TL;DR
PHBench is a comprehensive benchmark dataset and framework for predicting Series A funding outcomes from Product Hunt launch signals, demonstrating statistically significant predictive power and providing reproducible tools.
Contribution
This work introduces PHBench, a new benchmark with a large dataset, engineered features, and evaluation tools for startup funding prediction from Product Hunt data.
Findings
Best ensemble model achieves F0.5 = 0.097 and AP = 0.037, outperforming random chance.
Statistical analysis confirms a credible advantage over baseline models.
Large dataset captures genuine market trends, not noise.
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 < 0.001; F0.5 delta: +0.056, 95% CI: [0.006, 0.122], p = 0.016). Validation-set metrics (F0.5 = 0.284, AP = 0.126) reflect best-of-144…
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