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
阅读金融新闻的语言模型通常是从同一年的文本中训练的,以便测试它们,所以后面测试可以奖励后面观点。我们从NoLBERT,一个仅在1976年至1995年之间出版的文本上进行预训练的编码器中创建新闻主题,通过对比的对比对同一文章的段落进行细节调整,没有标签或回报。我们将其与LDA,MiniLM和FinBERT读者对比,从2012年到2024年,在1.2600万篇文章上,将每个读者的主题转换为注意力冲击和股票曝光,并预测了1000个最大的美国股票的回报,在三个重平衡频率上有8个模型。模型安装在2013年至2019年,在2020年至2021年
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.
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Topics: Stock (firearms), Sentence, Econometrics, Computer science, Reading (process), Language model
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