---
title: Survival Models for Startup Failures
url: https://www.ml-quant.com/papers/repec/bba-j00005-v-1-y-2023-i-3-p-1-15-d-264/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:bba:j00005:v:1:y:2023:i:3:p:1-15:d:264
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.anserpress.org%2Fjournal%2Fjes%2F1%2F3%2F14%2Fpdf%3Bh%3Drepec%3Abba%3Aj00005%3Av%3A1%3Ay%3A2023%3Ai%3A3%3Ap%3A1-15%3Ad%3A264
featured: 2023-12-06
citations: unknown
topic: ML & AI Methods
---


# Survival Models for Startup Failures

The research finds that advanced machine learning models like MTLR and Random Forest are more accurate in predicting startup failures than standard models.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.anserpress.org%2Fjournal%2Fjes%2F1%2F3%2F14%2Fpdf%3Bh%3Drepec%3Abba%3Aj00005%3Av%3A1%3Ay%3A2023%3Ai%3A3%3Ap%3A1-15%3Ad%3A264
- Identifier: RePEc:bba:j00005:v:1:y:2023:i:3:p:1-15:d:264
- Released: 2023-12-06
- First featured: Quant Letter No. 28 (2023-12-06): https://www.ml-quant.com/issues/2023-12-06/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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