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NoLBERT model trained on historical news aims to reduce stock market prediction bias

By PulseAugur Editorial · [1 sources] ·

Researchers developed NoLBERT, a language model trained on historical financial news from 1976-1995, to mitigate hindsight bias in stock market predictions. They compared NoLBERT's topics with those from LDA, MiniLM, and FinBERT using over a million articles from 2012-2024. While individual models showed limited success in forecasting stock returns, an ensemble of all readers and models achieved a notable monthly Sharpe ratio of 1.12. AI

IMPACT This research explores methods to improve the reliability of AI models in financial forecasting by addressing inherent biases in training data.

RANK_REASON The cluster is about an academic paper detailing a new model and its application in financial research. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

NoLBERT model trained on historical news aims to reduce stock market prediction bias

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The cluster is about an academic paper detailing a new model and its application in financial research. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Kevin Foley, Jonathan Hartadi, Shivesh Prakash, Charishma Takkallapalli, Swapnil Vatsal ·

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

    arXiv:2610.22745v1 Announce Type: cross Abstract: 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 betwee…

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