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| Title: | 基於風險溢酬主成分分析建構效率投資組合：臺灣股市實證
Constructing efficient portfolios based on risk-premium principal component analysis: evidence from Taiwan stock market |
| Authors: | 蔡圭峯
Tsai, Kuei-Feng |
| Contributors: | 林靖庭
羅秉政
Lin, Ching-Ting
Kendro Vincent
蔡圭峯
Tsai, Kuei-Feng |
| Keywords: | 風險溢酬主成分分析
統計因子模型
效率投資組合
Risk premium principal component analysis
Statistical factor models
Efficient portfolios |
| Date: | 2025 |
| Issue Date: | 2025-07-01 15:16:35 (UTC+8) |
| Abstract: | 本研究旨在探討風險溢酬主成分分析 (Risk-Premium Principal Component Analysis, RP-PCA) 與凸非負矩陣分解 (Convex Non-negative Matrix Factorization, convex-NMF) 在臺灣股市建構效率投資組合之應用。有別於傳統特徵因子模型，統計因子模型透過直接分析資產報酬的共變異結構，無需預先設定特定公司特徵即可找出潛在的系統性風險因素。本研究以2007年至2023年臺灣上市公司週資料為樣本，系統性比較不同參數設定下的統計因子表現。研究結果顯示，RP-PCA在加入橫斷面定價誤差權重後，其提取的因子具有顯著較高的夏普比率，特別是在較短的滾動視窗下表現最佳。權重調整策略方面，放空限制與130/30策略在樣本外測試中顯著提升投資組合績效，而波動度標準化效果則不一致。市場特徵因子迴歸分析顯示，RP-PCA因子對動能因子有顯著正向關係，對短期反轉因子呈負向關係，展現捕捉不同時間尺度價格動態的能力。研究結果進一步表明，使用三至五個統計因子已能有效捕捉主要系統性風險來源，過度增加因子數量可能導致過度擬合問題而降低策略效率。本研究也顯示統計因子模型與傳統市場特徵因子模型具互補關係，統計方法能有效整合現有風險來源，提供更穩健的投資組合建構策略。
This study investigates the application of Risk-Premium Principal Component Analysis (RP-PCA) and Convex Non-negative Matrix Factorization (convex-NMF) in constructing efficient portfolios in the Taiwan stock market. Unlike traditional characteristic factor models, statistical factor models identify potential systematic risk factors by directly analyzing the covariance structure of asset returns without predetermining specific company characteristics. Using weekly data from Taiwan listed companies from 2007 to 2023, this research systematically compares the performance of statistical factors under different parameter settings. Results indicate that RP-PCA, after incorporating cross-sectional pricing error weights, extracts factors with significantly higher Sharpe ratios, with optimal performance observed particularly in shorter rolling windows. Regarding weight adjustment strategies, both long-only constraints and 130/30 strategies significantly enhance portfolio performance in out-of-sample tests, while volatility normalization shows inconsistent effects. Market characteristic factor regression analysis reveals that RP-PCA factors exhibit significant positive relationships with momentum factors and negative relationships with short-term reversal factors, demonstrating the ability to capture price dynamics across different time scales. Further findings suggest that three to five statistical factors can effectively capture major systematic risk sources, while excessive factor inclusion may lead to overfitting problems that reduce strategy efficiency. This research confirms the complementary relationship between statistical factor models and traditional market characteristic factor models, demonstrating that statistical methods can effectively integrate existing risk sources to provide more robust portfolio construction strategies. |
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| Description: | 碩士
國立政治大學
金融學系
112352013 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112352013 |
| Data Type: | thesis |
| Appears in Collections: | [金融學系] 學位論文
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