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| Title: | 台灣股價加權指數報酬率與重要總體變數選擇探討
Exploration of the Relationship Between Taiwan’s Weighted Stock Index Returns and Key Macroeconomic Variables |
| Authors: | 洪子逸
Hung, Zih-Yi |
| Contributors: | 蔡致遠
李浩仲
Tsai, Chi-Yuan
Li, Hao-Chung
洪子逸
Hung, Zih-Yi |
| Keywords: | 台灣股價加權指數超額報酬
因子
樣本外預測
機器學習
TAIEX Excess Returns
Factors
Out-of-Sample Forecasting
Machine Learning |
| Date: | 2025 |
| Issue Date: | 2025-08-04 12:51:21 (UTC+8) |
| Abstract: | 本文蒐集2004年1月至2023年12月共80項對於台灣股價加權指數具潛在影響力總體變數，探討不同降維方法所萃取之潛在因子對模型預測力的影響。本文以擴散指數模型為框架，第一階段將變數以不區分類別與區分類別的兩種方式，進行四種降維演算法，分別是主成分分析、核主成分分析、偏最小平方迴歸與偏分量迴歸，第二階段採取遞迴式最小平方法建立預測模型，並以隨機漫步模型為基準，衡量樣本外預測能力。結果顯示，核主成分分析在大多數設定下皆具最佳預測表現，進行類別區分可進一步提升監督式降維方法的預測能力，大宗資產與就業類因子對台灣股價加權指數超額報酬率之邊際貢獻最為顯著。
This study compiles 80 macroeconomic and financial variables that may affect the Taiwan Stock Exchange Capitalization Weighted Index (TAIEX) for the period from January 2004 to December 2023 and investigates how latent factors extracted through alternative dimension reduction techniques influence out of sample predictive performance. Within a diffusion index framework, the analysis proceeds in two stages. In the first stage, the variables are processed under two schemes (without prior classification and with pre-classification), and four dimension reduction algorithms are applied: principal component analysis (PCA), kernel PCA (KPCA), partial least squares regression (PLS) and partial quantile regression (PQR). In the second stage, rolling recursive ordinary least squares models are estimated, using a random walk specification as the benchmark for forecast evaluation. The results show that KPCA provides the highest predictive accuracy in most settings, while pre-classification further improves the performance of the supervised methods. Factor importance analysis indicates that commodity related and labour market factors make the largest marginal contributions to forecasting TAIEX excess returns. |
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| Description: | 碩士
國立政治大學
經濟學系
112258021 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112258021 |
| Data Type: | thesis |
| Appears in Collections: | [經濟學系] 學位論文
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