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Forecasting GDP growth with stock returns: Time-series or cross-sectional information?

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  • Morita, Hiroshi

Abstract

This paper investigates whether the predictive content of stock-market information for macroeconomic activity reflects high-frequency time-series dynamics or cross-sectional aggregation. Using a factor-augmented mixed-data sampling (MIDAS) framework applied to Japan, we find that aggregate market indices and high-frequency variation provide limited forecasting gains, whereas factor-based predictors extracted from large cross-sections of individual stock returns can improve forecast accuracy relative to an autoregressive benchmark. Overall, the results suggest that the informational value of stock prices for GDP forecasting arises primarily from effective cross-sectional aggregation rather than from higher-frequency variation.

Suggested Citation

  • Morita, Hiroshi, 2026. "Forecasting GDP growth with stock returns: Time-series or cross-sectional information?," Economics Letters, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:ecolet:v:263:y:2026:i:c:s0165176526001400
    DOI: 10.1016/j.econlet.2026.112946
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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