Can Language Models Learn to Forecast Stock Prices
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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arXiv:2608. 09880v1 Announce Type: cross Abstract: Financial prediction typically relies on task-specific regression, ranking, or policy heads, separating the language model from the numerical object ultimately evaluated.
arXiv:2605.24564v2 Announce Type: replace Abstract: Backtesting large language models (LLMs) on historical financial data is unreliable when their pre-training data include the evaluated events. An L...
arXiv:2605. 03460v3 Announce Type: replace Abstract: Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail in the financial domain, which exhibits unique characteristics.
arXiv:2607. 16229v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as components of agentic systems that observe, plan, and act.
Forecast-Dojo is a replayable environment designed to benchmark and train large language model (LLM) forecasting agents. It integrates resolved prediction‑market questions with dated news, enabling agents to research events and revisit predictions at successive historical dates. The platform includes 1,568 Polymarket events, 18.8 million dated news articles, and supports repeated evaluation, training interactions, and outcome feedback, with evidence that research tools lower Brier scores across 12 tested models, though all models still lag behind historical market forecasts.
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...