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Can Large Language Models Improve Venture Capital Exit Timing After IPO?

Mohammadhossien Rashidi

TL;DR

The paper investigates whether large language models (LLMs) can improve venture capital exit timing after an IPO by constructing a forward-looking framework that assembles a monthly post-IPO information timeline, prompts theory-informed LLMs, and derives an LLM-implied exit date. It quantifies economic value through $ abla R = R_{ ext{LLM exit}} - R_{ ext{VC exit}}$ and compares AI-driven recommendations to realized exits across a 5-year window, incorporating robustness checks across prompts and LLMs. Early results on a 10-firm subset show mixed outcomes, highlighting the need for a larger sample to draw firm conclusions, but the approach demonstrates a concrete path for AI-assisted liquidity decisions in venture capital. Overall, the study contributes a novel evaluation framework that complements traditional hazard-rate and real-options models by translating information flows into forward-looking exit guidance and measurable financial implications.

Abstract

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.

Can Large Language Models Improve Venture Capital Exit Timing After IPO?

TL;DR

The paper investigates whether large language models (LLMs) can improve venture capital exit timing after an IPO by constructing a forward-looking framework that assembles a monthly post-IPO information timeline, prompts theory-informed LLMs, and derives an LLM-implied exit date. It quantifies economic value through and compares AI-driven recommendations to realized exits across a 5-year window, incorporating robustness checks across prompts and LLMs. Early results on a 10-firm subset show mixed outcomes, highlighting the need for a larger sample to draw firm conclusions, but the approach demonstrates a concrete path for AI-assisted liquidity decisions in venture capital. Overall, the study contributes a novel evaluation framework that complements traditional hazard-rate and real-options models by translating information flows into forward-looking exit guidance and measurable financial implications.

Abstract

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.
Paper Structure (20 sections, 1 equation, 1 figure, 1 table)

This paper contains 20 sections, 1 equation, 1 figure, 1 table.

Table of Contents

  1. Literature Review
  2. Data
  3. Data Sources
  4. Identifying VC Investors Using Textual Analysis
  5. Identifying Actual VC Exit Dates
  6. Sample Summary
  7. Methodology
  8. LLM-Integrated Real Options Exit Model (LLM-ROEM)
  9. Step 1: Monthly Post-IPO Information Timeline
  10. Step 2: LLM Prompting with Academic Theory
  11. Step 3: Deriving the LLM-Implied Exit Date
  12. Step 4: Economic Value Comparison
  13. Step 5: Robustness Checks
  14. Results
  15. Overview of Venture Capital Investors (Table 1)
  16. ...and 5 more sections

Figures (1)

  • Figure 1: IPOs frequency