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| Title: | 藉由機器學習強化基本面、股市動能與市場情緒的動態因子模型：以台灣股票市場為例
Machine Learning-Enhanced Dynamic Factor Models for Taiwanese Stock Markets: Integrating Fundamentals, Momentum, and Sentiment Factors |
| Authors: | 簡祥育
Chien, Hsiang-Yu |
| Contributors: | 江彌修
Chiang, Mi-Hsiu
簡祥育
Chien, Hsiang-Yu |
| Keywords: | 動態因子模型
主成分分析
機器學習
資產定價
Dynamic Factor Model
Principal Component Analysis
Machine Learning
Asset Pricing |
| Date: | 2025 |
| Issue Date: | 2025-07-01 15:15:47 (UTC+8) |
| Abstract: | 本研究以台灣股票市場為資料，藉由納入多種資產特徵，建構不同於 Fama and French (2018) 架構的動態因子模型。本文採用 Kelly et al. (2019) 所提出之 Instrumented Principal Component Analysis (IPCA) 方法，評估不同特徵組合與模型在統計解釋力與風險補償能力上的表現。研究結果顯示，相較於傳統的 Fama and French (2018) 六因子模型與主成分分析 (PCA) 方法，IPCA 模型於樣本外展現出更佳的表現。此外，IPCA 能夠從大量資產特徵中提取出具有財務意涵的潛在因子，並兼顧基本面、股價動能、投資人情緒及籌碼面資訊。另一方面，分析結果亦顯示模型所擷取之因子與產業結構具有關聯，提升對於因子財務詮釋的能力。此外，透過採用拔靴法進行特徵重要性檢定的結果顯示，模型所納入之多項特徵在統計上呈現顯著，進一步支持這些特徵於資產超額報酬解釋上的有效性。
This study utilizes data from the Taiwan stock market to construct a dynamic factor model that differs from the Fama and French (2018) framework by incorporating a wide range of asset characteristics. This study employs the Instrumented Principal Component Analysis (IPCA) method proposed by Kelly et al. (2019) to evaluate the model's performance related to both statistical explanatory power and risk compensation ability under several combinations of characteristics and models. The empirical results show that the IPCA model outperforms both the traditional Fama and French six-factor model and the Principal Component Analysis (PCA) approach in out-of-sample performance. Moreover, IPCA is able to extract latent factors with financial interpretations from high-dimensional characteristic data, capturing information combining fundamentals, price momentum, investor sentiment, and ownership structure. In addition, the analysis reveals a strong relationship between the extracted factors and industry structure, enhancing the model’s interpretability in terms of financial explanation. Finally, the bootstrap-based feature significance tests validate that many of the incorporated characteristics are statistically significant, further supporting their effectiveness in explaining cross-sectional variation in excess returns. |
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| Description: | 碩士
國立政治大學
金融學系
111352011 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111352011 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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