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CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News

Liyuan Chen, Shilong Li, Jiangpeng Yan, Shuoling Liu, Qiang Yang, Xiu Li

Abstract

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 confined to entity-level stock picking and ignore broader market attention. To facilitate the rigorous analysis of these challenges, we introduce CN-Buzz2Portfolio, a reproducible benchmark grounded in the Chinese market that maps daily trending news to macro and sector asset allocation. Spanning a rolling horizon from 2024 to mid-2025, our dataset simulates a realistic public attention stream, requiring agents to distill investment logic from high-exposure narratives instead of pre-filtered entity news. We propose a Tri-Stage CPA Agent Workflow involving Compression, Perception, and Allocation to evaluate LLMs on broad asset classes such as Exchange Traded Funds (ETFs) rather than individual stocks, thereby reducing idiosyncratic volatility. Extensive experiments on nine LLMs reveal significant disparities in how models translate macro-level narratives into portfolio weights. This work provides new insights into the alignment between general reasoning and financial decision-making, and all data, codes, and experiments are released to promote sustainable financial agent research.

CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News

Abstract

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 confined to entity-level stock picking and ignore broader market attention. To facilitate the rigorous analysis of these challenges, we introduce CN-Buzz2Portfolio, a reproducible benchmark grounded in the Chinese market that maps daily trending news to macro and sector asset allocation. Spanning a rolling horizon from 2024 to mid-2025, our dataset simulates a realistic public attention stream, requiring agents to distill investment logic from high-exposure narratives instead of pre-filtered entity news. We propose a Tri-Stage CPA Agent Workflow involving Compression, Perception, and Allocation to evaluate LLMs on broad asset classes such as Exchange Traded Funds (ETFs) rather than individual stocks, thereby reducing idiosyncratic volatility. Extensive experiments on nine LLMs reveal significant disparities in how models translate macro-level narratives into portfolio weights. This work provides new insights into the alignment between general reasoning and financial decision-making, and all data, codes, and experiments are released to promote sustainable financial agent research.
Paper Structure (50 sections, 1 figure, 7 tables)

This paper contains 50 sections, 1 figure, 7 tables.

Table of Contents

  1. Introduction
  2. Limitation 1: Scope Misalignment (Entity-Centric vs. Market-Narrative-Driven).
  3. Limitation 2: Scarcity of Macro-Semantic Reasoning in Emerging Markets.
  4. Related Work
  5. Financial LLM Benchmarks and Prediction Tasks
  6. Autonomous Trading Agents and Live Systems
  7. Cross-Cultural and Macro-Semantic Alignment
  8. The CN-Buzz2Portfolio Benchmark
  9. Task Formalization
  10. Data Construction: The "Buzz" Stream
  11. Asset Universes: Macro and Sector Perspectives
  12. Unified Trading Protocol: The Tri-Stage CPA Multi-Agent Framework
  13. Stage 1: Compression (Summarizer $\mathcal{A}_{sum}$).
  14. Stage 2: Perception (Analyst $\mathcal{A}_{ana}$).
  15. Stage 3: Allocation (Trader $\mathcal{A}_{trade}$).
  16. ...and 35 more sections

Figures (1)

  • Figure 1: Comparison of Financial Agent Research Paradigms.(a) Direct Live Trading: Agents interact with real-time markets. While offering maximum realism, this approach poses scientific challenges regarding reproducibility and attribution, making it difficult to isolate valid reasoning from market randomness. (b) Entity-Centric Benchmarks (e.g., StockBench): The standard paradigm mapping news to pre-defined target stocks. This overlooks the "Public Attention Filtering" process and often suffers from high idiosyncratic volatility at the individual stock level, complicating the verification of logical consistency. (c) CN-Buzz2Portfolio (Ours): A rolling-horizon benchmark simulating the pipeline from Daily Trending News (Public Attention) to Macro & Sector Allocation. By targeting diversified asset classes to reduce noise, this framework serves as a diagnostic tool to rigorously evaluate the alignment between semantic understanding and verifiable portfolio logic.