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PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals

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  • Yagiz Ihlamur
  • Ben Griffin
  • Rick Chen

Abstract

Structured launch signals on Product Hunt contain statistically significant predictive information for Series A funding outcomes. We construct PHBench from 67,292 featured Product Hunt posts spanning 2019-2025, linked to Crunchbase funding records via deterministic domain matching, identifying 528 verified Series A raises within 18 months of launch (positive rate: 0.78%). Our best-performing model, a three-component ensemble (ENS_avg, ENS_ISO, XGB) selected by validation F0.5, achieves F0.5 = 0.097 and AP = 0.037 (95% CI: 0.024-0.072; 4.7x lift over random) on the private held-out test set (103 positives). A paired bootstrap confirms a statistically credible advantage over the logistic regression baseline (AP delta: +0.013, 95% CI: [0.004, 0.039], p

Suggested Citation

  • Yagiz Ihlamur & Ben Griffin & Rick Chen, 2026. "PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals," Papers 2605.02974, arXiv.org.
    • Handle: RePEc:arx:papers:2605.02974
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