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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.15938 |
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| _version_ | 1866911359762956288 |
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| author | Lim, Brian Godwin Dayta, Dominic Tiu, Benedict Ryan Tan, Renzo Roel Garces, Len Patrick Dominic Ikeda, Kazushi |
| author_facet | Lim, Brian Godwin Dayta, Dominic Tiu, Benedict Ryan Tan, Renzo Roel Garces, Len Patrick Dominic Ikeda, Kazushi |
| contents | The intricate dynamics of stock markets have led to extensive research on models that are able to effectively explain their inherent complexities. This study leverages the econometrics literature to explore the dynamic factor model as an interpretable model with sufficient predictive capabilities for capturing essential market phenomena. Although the model has been extensively applied for predictive purposes, this study focuses on analyzing the extracted loadings and common factors as an alternative framework for understanding stock price dynamics. The results reveal novel insights into traditional market theories when applied to the Philippine Stock Exchange using the Kalman method and maximum likelihood estimation, with subsequent validation against the capital asset pricing model. Notably, a one-factor model extracts a common factor representing systematic or market dynamics similar to the composite index, whereas a two-factor model extracts common factors representing market trends and volatility. Furthermore, an application of the model for nowcasting the growth rates of the Philippine gross domestic product highlights the potential of the extracted common factors as viable real-time market indicators, yielding over a 34% decrease in the out-of-sample prediction error. Overall, the results underscore the value of dynamic factor analysis in gaining a deeper understanding of market price movement dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange Lim, Brian Godwin Dayta, Dominic Tiu, Benedict Ryan Tan, Renzo Roel Garces, Len Patrick Dominic Ikeda, Kazushi Statistical Finance Machine Learning The intricate dynamics of stock markets have led to extensive research on models that are able to effectively explain their inherent complexities. This study leverages the econometrics literature to explore the dynamic factor model as an interpretable model with sufficient predictive capabilities for capturing essential market phenomena. Although the model has been extensively applied for predictive purposes, this study focuses on analyzing the extracted loadings and common factors as an alternative framework for understanding stock price dynamics. The results reveal novel insights into traditional market theories when applied to the Philippine Stock Exchange using the Kalman method and maximum likelihood estimation, with subsequent validation against the capital asset pricing model. Notably, a one-factor model extracts a common factor representing systematic or market dynamics similar to the composite index, whereas a two-factor model extracts common factors representing market trends and volatility. Furthermore, an application of the model for nowcasting the growth rates of the Philippine gross domestic product highlights the potential of the extracted common factors as viable real-time market indicators, yielding over a 34% decrease in the out-of-sample prediction error. Overall, the results underscore the value of dynamic factor analysis in gaining a deeper understanding of market price movement dynamics. |
| title | Dynamic Factor Analysis of Price Movements in the Philippine Stock Exchange |
| topic | Statistical Finance Machine Learning |
| url | https://arxiv.org/abs/2510.15938 |