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.

徐士勛、管中閔與羅雅惠（2005）。以擴散指標為基礎之總體經濟預測。台灣經濟預測與政策。36（1），1-28。
劉祥熹與涂登才（2012）。美國股市及其總體經濟變數間關連性與波動性之研究—VEC GJR DCC-GARCH-M 之模型應用。《經濟研究》，48（1），139–189。
張天惠（2012）。我國金融情勢指數與總體經濟預測。中央銀行季刊，34（2），11-42。
李偉銘、吳淑貞與黃啟泰（2015）。總體經濟變數對臺灣股市之大盤及類股熊市預測表現之探討。經濟研究，51（2），171-224。
葉錦徽與潘宗麟（2024）。探索預測台灣通膨隱而未現的重要因子－監督式降維模型的實證。管理評論，43（3），19-40。
Bai, J., & Ng, S. (2008). Forecasting economic time series using targeted predictors. Journal of Econometrics, 146(2), 304–317.
Camacho, M., & Sancho, I. (2003). Spanish diffusion indexes. Spanish Economic Review, 5(3), 173–203.
Chen, N., Roll, R., & Ross, S. (1986). Economic forces and the stock market. Journal of Business, 59(3), 383–403.
Cooper, J. P. (1972). The predictive performance of quarterly econometric models of the United States. In B. G. Hickman (Ed.), Econometric models of cyclical behavior (pp. 813–947). NBER.
Dhakal, D., Kandil, M., & Sharma, S. (1993). Causality between the money supply and share prices: A VAR investigation. Quarterly Journal of Business and Economics, 32(3), 52–74.
Giglio, S., Kelly, B., & Pruitt, S. (2016). Systemic risk and the macroeconomy: An empirical evaluation. Journal of Financial Economics, 119(3), 457–471.
Giacomini, R., & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74(6), 1545–1578.
Kelly, B., & Pruitt, S. (2015). The three-pass regression filter: A new approach to forecasting using many predictors. Journal of Econometrics, 186(2), 294–316.
Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies, 1(1), 41–66.
Lo, A. W., & MacKinlay, A. C. (1989). The size and power of the variance ratio test in finite samples: A Monte Carlo investigation. Journal of Econometrics, 40(2), 203–238.
Sims, C. A. (1980a). Macroeconomics and reality. Econometrica, 48(1), 1–48.
Stock, J. H., & Watson, M. W. (1998). Diffusion indexes (NBER Working Paper No. 6702). National Bureau of Economic Research.
Stock, J. H., & Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167–1179.
Chiang, T. C. (2023). Real stock market returns and inflation: Evidence from uncertainty hypotheses. Finance Research Letters, 53, 103606.
Keswani, S., Puri, V., & Jha, R. (2024). Relationship among macroeconomic factors and stock prices: Cointegration approach from the Indian stock market. Cogent Economics & Finance, 12(1), 2355017.
Humpe, A., McMillan, D. G., & Schöttl, A. (2025). Macroeconomic determinants of the stock market: A comparative study of Anglosphere and BRICS. Finance Research Letters, 75, 106869.