Forecast Accuracy

A Test of Lookahead Bias in LLM Forecasts ArXiv ID: 2512.23847 “View on arXiv” Authors: Zhenyu Gao, Wenxi Jiang, Yutong Yan Abstract We develop a statistical test to detect lookahead bias in economic forecasts generated by large language models (LLMs). Using state-of-the-art pre-training data detection techniques, we estimate the likelihood that a given prompt appeared in an LLM’s training corpus, a statistic we term Lookahead Propensity (LAP). We formally show that a positive correlation between LAP and forecast accuracy indicates the presence and magnitude of lookahead bias, and apply the test to two forecasting tasks: news headlines predicting stock returns and earnings call transcripts predicting capital expenditures. Our test provides a cost-efficient, diagnostic tool for assessing the validity and reliability of LLM-generated forecasts. ...

Generative AI for Analysts ArXiv ID: 2512.19705 “View on arXiv” Authors: Jian Xue, Qian Zhang, Wu Zhu Abstract We study how generative artificial intelligence (AI) transforms the work of financial analysts. Using the 2023 launch of FactSet’s AI platform as a natural experiment, we find that adoption produces markedly richer and more comprehensive reports – featuring 40% more distinct information sources, 34% broader topical coverage, and 25% greater use of advanced analytical methods – while also improving timeliness. However, forecast errors rise by 59% as AI-assisted reports convey a more balanced mix of positive and negative information that is harder to synthesize, particularly for analysts facing heavier cognitive demands. Placebo tests using other data vendors confirm that these effects are unique to FactSet’s AI integration. Overall, our findings reveal both the productivity gains and cognitive limits of generative AI in financial information production. ...