is a key indicator that measures the performance of all stocks on the Indonesian stock exchange. This study compares the performance of the Average-Based FTSMC, GBM, and LSTM models in predicting JCI movements. The research data consists of daily stock index values from June 1, 2024, to June 29, 2026. The models were compared using the evaluation metrics MAPE, RMSE, and MAE. The results show that the Average-Based FTSMC model provides the highest accuracy (MAPE: 0.925%, RMSE: 86.753, and MAE: 65.162). These findings indicate that the fuzzy time series approach is more adaptive to JCI fluctuations than both classical stochastic models and deep learning models. This study is limited to historical data using a single input variable; therefore, generalizing the results to market conditions requires further investigation.
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M. Chang and Y. Li, “Impact of capital market volatility on economic growth - An analysis based on stochastic volatility model,” Heliyon, vol. 10, no. 3, pp. 1–16, 2024, doi: 10.1016/j.heliyon.2024.e25679.
X. Ji, J. Wang, and Z. Yan, “A stock price prediction method based on deep learning technology,” Int. J. Crowd Sci., vol. 5, no. 1, pp. 55–72, 2021, doi: 10.1108/IJCS-05-2020-0012.
M. Hamidah and R. Putra, “Factors that encourage investors to decide to invest in shares in the capital market for the 2020-2023 period (Covid-19 pandemic): Systematic Literature Review,” Int. J. Soc. Sci. Hum. Res., vol. 7, no. 05, pp. 2604–2611, 2024, doi: 10.47191/ijsshr/v7-i05-08.
R. Bhowmik and S. Wang, “Stock market volatility and return analysis : A systematic literature review,” Entropy, vol. 22, no. 552, pp. 1–18, 2020, doi: 10.3390/e22050522.
W. Lu, J. Li, J. Wang, and L. Qin, “A CNN-BiLSTM-AM method for stock price prediction,” Neural Comput. Appl., vol. 33, no. 10, pp. 4741–4753, 2021, doi: 10.1007/s00521-020-05532-z.
Fuad and I. Yuliadi, “Determinants of the composite stock price index (IHSG) on the Indonesia stock exchange,” J. Econ. Res. Soc. Sci., vol. 5, no. 1, pp. 27–41, 2021, doi: 10.18196/jers.
P. Sia, C. Puah, C.-M. Leong, K.-J. Yii, and M. M.-J. Tang, “Does inflation or interest rate matter to Indonesian stock prices ? An asymmetric approach,” Emerald, vol. 27, no. 1, pp. 72–86, 2024, doi: 10.1108/JED-07-2024-0239.
N. N. Marpaung, I. Rini, and D. Pangestuti, “Macroeconomic factors and Jakarta Stock Exchange : A comparative analysis pre- and until the COVID-19 pandemic,” SAGE, vol. 14, no. 2, pp. 1–15, 2024, doi: 10.1177/21582440241247894.
V. Bratian, A.-M. Acu, D. M. Mihaiu, and R.-A. Serban, “Geometric brownian motion ( GBM ) of stock indexes and financial market uncertainty in the context of non-crisis and financial crisis scenarios,” Mathematics, vol. 10, no. 3, pp. 1–23, 2022, doi: 10.3390/math10030309.
A. K. Sinha, “The reliability of geometric Brownian motion forecasts of S&P500 index values,” J. Forecast., vol. 40, no. 8, pp. 1444–1462, 2021, doi: 10.1002/for.2775.
A. Sinha, “Daily and Weekly Geometric Brownian Motion Stock Index Forecasts,” Risk Financ. Manag., vol. 17, no. 10, pp. 1–22, 2024, doi: 10.3390/jrfm17100434.
A. Illa et al., “Performance analysis of ARIMA , LSTM , and hybrid ARIMA-LSTM in forecasting the composite stock price index,” Cauchy, vol. 10, no. 2, pp. 588–604, 2025, doi: 10.18860/cauchy.v10i2.33379.
J. K. Mutinda and A. K. Langat, “Stock price prediction using combined GARCH-AI models,” Sci. African, vol. 26, pp. 1–14, 2024, doi: 10.1016/j.sciaf.2024.e02374.
H. Guan, H. Jie, S. Guan, and A. Zhao, “A novel fuzzy-Markov forecasting model for stock fluctuation time series,” Evol. Intell., vol. 13, no. 2, pp. 133–145, 2020, doi: 10.1007/s12065-019-00328-0.
S. Aulia, I. W. Mangku, and I. G. P. Purnaba, “Perbandingan kinerja model FTS-Markov chain dan geometric brownian motion dalam memprediksi harga saham BBRI,” Milang, vol. 21, no. 2, pp. 163–177, 2025, doi: 10.29244/milang.21.2.163-177.
K. Ramadani and D. Devianto, “The forecasting model of Bitcoin price with fuzzy time series Markov chain and chen logical method,” AIP Conf. Proc., vol. 2296, no. 1, p. 020095, 2023, doi: 10.1063/5.0032178.
X. Wen and W. Li, “Time Series Prediction Based on LSTM-Attention-LSTM Model,” IEEE Access, vol. 11, pp. 48322–48331, 2023, doi: 10.1109/ACCESS.2023.3276628.
B. Sabzipour et al., “Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment,” J. Hydrol., vol. 627, 2023, doi: 10.1016/j.jhydrol.2023.130380.
A. Yunita et al., “Performance analysis of neural network architectures for time series forecasting : A comparative study of RNN , LSTM , GRU , and hybrid models,” MethodsX, vol. 15, p. 103462, 2025, doi: 10.1016/j.mex.2025.103462.
V. Deswal and D. Kumar, “Hyperparameter tuning of the LSTM model for stock price prediction,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 4, pp. 705–712, 2024.
R. A. Ferdiansyah and E. Utami, “Komparasi model transformer dan LSTM dalam prediksi Harga Saham Blue Chip di BEI,” JNANALOKA, vol. 7, no. 1, pp. 51–59, 2024, doi: 10.36802/jnanaloka.2026.v7-no01-%25p.
R. C. Tsaur, “A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan and us Dollar,” Int. J. Innov. Comput. Inf. Control, vol. 8, no. 7, pp. 4931–4942, 2012.
S. Hariyanto, Y. D. Sumanto, S. Khabibah, and Zaenurrohman, “Average-based fuzzy time series Markov chain based on frequency density partitioning,” J. Appl. Math., vol. 1, pp. 1–9, 2023, doi: 10.1155/2023/9319883.
K. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series,” Fuzzy Sets Syst., vol. 123, no. 3, pp. 387–394, 2001, doi: 10.1016/S0165-0114(00)00057-9.
A. Zakiah, E. Sulistianingsih, and N. Satyahadewi, “Geometric brownian motion with jump diffusion and value at risk analysis of PT Bank Negara,” Barekeng, vol. 19, no. 1, pp. 617–628, 2025, doi: 10.30598/barekengvol19iss1pp617-628.
R. S. Tsay, Analysis of financial time series, 2nd ed. Canada: John Wiley & Sons, Inc, 2010. doi: 10.1002/9780470644560.
J. C. Hull, Options, Futures, and Other Derivatives, 11th ed. United Kingdom: Pearson Education, 2022.
M. Alhagyan, “The effects of incorporating memory and stochastic volatility into GBM to forecast exchange rates of Euro,” Alexandria Eng. J., vol. 61, no. 12, pp. 9601–9608, 2022, doi: 10.1016/j.aej.2022.03.036.
I. Malashin, V. Tynchenko, A. Gantimurov, V. Nelyub, and A. Borodulin, “Applications of long short-term memory (LSTM) networks in polymeric sciences: A review,” Polymers (Basel)., vol. 16, no. 18, pp. 1–44, 2024, doi: 10.3390/polym16182607.
M. Krichen and A. Mihoub, “Long short-term memory networks: A comprehensive survey,” AI, vol. 6, no. 9, pp. 1–21, 2025, doi: 10.3390/ai6090215.
A. Lawi, H. Mesra, and S. Amir, “Implementation of long short‑term memory and gated recurrent units on grouped time‑series data to predict stock prices accurately,” J. Big Data, vol. 9, pp. 1–19, 2022, doi: 10.1186/s40537-022-00597-0.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE , MAE , MAPE , MSE and RMSE in regression analysis evaluation,” PeerJ Comput. Sci., vol. 7, pp. 1–24, 2021, doi: 10.7717/peerj-cs.623.
C. D. Lewis, Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. London: Butterworth Scientific, 1982.
R. J. C. Chen, P. Bloomfield, and J. S. Fu, “An evaluation of alternative forecasting methods to recreation visitation,” J. Leis. Res., vol. 35, no. 4, pp. 441–454, 2003, doi: 10.1080/00222216.2003.11950005.
DOI: http://dx.doi.org/10.30829/zero.v10i2.31020
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