| dc.contributor.author | Tañan, Michael James | |
| dc.contributor.author | Iyo, Kemuel Dan | |
| dc.contributor.author | Castro, Abigail | |
| dc.date.accessioned | 2025-02-15T07:48:20Z | |
| dc.date.available | 2025-02-15T07:48:20Z | |
| dc.date.issued | 2024-04 | |
| dc.identifier.citation | Tañan, M. J., Iyo, K. D., & Castro, A. (2024). Forecasting of the Philippine stock exchange composite index [Undergraduate Thesis]. University of Mindanao. | en_US |
| dc.identifier.uri | https://repository.umindanao.edu.ph/handle/20.500.14045/1297 | |
| dc.description | In Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Business Administration
Major in Financial Management | en_US |
| dc.description.abstract | This study explores forecasting the Philippine Stock Exchange Composite Index (PSEi) for 2024-2026 using the ARIMA (Autoregressive Integrated Moving Average) model. Leveraging historical data from February 01, 2010 to December 31, 2023, the research scrutinizes various ARIMA models, revealing
challenges in stability and diagnostic concerns. The results highlight ARIMA Model 7 (2,1,2) as the most robust, demonstrating strong explanatory power and stable dynamics. The forecasted values align with historical trends but exhibit uncertainties, as seen in a wide 95 percent interval. While the ARIMA
model performs satisfactorily, acknowledging its limitations, the study recommends exploring model combinations and integrating advanced machine learning methods for enhanced accuracy. Additionally, incorporating three efficient market hypothesis is essential for formulating investment strategies. The research contributes insights to stock market forecasting in emerging markets like the Philippines. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | College of Business Administration | en_US |
| dc.subject | Stock exchanges | en_US |
| dc.title | Forecasting of the Philippine stock exchange composite index | en_US |
| dc.type | Thesis | en_US |
