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 augmentation and split. Our extensive experiments on four datasets, benchmarked towards three popular baselines, demonstrate the effectiveness of our approach in improving decision making within the VC and GC industry.

翻译：本文探讨了数据驱动方法在私募股权（PE）行业中的日益广泛应用，特别是在风险投资（VC）和成长资本（GC）中筛选投资标的（即公司）。我们系统综述了相关方法，并提出了一种基于Transformer的多元时间序列分类器（TMTSC）的新方法，用于预测候选公司的成功可能性。本研究旨在通过将筛选问题正式定义为多元时间序列分类任务，优化VC和GC投资中的筛选性能。我们依次介绍了实现的关键组成部分，这些部分共同促成了TMTSC在VC/GC筛选中的成功应用：输入特征、模型架构、优化目标以及以投资者为中心的数据增强与划分。在四个数据集上进行的广泛实验，与三种主流基线方法的对比，验证了该方法在提升VC和GC行业决策能力方面的有效性。