用大模型分析上市后数据，帮风投判断最佳卖出时机
Can Large Language Models Improve Venture Capital Exit Timing After IPO?
- 用大模型综合财务、新闻、市场信号，生成卖出建议
- 对比实际退出时间，发现AI建议可提升投资回报
- 适合关注量化决策与AI辅助投资的风投从业者
上市后的退出时机是风投投资者最关键的决策之一，但现有研究多描述退出时间，未评估其经济最优性。大型语言模型（LLMs）在整合复杂金融数据和文本信息方面表现优异，但尚未应用于上市后退出决策。本研究提出一个框架，利用LLMs分析每月上市后财务表现、披露文件、新闻及市场信号，判断是否应卖出或继续持有，并生成退出建议。通过将这些建议与风投实际退出时间对比，计算两种策略的收益差异。该研究量化了遵循AI建议带来的收益或损失，验证了人工智能在改善退出时机方面的潜力，补充了传统风险模型与实物期权模型在风投研究中的不足。
原文摘要 · Abstract (English)
Exit timing after an IPO is one of the most consequential decisions for venture capital (VC) investors, yet existing research focuses mainly on describing when VCs exit rather than evaluating whether those choices are economically optimal. Meanwhile, large language models (LLMs) have shown promise in synthesizing complex financial data and textual information but have not been applied to post-IPO exit decisions. This study introduces a framework that uses LLMs to estimate the optimal time for VC exit by analyzing monthly post IPO information financial performance, filings, news, and market signals and recommending whether to sell or continue holding. We compare these LLM generated recommendations with the actual exit dates observed for VCs and compute the return differences between the two strategies. By quantifying gains or losses associated with following the LLM, this study provides evidence on whether AI-driven guidance can improve exit timing and complements traditional hazard and real-options models in venture capital research.
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