Paper: arXiv 2510.15938

Authors: Brian Godwin Lim, Dominic Dayta, Benedict Ryan Tiu, Renzo Roel Tan, Len Patrick Dominic Garces, Kazushi Ikeda

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric techniques (dynamic factor models, Kalman filtering, MLE) with heavy mathematical formalism, yet also demonstrates empirical validation with out-of-sample error metrics (34% reduction) and real-world data (Philippine Stock Exchange, GDP nowcasting), making it mathematically dense and backtest-ready.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Analyze PSE price dynamics<br>via Dynamic Factor Model"] --> B["Data Input:<br>Philippine Stock Exchange<br>Price Data"]

  B --> C["Methodology:<br>Kalman Filter &<br>Maximum Likelihood Estimation"]

  C --> D{"Computational Process:<br>Factor Model Selection"}

  D -- "1-Factor Model" --> E["Outcome 1:<br>Extracted Common Factor<br>correlates with PSE Composite Index<br>(Market Dynamics)"]
  D -- "2-Factor Model" --> F["Outcome 2:<br>Factors representing<br>Market Trends & Volatility"]

  E & F --> G["Validation & Application:<br>Test against CAPM &<br>Nowcast GDP Growth Rates"]

  G --> H["Final Outcome:<br>34% reduction in prediction error<br>Validated as real-time market indicators"]

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1 of 87 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.