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Forecasting Chinese equity premium: A dimensionality reduction combination approach

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  • Yang, Zheng
  • Wu, Haocheng
  • Kuo, Biing-Shen
  • Ma, Yongkai

Abstract

Using a high-dimensional dataset comprising 993 macroeconomic predictors, we develop a dimensionality reduction combination forecast framework to examine the out-of-sample predictability of the Chinese equity premium. We compare forecasts across two aspects: (1) 14 predictor groups versus the full set, and (2) 15 individual dimensionality reduction models versus three combination methods. Our findings indicate that the full set offers richer information and that combining forecasts across dimensionality reduction models yields statistically and economically out-of-sample gains. Encompassing tests and MSPE decomposition explain the benefits of the dimensionality reduction combination forecast. These findings survive a series of robustness checks.

Suggested Citation

  • Yang, Zheng & Wu, Haocheng & Kuo, Biing-Shen & Ma, Yongkai, 2026. "Forecasting Chinese equity premium: A dimensionality reduction combination approach," Journal of Economic Dynamics and Control, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:dyncon:v:186:y:2026:i:c:s0165188926000540
    DOI: 10.1016/j.jedc.2026.105308
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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