本研究以風險溢酬主成分分析法（Risk-Premium PCA, RP-PCA）探索臺灣股票市場的潛在定價因子結構，並與標準主成分分析法（PCA）及Fama and French (2018) 六因子模型進行系統性比較。RP-PCA 是 Lettau and Pelger（2020）提出的廣義 PCA 估計框架，透過在標準 PCA 目標函數中引入橫斷面定價誤差懲罰項，使因子萃取同時利用資產報酬的一階動差（橫斷面均值）與二階動差（共變異）資訊，從理論上克服了標準 PCA 忽略橫斷面均值資訊的根本局限，尤其能有效識別具有高 Sharpe Ratio 但共變異貢獻有限的弱因子。實證結果顯示，RP-PCA 在橫斷面定價效率、定價誤差與時間序列解釋力三個維度上均優於或等同於 PCA 與 Fama-French 六因子模型，且 RP-PCA 能以更少的因子個數達到 PCA 需更多因子方能實現的同等樣本外定價表現，展現顯著的因子利用效率優勢。此外，RP-PCA 萃取的因子雖與 PCA 萃取的因子具有高度相似的風險結構，但 RP-PCA 因子在控制 PCA 因子風險暴露後仍存在顯著的正向未解釋橫斷面溢酬，表明 RP-PCA 透過橫斷面均值最佳化，在相似的風險暴露結構上系統性地提取了更高的期望報酬，此為 RP-PCA 相對於 PCA 最核心的定價優勢所在。同時，RP-PCA 因子對 Fama-French 六因子亦具有顯著的正向未解釋溢酬，且兩者的風險結構重疊程度相對有限，確認 RP-PCA 能從臺灣市場數據中萃取 Fama-French 框架所無法定價的獨特橫斷面溢酬來源。另一方面，在實際投資應用上，以 RP-PCA 因子為基礎的量化選股策略在樣本外期間實現了優於 PCA 與 Fama-French 策略的風險調整後報酬，亦顯著優於大盤與臺灣 50 指數等被動投資基準。以統計套利方法利用 RP-PCA 與 PCA 因子的線性關係設計市場中性策略，在對沖系統性風險後，各策略樣本外均實現持續穩定的正向累積報酬，確認 RP-PCA 相對於 PCA 的額外定價資訊可轉化為可持續的統計套利收益。

This study employs Risk-Premium Principal Component Analysis (RP-PCA), proposed by Lettau and Pelger (2020), to identify latent pricing factors in the Taiwan stock market and conducts a systematic comparison with standard Principal Component Analysis (PCA) and the Fama and French (2018) six-factor model. RP-PCA extends the standard PCA objective function by incorporating a penalty on cross-sectional pricing errors, enabling factor extraction to simultaneously exploit both first-moment (cross-sectional mean returns) and second-moment (covariance structure) information. This design theoretically overcomes the fundamental limitation of standard PCA—its complete neglect of cross-sectional mean information—and is particularly effective at identifying high-Sharpe-ratio weak factors that standard PCA may not detect. Empirical results demonstrate that RP-PCA outperforms both PCA and the Fama-French six-factor model across all dimensions of model fit—cross-sectional pricing efficiency, pricing errors, and time-series explanatory power—while achieving comparable out-of-sample pricing performance with fewer factors than PCA, reflecting a substantial advantage in factor utilization efficiency. Analysis further reveals that although RP-PCA factors share a highly similar risk structure with PCA factors, RP-PCA factors retain significant positive unexplained cross-sectional premia after controlling for PCA factor exposures. This finding shows that RP-PCA systematically extracts higher expected returns from essentially the same risk structure through cross-sectional mean optimization, which constitutes the core pricing advantage of RP-PCA over PCA. Moreover, RP-PCA factors exhibit significant incremental pricing information relative to the Fama-French framework, with limited risk structure overlap, confirming that RP-PCA successfully identifies cross-sectional return sources in the Taiwan market that Fama-French factors cannot price. In addition, the RP-PCA-based quantitative stock selection strategy achieves superior risk-adjusted returns relative to both PCA and Fama-French strategies in out-of-sample periods. Market-neutral statistical arbitrage strategies designed to exploit the near-linear relationship between RP-PCA and PCA factors further generate persistently positive out-of-sample cumulative returns across all factor pairs after hedging systematic risks, validating that RP-PCA's incremental pricing information translates into sustainable statistical arbitrage profits.

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