Stock price forecasting under post-pandemic and normal market conditions using the generalized wiener process
DOI:
https://doi.org/10.55749/rmm.v1i1.175Keywords:
Financial forcesting, Generalized weiner process, Indonesian mining stocks, MAPE, Monte Carlo simulation, Stock price forecasting, Post-pandemic marketAbstract
The Covid-19 pandemic significantly altered stock market behavior and increased public participation in short-term trading and investment activities. However, limited analytical knowledge among new investors may increase the risk of inaccurate trading decisions, particularly during volatile market periods. This study evaluates the forecasting performance of the generalized Wiener process in predicting stock prices under two different market regimes: the post-pandemic New Normal period in 2021 and normal market conditions in 2023. Daily adjusted closing prices of two Indonesian mining companies, PT Aneka Tambang Tbk (ANTM) and PT Timah Tbk (TINS), were used as case studies. Historical price data from January to December 2020 were used to forecast stock prices for January–February 2021, while data from January to December 2022 were used to forecast January–February 2023 prices. Two forecasting schemes were compared: forecasts based on real-time reference data and forecasts based on previous predicted values. Forecast accuracy was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that the generalized Wiener process produced more accurate forecasts under normal market conditions than during the post-pandemic period. Real-time reference data consistently generated lower forecasting errors than previous-prediction reference data. For TINS, the MAPE decreased from 5.51% in the 2021 post-pandemic period to 2.73% in the 2023 normal period using real-time reference data. In contrast, using previous predicted values produced substantially higher errors, particularly in 2021, with a MAPE of 27.28%. These findings indicate that the generalized Wiener process is reliable for short-term stock price forecasting when supported by real-time reference data, even under volatile market conditions. The study contributes to financial forecasting by demonstrating the sensitivity of stochastic stock price models to market regimes and reference-data selection.
References
1. Bai, C., Zhu, Q., and Sarkis, J. (2024). Do Blockchain Capabilities Help Overcome Supply and Operational Risks: Insights from Firm Market Returns during COVID-19, Omega, Vol. 126, 103049. Doi: 10.1016/j.omega.2024.103049
2. Yang, C., and Wu, H. (2021). Investor Sentiment with Information Shock in the Stock Market, Emerging Markets Finance and Trade, Vol. 57, 510–524. Doi: 10.1080/1540496X.2019.1593136
3. Kalaycı, B., Purutçuoğlu, V., and Weber, G. W. (2022). Operation Research in Neuroscience: A Recent Perspective of Operation Research Application in Finance, Operations Research, 170–190.
4. Zhou, Z., Gao, M., Xiao, H., Wang, R., and Liu, W. (2021). Big Data and Portfolio Optimization: A Novel Approach Integrating DEA with Multiple Data Sources, Omega, Vol. 104, 102479. Doi: 10.1016/j.omega.2021.102479
5. Chu, L., He, X. Z., Li, K., and Tu, J. (2022). Investor Sentiment and Paradigm Shifts in Equity Return Forecasting, Management Science, Vol. 68, No. 6, 4301–4325. Doi : 10.1287/mnsc.2020.3834
6. He, Y., Qu, L., Wei, R., and Zhao, X. (2022). Media-Based Investor Sentiment and Stock Returns: A Textual Analysis Based on Newspapers, Applied Economics, Vol. 54, 774–792. Doi: 10.1080/00036846.2021.1966369
7. Liu, Y. J., Yang, G. S., and Zhang, W. G. (2024). A Novel Regret-Rejoice Cross-Efficiency Approach for Energy Stock Portfolio Optimization, Omega, Vol. 126, 103051. Doi: 10.1016/j.omega.2024.103051
8. Cerqueti, R., Cesarone, F., and Ficcadenti, V. (2024). Portfolio Decision Analysis for Pandemic Sentiment Assessment Based on Finance and Web Queries, Annals of Operations Research, 1–31.
9. Baker, M., and Wurgler, J. (2006). Investor Sentiment and the Cross-Section of Stock Returns, Journal of Finance, Vol. 61, 1645–1680. Doi : 10.1111/j.1540-6261.2006.00885.x
10. Zhou, Y., Fan, J. Q., and Xue, L. R. (2024). How Much Can Machines Learn Finance from Chinese Text Data?, Management Science.
11. Li, H., and Wu, D. (2024). Online Investor Attention and Firm Restructuring Performance: Insights from an Event-Based DEA-Tobit Model, Omega, Vol. 122, 102967. Doi: 10.1016/j.omega.2023.102967
12. Dechow, P., Lawrence, A., Luo, M., and Stamenov, V. (2024). Media Attention and Event-Based Grouping of Stocks: An Examination of Stocks Hyped by Media Outlets as Benefiting from the Olympics, Management Science.
13. Kehinde, T. O., Chan, F. T. S., and Chung, S. H. (2023). Scientometric Review and Analysis of Recent Approaches to Stock Market Forecasting: Two Decades Survey, Expert Systems with Applications, Vol. 213, 119299. Doi: 10.1016/j.eswa.2022.119299
14. Pai, P. F., and Lin, C. S. (2005). A Hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting, Omega, Vol. 33, 497–505. Doi: 10.1016/j.omega.2004.07.024
15. Wang, J. J., Wang, J. Z., Zhang, Z. G., and Guo, S. P. (2012). Stock Index Forecasting Based on a Hybrid Model, Omega, Vol. 40, 758–766. Doi: 10.1016/j.omega.2011.07.008
16. Gao, J., Mao, Y. S., Xu, Z. S., and Luo, Q. L. (2024). Quantitative Investment Decisions Based on Machine Learning and Investor Attention Analysis, Technological and Economic Development of Economy, Vol. 30, No. 3, 527–561.
17. Li, Y., Shen, D., Wang, P., and Zhang, W. (2020). Does Intraday Time-Series Momentum Exist in Chinese Stock Index Futures Market?, Finance Research Letters, Vol. 35, 101292. Doi : 10.1016/j.frl.2019.09.007
18. Olgun, O., and Yetkiner, I. H. (2011). Determination of Optimal Hedging Strategy for Index Futures: Evidence from Turkey, Emerging Markets Finance and Trade, Vol. 47, 68–79. Doi : 10.2753/REE1540-496X470604
19. Makarov, I., and Schoar, A. (2020). Trading and Arbitrage in Cryptocurrency Markets, Journal of Financial Economics, Vol. 135, 293–319. Doi : 10.1016/j.jfineco.2019.07.001
20 Goldstein, M., Kwan, A., and Philip, R. (2022). High-Frequency Trading Strategies, Management Science, Vol. 69, No. 8, 4413–4434.
21. Aggarwal, P., and K. K. (2023). Analysis of the Impact of COVID-19 on the Stock Market and Capability of Investing Strategies, AIP Conference Proceedings, Vol. 2782, 020112. Doi : 10.1063/5.0154174
22. Marshall, B. R., Nguyen, N. H., and Visaltanachoti, N. (2017). Time Series Momentum and Moving Average Trading Rules, Quantitative Finance, Vol. 17, 405–421. Doi : 10.1080/14697688.2016.1205209
23. Nițoi, M., and Pochea, M. M. (2020). Time-Varying Dependence in European Equity Markets: A Contagion and Investor Sentiment Driven Analysis, Economic Modelling, Vol. 86, 133–147. Doi : 10.1016/j.econmod.2019.06.007
24. Baker, M., Wurgler, J., and Yuan, Y. (2009). Global, Local, and Contagious Investor Sentiment, Journal of Financial Economics, Vol. 104, 272–287.
25. Jing, N., Wu, Z., and Wang, H. (2021). A Hybrid Model Integrating Deep Learning with Investor Sentiment Analysis for Stock Price Prediction, Expert Systems with Applications, Vol. 178, 115019. Doi : 10.1016/j.eswa.2021.115019
26. Li, H., and Wu, D. (2024). Online Investor Attention and Firm Restructuring Performance: Insights from an Event-Based DEA-Tobit Model, Omega, Vol. 122, 102967. Doi: 10.1016/j.omega.2023.102967
27. Schmeling, M. (2009). Investor Sentiment and Stock Returns: Some International Evidence, Journal of Empirical Finance, Vol. 16, 394–408. Doi: 10.1016/j.jempfin.2009.01.002
28. Tetlock, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market, Journal of Finance, Vol. 62, 1139–1168. Doi: 10.1111/j.1540-6261.2007.01232.x
29. Chen, R., Wang, S., Jin, C., Yu, J., Zhang, X., and Zhang, S. (2023). Comovements between Multidimensional Investor Sentiment and Returns on Internet Financial Products, International Review of Financial Analysis, Vol. 85, 102433. Doi: 10.1016/j.irfa.2022.102433
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Irsal Alfian, Radhiah, Rini Oktavia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
