本研究旨在解決過去文獻中提出的兩個主要問題。首先，儘管過去研究顯示許多模型在樣本外達到統計顯著性，但往往無法獲得顯著的已實現效用收益。本研究通過結合大量預測因子並應用迭代式組合迴歸，以預測股票市場風險溢酬與條件波動率。其次，模型的預測能力在不同經濟狀況下存在顯著差異，景氣循環會提高模型預測能力的不穩定性。鑑於此，本研究提出雙重狀態預測迴歸模型，在模型內納入總體經濟指標的虛擬變數與預測因子的交乘項來控制市場變化。實證發現，該模型在不同預測期間下均獲得顯著的樣本外預測力，相較於歷史平均法可獲得超過4%的效用收益，大幅提升資產配置績效，具有顯著的經濟意義。

(182_M69eefaf0d9afd_Abs.pdf(檔案不存在))

This study aims to address two main issues raised in the literature. First, while numerous past research has shown that many models can exhibit out-of-sample statistical significance, they often fail to achieve significant realized utility gains. This study combines a large set of predictors and applies an iterated combination approach to predict stock market risk premiums and conditional volatility. Second, the predictive performance of forecasting models varies significantly under different economic conditions, with business cycles increasing the instability of model predictions. To tackle this challenge, we propose a two-state predictive regression model. This model incorporates the interaction terms between macroeconomic indicators and predictors to control for market variations. Empirical results show that the model achieves significant out-of-sample predictive power over different forecasting horizons and obtains utility gains exceeding 4% compared to the historical average model, substantially improving asset allocation performance and demonstrating significant economic value.

(182_M69eefaf0d9afd_Abs.pdf(檔案不存在))

Ang, A., & Bekaert, G. (2007). Stock Return Predictability: Is it There? The Review of Financial Studies, 20(3), 651-707. https://doi.org/10.1093/rfs/hhl021 Bates, J. M., & Granger, C. W. (1969). The Combination of Forecasts. Journal of the Operational Research Society, 20(4), 451-468. https://doi.org/10.1057/jors.1969.103 Boyd, J. H., Hu, J., & Jagannathan, R. (2005). The Stock Market's Reaction to Unemployment News: Why Bad News Is Usually Good for Stocks. The Journal of Finance, 60(2), 649-672. https://doi.org/10.1111/j.1540-6261.2005.00742.x Campbell, J. Y., & Thompson, S. B. (2008). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? The Review of Financial Studies, 21(4), 1509-1531. https://doi.org/10.1093/rfs/hhm055 Chen, J., Tang, G., Yao, J., & Zhou, G. (2023). Employee Sentiment and Stock Returns. Journal of Economic Dynamics and Control, 149, 104636. https://doi.org/10.1016/j.jedc.2023.104636 Clark, T. E., & West, K. D. (2007). Approximately Normal Tests for Equal Predictive Accuracy in Nested Models. Journal of Econometrics, 138(1), 291-311. https://doi.org/10.1016/j.jeconom.2006.05.023 Cochrane, J. H. (1991). Production-Based Asset Pricing and the Link Between Stock Returns and Economic Fluctuations. The Journal of Finance, 46(1), 209-237. https://doi.org/10.1111/j.1540-6261.1991.tb03750.x Conrad, C., & Kleen, O. (2020). Two are Better than One: Volatility Forecasting Using Multiplicative Component GARCH-MIDAS Models. Journal of Applied Econometrics, 35(1), 19-45. https://doi.org/10.1002/jae.2742 Cooper, M. J., Gutierrez Jr., R. C., & Hameed, A. (2004). Market States and Momentum. The Journal of Finance, 59(3), 1345-1365. https://doi.org/10.1111/j.1540-6261.2004.00665.x Dangl, T., & Halling, M. (2012). Predictive Regressions with Time-Varying Coefficients. Journal of Financial Economics, 106(1), 157-181. https://doi.org/10.1016/j.jfineco.2012.04.003 Dong, X., Li, Y., Rapach, D. E., & Zhou, G. (2022). Anomalies and the Expected Market Return. The Journal of Finance, 77(1), 639-681. https://doi.org/10.1111/jofi.13099 Fama, E. F., & French, K. R. (1988). Dividend Yields and Expected Stock Returns. Journal of Financial Economics, 22(1), 3-25. https://doi.org/10.1016/0304-405X(88)90020-7 Froot, K. A., Scharfstein, D. S., & Stein, J. C. (1992). Herd on the Street: Informational Inefficiencies in a Market with Short-Term Speculation. The Journal of Finance, 47(4), 1461-1484. https://doi.org/10.1111/j.1540-6261.1992.tb04665.x Goyal, A., Welch, I., & Zafirov, A. (2024). A Comprehensive 2022 Look at the Empirical Performance of Equity Premium Prediction. The Review of Financial Studies, 37(11), 3490-3557. https://doi.org/10.1093/rfs/hhae044 Henkel, S. J., Martin, J. S., & Nardari, F. (2011). Time-Varying Short-Horizon Predictability. Journal of Financial Economics, 99(3), 560-580. https://doi.org/10.1016/j.jfineco.2010.09.008 Huang, D., Jiang, F., Tu, J., & Zhou, G. (2017). Forecasting Stock Returns in Good and Bad Times: The Role of Market States. Social Science Research Network, 2188989. http://dx.doi.org/10.2139/ssrn.2188989 Kelly, B., Malamud, S., & Zhou, K. (2024). The Virtue of Complexity in Return Prediction. The Journal of Finance, 79(1), 459-503. https://doi.org/10.1111/jofi.13298 Kothari, S. P., & Shanken, J. (1997). Book-to-Market, Dividend Yield, and Expected Market Returns: A Time-Series Analysis. Journal of Financial Economics, 44(2), 169-203. https://doi.org/10.1016/S0304-405X(97)00002-0 Lin, H., Wu, C., & Zhou, G. (2018). Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach. Management Science, 64(9), 4218-4238. https://doi.org/10.1287/mnsc.2017.2734 Newey, W. K., & West, K. D. (1987). Hypothesis Testing with Efficient Method of Moments Estimation. International Economic Review, 28(3), 777-787. https://doi.org/10.2307/2526578 Paye, B. S. (2012). ‘Déjà vol’: Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables. Journal of Financial Economics, 106(3), 527-546. https://doi.org/10.1016/j.jfineco.2012.06.005 Rapach, D. E., Ringgenberg, M. C., & Zhou, G. (2016). Short Interest and Aggregate Stock Returns. Journal of Financial Economics, 121(1), 46-65. https://doi.org/10.1016/j.jfineco.2016.03.004 Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy. The Review of Financial Studies, 23(2), 821-862. https://doi.org/10.1093/rfs/hhp063 Rapach, D. E., Strauss, J. K., & Zhou, G. (2013). International Stock Return Predictability: What Is the Role of the United States? The Journal of Finance, 68(4), 1633-1662. https://doi.org/10.1111/jofi.12041 Rapach, D. E., & Zhou, G. (2022). Asset Pricing: Time-Series Predictability. Oxford Research Encyclopedia of Economics and Finance. https://doi.org/10.1093/acrefore/9780190625979.013.777 Stock, J. H., & Watson, M. W. (2004). Combination Forecasts of Output Growth in a Seven‐Country Data Set. Journal of Forecasting, 23(6), 405-430. https://doi.org/10.1002/for.928 Thornton, D. L., & Valente, G. (2012). Out-of-Sample Predictions of Bond Excess Returns and Forward Rates: An Asset Allocation Perspective. The Review of Financial Studies, 25(10), 3141-3168. https://doi.org/10.1093/rfs/hhs069 Welch, I., & Goyal, A. (2008). A Comprehensive Look at The Empirical Performance of Equity Premium Prediction. The Review of Financial Studies, 21(4), 1455-1508. https://doi.org/10.1093/rfs/hhm014