Reading the News Without Hindsight: NoLBERT Topics and the Cross-Section of Stock Returns
Language models that read financial news are usually trained on text from the same years used to test them, so a backtest can reward hindsight. We build news topics from NoLBERT, an encoder pretrained only on text published between 1976 and 1995, adapted into a sentence encoder by contrastive fine-tuning on passages of the same article, with no labels or returns. We compare it with LDA, MiniLM and FinBERT readers on 1.26 million articles from 2012 to 2024, convert each reader's topics into attention shocks and stock exposures, and forecast the returns of the 1,000 largest US stocks with eight models at three rebalancing frequencies. Models are fitted on 2013-2019, choices are made on 2020...
| Authors | Kevin Foley, Jonathan Hartadi, Shivesh Prakash et al. |
|---|---|
| Primary category | q-fin.ST |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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