{
  "id": 7088,
  "url": "https://arxiv.org/abs/2603.22305v1",
  "title": "CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News",
  "summary": "Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment analysis and event extraction to acting as dynamic decision-making agents in complex financial environments. However, the evolution of LLMs into autonomous financial agents faces a significant dilemma in evaluation paradigms. Direct live trading is irreproducible and prone to outcome bias by confounding luck with skill, whereas existing static benchmarks are often confin",
  "authors": "Liyuan Chen, Shilong Li, Jiangpeng Yan, Shuoling Liu, Qiang Yang, Xiu Li",
  "category": "research",
  "topics": "bias-fairness,agents-autonomy,environment",
  "orgs": null,
  "regions": "china",
  "published_at": "2026-03-18T02:31:28.000Z",
  "fetched_at": "2026-07-14T16:32:59.164Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7088",
  "original_url": "https://arxiv.org/abs/2603.22305v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}