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 selection bias on 53 positives and are reported for benchmark reproducibility only.
We further evaluate three zero-shot Gemini models (Gemini 2.5 Flash, Gemini 3 Flash, and Gemini 3.1 Pro) in an anonymized numerical setting. The best LLM achieves AP = 0.034 (Gemini 3 Flash), below the LR baseline AP of 0.044. Notably, the most capable Gemini variant (Gemini 3.1 Pro, AP = 0.023) performs worst -- an unexpected pattern that warrants further investigation across providers and prompting strategies. Both ML and LLM models show the same temporal performance decay tracking the 2020-2021 funding boom and subsequent contraction, confirming the dataset captures genuine market structure rather than noise.
PHBench provides a reproducible framework comprising public training, validation, and blind test splits; 61 engineered features; a five-metric evaluation harness; and a public leaderboard at this https URL. All code, baseline models, and anonymized dataset splits are publicly available.
| Comments: | 30 pages, 1 figure, 4 appendices. Website, leaderboard, and dataset: this https URL |
| Subjects: | Pricing of Securities (q-fin.PR); Machine Learning (cs.LG) |
| ACM classes: | I.2.6; H.2.8; J.4 |
| Cite as: | arXiv:2605.02974 [q-fin.PR] |
| (or arXiv:2605.02974v1 [q-fin.PR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.02974 arXiv-issued DOI via DataCite |
From: Yagiz Ihlamur [view email]
[v1]
Sun, 3 May 2026 17:03:33 UTC (123 KB)
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