Forecasting GDP growth with stock returns: Time-series or cross-sectional information?
研究股票市场信息对宏观经济活动的预测能力，发现横截面因子比高频时间序列更能提升日本GDP预测精度。
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.