SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performance

TL;DR

This paper introduces a multi-agent framework that combines machine learning and LLMs to improve startup success prediction accuracy, addressing current limitations and demonstrating significant performance gains.

Contribution

The paper presents a novel multi-agent system integrating traditional ML and LLMs for startup success forecasting, with enhanced explainability and performance.

Findings

Founder segmentation significantly impacts success likelihood.

Baseline LLMs tend to overpredict success and struggle with class imbalance.

The framework improves prediction accuracy by over 100% compared to GPT models.

Abstract

LLM based agents have recently demonstrated strong potential in automating complex tasks, yet accurately predicting startup success remains an open challenge with few benchmarks and tailored frameworks. To address these limitations, we propose the Startup Success Forecasting Framework, an autonomous system that emulates the reasoning of venture capital analysts through a multi agent collaboration model. Our framework integrates traditional machine learning methods such as random forests and neural networks within a retrieval augmented generation framework composed of three interconnected modules: a prediction block, an analysis block, and an external knowledge block. We evaluate our framework and identify three main findings. First, by leveraging founder segmentation, startups led by L5 founders are 3.79 times more likely to succeed than those led by L1 founders. Second, baseline large language models consistently overpredict startup success and struggle under realistic class imbalances largely due to overreliance on founder claims. Third, our framework significantly enhances prediction accuracy, yielding a 108.3 percent relative improvement over GPT 4o mini and a 30.8 percent relative improvement over GPT 4o. These results demonstrate the value of a multi agent approach combined with discriminative machine learning in mitigating the limitations of standard large language model based prediction methods.