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Abstract We introduce a novel strategy to predict monthly equity premia that is based on extracted news from more than 700,000 newspaper articles, which were published in The New York Times and Washington Post between 1980 and 2018. We propose a flexible data-adaptive switching approach to map a large set of different news-topics into forecasts of aggregate stock returns. The information that is embedded in our extracted news is not captured by established economic predictors. Compared with the prevailing historical mean between 1999 and 2018, we find large out-of-sample (OOS) gains with an ROOS2 of 6.52% and sizeable utility gains for a mean–variance investor. The empirical results indicate that geopolitical news are at times more valuable than economic news to predict the equity premium and we also find that forecasting gains arise in down markets.
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Stored only when both papers are in the corpus — 46 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.