Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems

TL;DR

This paper introduces a Transformer-based multivariate time series classifier to improve investment target sourcing in private equity, demonstrating its effectiveness through extensive experiments on real-world data.

Contribution

It proposes a novel Transformer-based approach for multivariate time series classification tailored to VC and GC investment sourcing, enhancing decision-making accuracy.

Findings

Outperforms three popular baseline models in real-world tasks

Improves success prediction accuracy for investment targets

Enhances decision-making in VC and GC industries

Abstract

This paper addresses the growing application of data-driven approaches within the Private Equity (PE) industry, particularly in sourcing investment targets (i.e., companies) for Venture Capital (VC) and Growth Capital (GC). We present a comprehensive review of the relevant approaches and propose a novel approach leveraging a Transformer-based Multivariate Time Series Classifier (TMTSC) for predicting the success likelihood of any candidate company. The objective of our research is to optimize sourcing performance for VC and GC investments by formally defining the sourcing problem as a multivariate time series classification task. We consecutively introduce the key components of our implementation which collectively contribute to the successful application of TMTSC in VC/GC sourcing: input features, model architecture, optimization target, and investor-centric data processing. Our extensive experiments on two real-world investment tasks, benchmarked towards three popular baselines, demonstrate the effectiveness of our approach in improving decision making within the VC and GC industry.