The paper explores how large language models (LLMs) can forecast a firm’s future financial performance by integrating alternative data—such as consumer transactions, web traffic, and prediction markets—with traditional financial information. A two‑agent framework is proposed: one agent identifies which alternative data channels are relevant for each firm, and the other uses firm‑ and channel‑specific context to predict revenue. Experiments across four commercial alternative data channels show that incorporating alternative data in context improves LLM forecasts over using either data source alone and outperforms standard forecasting baselines.
arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...
arXiv:2609.36914v1 Announce Type: new Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning,...
LEAP (Likelihood Elicitation and Aggregation for Probabilistic forecasting) is a new approach that reorganizes how evidence is used in LLM-based forecasting systems. Instead of a monolithic prediction that aggregates all evidence at once, LEAP examines each evidence item separately, elicits likelihood parameters, and combines them with an explicit prior to produce a posterior distribution. The method supports continuous, single-choice, and multi-choice forecasts and has been shown to improve prediction and calibration metrics across models on a benchmark covering forecasting, information-seeking, and browsing tasks.
FinLLM-KG is a model‑agnostic framework that enhances financial sentiment analysis by integrating causal large language models with a financial knowledge graph derived from SEC 10‑K filings. It injects temporally grounded, company‑level context at inference time without updating model weights, allowing sentiment predictions to consider broader firm‑level information. The framework also attributes sentiment scores to individual sentences and retrieved knowledge‑graph facts, improving explainability and supporting human‑in‑the‑loop review, and simulations show it consistently outperforms state‑of‑the‑art baselines in portfolio performance.
arXiv:2608. 03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features.
arXiv:2508. 09904v3 Announce Type: replace-cross Abstract: Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form.
arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.
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.
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....
The study evaluates a multi‑agent large language model system for forecasting outcomes of the 2026 FIFA World Cup. Two specialist agents—one quantitative and one news‑focused—produce forecasts that are then reviewed by a critic and combined by a meta‑agent. Results show the news specialist performs best, matching betting market accuracy, while the meta‑agent adds little beyond the specialists’ predictions.
arXiv:2610. 38225v1 Announce Type: new Abstract: Real-world forecasting often requires integrating heterogeneous evidence whose predictive value varies across horizons, operating regimes, and domains.
arXiv:2610.07006v1 Announce Type: new Abstract: Learning effective representations helps characterize the structure and dynamics of equity markets from financial data with a low signal-to-noise ratio...