Forecasting the Philippine Stock Exchange Index Using Time Series Modeling Techniques

Forecasting stock market indices like the Philippine Stock Exchange Index (PSEi) is crucial for investors, economists, and policymakers in understanding market behavior and making strategic decisions. This study aimed to examine the historical trend of the PSEi from 2004 to 2023 and determine which among various time series models best predicts its value in 2025. Specifically, the study evaluated polynomial regressions, logarithmic, power series, moving averages, exponential smoothing, and autoregressive models to identify the most suitable forecasting approach. A quantitative research design was employed using secondary data collected from Yahoo Finance and Investing.com. Monthly PSEi closing prices were compiled, averaged annually, and analyzed using Microsoft Excel and the Data Analysis Toolpak, which enabled trendline generation, smoothing applications, and lag-based regression modeling. The results showed that the PSEi experienced an overall upward but volatile movement over two decades, with notable dips during global crises. Among the models tested, the quintic polynomial regression achieved the highest explanatory power, but its predicted value of 15,872.78 suggests potential overfitting. Moving average models effectively smoothed short-term fluctuations but tended to underpredict future growth, while autoregressive models captured significant temporal dependencies, with higher-order lags revealing delayed market responses. The study concludes that while polynomial and curve-fitting models can capture nonlinear behavior, they should be used cautiously due to overfitting risks. It is recommended that future forecasting efforts explore hybrid models that combine polynomial trends, autoregressive structures, and smoothing techniques for improved accuracy.

Autoregressive models, forecast accuracy, polynomial regression, stock market trends, time-series smoothing

1. Babangida, J. S. (2023). Nonlinearity in emerging market indices: A comprehensive study of stock exchange market dynamics. Applied Econometrics, 72, 23-37. https://doi.org/10.22394/1993-7601-2023-72-23-37 [Google Scholar] [Crossref]

2. Berenson, M. L., Levine, D. M., Szabat, K. A., & Stephan, D. F. (2024). Basic Business Statistics: Concepts and Applications (15th ed.) [eTextbook]. Pearson. [Google Scholar] [Crossref]

3. Botchkarev, A. (2015). Assessing Excel VBA suitability for Monte Carlo simulation. arXiv. https://doi.org/10.48550/arXiv.1503.08376 [Google Scholar] [Crossref]

4. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley. [Google Scholar] [Crossref]

5. Brooks, C. (2023). Introductory econometrics for finance (4th ed.). Cambridge University Press. [Google Scholar] [Crossref]

6. Caporin, M., & Storti, G. (2020). Financial time series: Methods and models. Journal of Risk and Financial Management, 13(5), 86. https://doi.org/10.3390/jrfm13050086 [Google Scholar] [Crossref]

7. Clayton, R., & Schmidt, B. (2017). Are capital market parameters estimated from Yahoo Finance and Nasdaq data the same? Banking & Finance Review, 9(1), 27--46. https://digitalcommons.jsu.edu/cgi/viewcontent.cgi?article=1155&context=fac_res [Google Scholar] [Crossref]

8. Corpuz, J. A. M. (2025, March 7). Prospects and challenges for PHL stock market in 2025. BusinessWorld. https://www.bworldonline.com/special-features/2025/03/07/657878/prospects-and-challenges-for-phl-stock-market-in-2025/ [Google Scholar] [Crossref]

9. Cruz-Hernández, A. R., & Mora-Valencia, A. (2023). Adaptive market hypothesis and predictability: Evidence in Latin American stock indices. Latin American Research Review, 58(1), 3--17. https://doi.org/10.1017/lar.2023.15 [Google Scholar] [Crossref]

10. Fazap, F. M. P. (2023). Hybrid Machine Learning Models for Long-Term Stock Market Forecasting: Integrating Technical Indicators. Journal of Risk and Financial Management, 18(4), 201. https://doi.org/10.3390/jrfm18040201 [Google Scholar] [Crossref]

11. Frenzel, P. (2023). Time-Series Analysis --- Part 4: Autoregressive Models. Medium. https://prof-frenzel.medium.com/kb-time-series-analysis-part-4-autoregressive-models-ed824838bd4c [Google Scholar] [Crossref]

12. Gayo, W. S., Urrutia, J. D., Temple, J. M. F., Sandoval, J. R. D., & Sanglay, J. E. A. (2015, June). Modeling Philippine Stock Exchange Composite Index using time series analysis. Journal of Physics: Conference Series, 622(1), 012022. https://doi.org/10.1088/1742-6596/622/1/012022 [Google Scholar] [Crossref]

13. Gunay, S., & Can, G. (2022). The source of financial contagion and spillovers: An evaluation of the COVID-19 pandemic and the global financial crisis. PLOS ONE, 17(1), e0261835. https://doi.org/10.1371/journal.pone.0261835 [Google Scholar] [Crossref]

14. Hamilton, J. D. (2020). Time Series Analysis. Princeton University Press. https://doi.org/10.2307/j.ctv14jx6sm [Google Scholar] [Crossref]

15. He, X., & Wang, J. (2022). A hybrid forecasting system based on comprehensive feature selection and intelligent optimization for stock price index forecasting. Mathematics, 10(23), 3778. https://doi.org/10.3390/math12233778 [Google Scholar] [Crossref]

16. Holmes, E. E., Scheuerell, M. D., & Ward, E. J. (2021). Applied time series analysis for fisheries and environmental data (Ed. 2021). https://eeholmes.github.io/tsbook/ [Google Scholar] [Crossref]

17. Hongsakulvasu, N., & Liammukda, A. (2020). Asian Stock Markets Analysis: The New Evidence from Time-Varying Coefficient Autoregressive Model. The Journal of Asian Finance, Economics and Business, 7(9), 95--104. https://doi.org/10.13106/JAFEB.2020.VOL7.NO9.095 [Google Scholar] [Crossref]

18. Huang, J., Zhang, P., & Zhang, J. (2023). Understanding Momentum and Reversal Investing Strategies. Journal of Economics, Finance and Accounting Studies, 5(1), 106-112. https://doi.org/10.32996/jefas.2023.5.1.8 [Google Scholar] [Crossref]

19. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3/ [Google Scholar] [Crossref]

20. Investing.com breaks into world's top 400 websites, reaches 10 M downloads. (2019, August 15). Yahoo Finance Australia. https://au.finance.yahoo.com/news/investing-com-breaks-world-top-090900074.html [Google Scholar] [Crossref]

21. Kirikos, D. G. (2013). How did the financial crisis affect the forecasting performance of time series exchange rate models? Evidence from Euro rates. International Journal of Economics and Finance, 5(2), 28-34. https://doi.org/10.5539/ijef.v5n2p28 [Google Scholar] [Crossref]

22. Lawal, A. A., Alli, O., Olatunji, A. O., Ezeife, E., & Okoduwa, E. D. (2023). Time series analysis and forecasting in finance: A data-mining approach. Open Access Research Journal of Science and Technology, 9(1), 75--86. https://doi.org/10.53022/oarjst.2023.9.1.0045 [Google Scholar] [Crossref]

23. Liu, G., Wang, Y., Chen, X., Zhang, Y., & Shang, Y. (2020). Forecasting volatility of the Chinese stock markets using TVP HAR-type models. Physica A: Statistical Mechanics and its Applications, 542, 123445. [Google Scholar] [Crossref]

24. Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. The Journal of Portfolio Management, 30(5), 15--29. https://doi.org/10.3905/jpm.2004.442611 [Google Scholar] [Crossref]

25. Mallikarjuna, M., & Prabhakara Rao, R. (2019). Evaluation of forecasting methods from selected stock market returns. Financial Innovation, 5, Article 40. https://doi.org/10.1186/s40854-019-0157-x [Google Scholar] [Crossref]

26. Navarro, M. M., Young, M. N., Prasetyo, Y. T., & Taylor, J. V. (2023). Stock market optimization amidst the COVID-19 pandemic: Technical analysis, K-means algorithm, and mean-variance model (TAKMV) approach. Heliyon, 9(7), e17577. https://doi.org/10.1016/j.heliyon.2023.e17577 [Google Scholar] [Crossref]

27. Patwary, M. S. A., & Das, K. P. (2023). Forecasting stock indices with the COVID-19 infection rate as an exogenous variable. PeerJ Computer Science, 9, e1532. https://doi.org/10.7717/peerj-cs.1532 [Google Scholar] [Crossref]

28. Philippine Stock Exchange. (2025). PSEi daily trading data, June 2025 [Data set]. [Table adapted from Philippine Stock Exchange historical data]. https://www.pse.com.ph/ [Google Scholar] [Crossref]

29. Rahardja, D. (2021). Statistical time-series forecast via Microsoft Excel (FORECAST.ETS) built-in function. Journal of Research in Applied Mathematics, 7(11), 69--73. [Google Scholar] [Crossref]

30. Rao, M. (2025, March 21). Historical market data sources. QuantInsti. https://blog.quantinsti.com/financial-market-data-providers/ [Google Scholar] [Crossref]

31. Rönkkö, M., Holmi, J., Niskanen, M., & Mättö, M. (2024). The adaptive markets hypothesis: Insights into small stock market efficiency. Applied Economics, 56(25), 3048--3062. https://doi.org/10.1080/00036846.2024.2326039 [Google Scholar] [Crossref]

32. Salgotra, R., Singh, H., Kaur, G., Singh, S., Singh, P., & Lukasi, S. (2024). A novel approach to predict the Asian exchange stock market index using artificial intelligence. Algorithms, 17(10), 457. https://doi.org/10.3390/a17100457 [Google Scholar] [Crossref]

33. Schaffer, A. L., Dobbins, T. A., & Pearson, S. A. (2021). Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: A guide for evaluating large-scale health interventions. BMC Medical Research Methodology, 21, 58. https://doi.org/10.1186/s12874-021-01235-8 [Google Scholar] [Crossref]

34. Tiu, T., Patiu, L., Adriano, M., Cagampan, L., Lorenzo, M., Lee, J., & Cha, G. (2020). Volatility transmission between the foreign exchange rate and stock market from 2004--2018: An empirical analysis of selected emerging markets of Asia-Pacific region. International Journal of Economics and Financial Research, 3, 57--77. [Google Scholar] [Crossref]

35. Tsay, R. S. (2014). Analysis of financial time series (3rd ed.). Wiley. [Google Scholar] [Crossref]

36. Ullrich, T. (2021). On the autoregressive time series model using real and complex analysis. Forecasting, 3(4), 716-728. https://doi.org/10.3390/forecast3040044 [Google Scholar] [Crossref]

37. Wang, Y. B., Qing, S. Y., Liang, Z. Y., Ma, C., Bai, Y. C., & Xu, C. J. (2023). Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China. World journal of gastroenterology, 29(42), 5716--5727. https://doi.org/10.3748/wjg.v29.i42.5716 [Google Scholar] [Crossref]

38. World Bank. (2022). Philippines economic updates [Data set]. https://www.worldbank.org/en/country/philippines/overview [Google Scholar] [Crossref]

39. Zakamulin, V. (2014). The real-life performance of market timing with moving average rules. Journal of Asset Management, 15(4), 261-278. https://doi.org/10.1057/jam.2014.25 [Google Scholar] [Crossref]

40. Zakamulin, V., & Giner, J. (2023). Optimal trend-following with transaction costs. International Review of Financial Analysis, 90, 102928. https://doi.org/10.1016/j.irfa.2023.102928 [Google Scholar] [Crossref]

41. Zhang, Y., Chen, L., & Li, N. (2025). Improved quintic polynomial autonomous vehicle lane-change trajectory planning based on hybrid algorithm optimization. World Electric Vehicle Journal, 16(5), 244. https://doi.org/10.3390/weyj16050244 [Google Scholar] [Crossref]

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