The paper introduces an AI economist agent that integrates large language models, retrieval‑augmented generation, knowledge graphs, and quantitative models to conduct evidence‑based economic and financial scenario analysis. The framework orchestrates LLM agents to plan analyses, retrieve relevant evidence, and structure economic mechanisms, while registered quantitative models produce numerical outcomes and predefined tests validate intermediate results for inclusion in the final report. Applied to European macro‑financial stress scenarios and bank capital analysis, the empirical study demonstrates the agent’s ability to combine flexible evidence retrieval and scenario construction while maintaining traceability to sources and explicit model calculations.
By Masahiro Kato
arXiv:2608. 03339v1 Announce Type: new Abstract: Enterprise forecasting increasingly relies on autonomous agents that interpret documents, search for data, generate code, and revise models.
By Junhyeok Kang, Sangjun Han, Hyeokjun Choe, Soonyoung Lee
arXiv:2606. 24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamentals, macroeconomic regime, and contemporaneous text.
By Patara Trirat, Jin Myung Kwak, Jay Heo, Heejun Lee, Sung Ju Hwang
arXiv:2605. 27864v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in finance, yet most existing work emphasizes trading signals or financial NLP tasks centered on prediction.
By Di Zhu, Lei Nico Zheng, Zihan Chen
arXiv:2606. 03918v1 Announce Type: new Abstract: AI agents can increasingly handle the mechanical tasks of financial analysis: retrieving documents, calculating formulas, updating spreadsheets.
By Eric Cho, Shawn Huang, Alice Lu, Andy Lyu
arXiv:2605.27864v5 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly applied in finance, yet most existing work emphasizes trading signals or financial NLP tasks centered...
By Di Zhu, Lei Nico Zheng, Zihan Chen
arXiv:2607. 18271v1 Announce Type: new Abstract: Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations.
By Ria Mundhra, Gustavo Sato dos Santos, Michael Benedikt
arXiv:2607. 09121v1 Announce Type: cross Abstract: In this study, we examine the opportunities brought by Large Language Models (LLMs) to various aspects of fundamental analysis of companies based on their reports as well as data and documents describing macroeconomic situation like GDP and inflation changes as well as documents filled to the U.
By Bartosz Zi\'o{\l}ko, Kacper Dobrzeniewski
The paper introduces a framework that uses large language models (LLMs) to generate natural‑language narratives explaining cross‑sectional stock return predictions. It combines temporal Shapley additive explanations (SHAP) from an XGBoost model with historical regime analogs to provide context. A controlled study shows that progressively externalizing numerical and relational reasoning improves evidence faithfulness and accuracy, while historical analogs boost human‑rated usefulness.
By Sujung Kim, Seung Hwan Cho, Sangjin Park, Young-Min Kim
arXiv:2607. 19409v1 Announce Type: new Abstract: Recent advances in large language models have accelerated deployment of agentic systems in operational finance.
By Wolfgang M. Pauli, Sarah Panda, Kidus Admassu, Said Bleik, Ademola Okerinde, Jeremy Reynolds
arXiv:2609.05905v1 Announce Type: cross
Abstract: LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events....
By Yuanpu Cao, Yongkang Du, Yurui Chang, Lu Lin, Jinghui Chen
arXiv:2512. 02436v2 Announce Type: replace Abstract: Prediction markets allow users to trade on outcomes of real-world events, but are prone to fragmentation with overlapping questions, implicit equivalences, and hidden contradictions across markets.
By Agostino Capponi, Alfio Gliozzo, Brian Zhu