Kevin Foley, Jonathan Hartadi, Shivesh Prakash, Charishma Takkallapalli, Swapnil Vatsal · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.22745 · cited by 0 works (OpenAlex) · open-access PDF
ونحن نعمل على بناء موضوعات صحيحة من NoLBERT ، والتي تم تدريبها فقط على النص الذي نشر بين عامي 1976 و1995 ، وتصنيعها إلى محرر القصص عن طريق التكيف المباشر على المراحل من نفس المقال ، بدون علامات أو إصدارات. ونقارنها مع قراءات LDA ، MiniLM و FinBERT على 1.26 مليون مقال من 2012 إلى 2024 ، ونقارن موضوعات كل قارئ إلى ضربات الاهتمام والضغط على الأسهم ، وننظر إلى الإيرادات من 1000 أكبر الأسهم الأمريكية مع ثمانية نموذجين في ثلاث مرات التوازن. يتم تثبيتها على 2013-2019, يتم اختيارها على 2020-2021, و2024 يتم تنفيذها. يتم إجراء تجربة مع أفضل نقطة التحقق من فقدان المال في كل مرتبة
Machine translation (Meta M2M100); the English original follows.
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 2020-2021, and 2022-2024 is held out. The experiment with the best validation score loses money at every frequency. Measuring attention shocks against the past 63 trading days instead of 5 raises the test rank IC of every reader. NoLBERT gives the most stable topics and forecasts about as well as LDA. An equal-weight combination of all readers and models earns a monthly net Sharpe ratio of 1.12, significant against zero before adjusting for the search but not against a price-only benchmark.
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
Topics: Stock (firearms), Sentence, Econometrics, Computer science, Reading (process), Language model
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.22745, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange