arXiv Machine Learning October 19, 2026 By Kevin Foley, Jonathan Hartadi, Shivesh Prakash, Charishma Takkallapalli, Swapnil Vatsal

Reading the News Without Hindsight: NoLBERT Topics and the Cross-Section of Stock Returns

Read the original on arXiv Machine Learning →

The paper introduces NoLBERT, a language model pretrained only on financial news from 1976 to 1995, and adapts it into a sentence encoder via contrastive fine‑tuning. It compares NoLBERT’s topic extraction to LDA, MiniLM, and FinBERT on 1.26 million articles from 2012 to 2024, converting topics into attention shocks and stock exposures to forecast returns of the 1,000 largest US stocks across multiple models and rebalancing frequencies. While NoLBERT produces the most stable topics and performs comparably to LDA, the best‑validated model still loses money, and an equal‑weight blend of all readers yields a monthly net Sharpe ratio of 1.12, significant against zero but not against a price‑only benchmark.

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