Predicting Startup-VC Fund Matches with Structural Embeddings and Temporal Investment Data

TL;DR

This paper introduces a novel method for predicting venture capital investments in startups by combining structural embeddings, textual and numerical features, and temporal investment sequences, outperforming static baselines.

Contribution

The study presents a new approach that integrates network structure, textual data, and temporal investment history for more accurate startup-fund match predictions.

Findings

Higher accuracy than static baseline models

Structural features improve prediction quality

Temporal investment modeling captures dynamic fund-startup relationships

Abstract

This study proposes a method for predicting startup inclusion, estimating the probability that a venture capital fund will invest in a given startup. Unlike general recommendation systems, which typically rank multiple candidates, our approach formulates the problem as a binary classification task tailored to each fund-startup pair. Each startup is represented by integrating textual, numerical, and structural features, with Node2Vec capturing network context and multihead attention enabling feature fusion. Fund investment histories are encoded as LSTM based sequences of past investees. Experiments on Japanese startup data demonstrate that the proposed method achieves higher accuracy than a static baseline. The results indicate that incorporating structural features and modeling temporal investment dynamics are effective in capturing fund-startup compatibility.