Equity‐premium prediction: Attention is all you need
研究发现，直接用文本数据的扩散指数模型无法改进股权溢价预测，但通过选择最具预测性的词语并动态更新词典，能显著提升预测效果。
Summary Predictions of stock returns are greatly improved relative to low‐dimensional forecasting regressions when the forecasts are based on the estimated factor of large data sets, also known as the diffusion index (DI) model. However, when applied to text data, DI models do not perform well. This paper shows that by simply using text data in a DI model does not improve equity‐premium forecasts over the naive historical‐average model, but substantial gains are obtained when one selects the most predictive words before computing the factors and allows the dictionary to be updated over time.