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.

翻译：本文探讨了数据驱动方法在私募股权行业日益增长的应用，特别是在为风险投资和成长资本寻找投资标的（即公司）方面。我们对相关方法进行了全面综述，并提出了一种新颖方法，利用基于Transformer的多变量时间序列分类器来预测候选公司的成功概率。本研究的目标是通过将标的寻找问题形式化定义为多变量时间序列分类任务，优化风险投资和成长资本的投资标的寻找绩效。我们依次介绍了实现方案的关键组成部分——输入特征、模型架构、优化目标以及以投资者为中心的数据处理流程——这些要素共同促成了TMTSC在风险投资/成长资本标的寻找中的成功应用。我们在两个真实投资任务上进行的广泛实验，以三种常用基线方法为基准，证明了该方法在改善风险投资和成长资本行业决策有效性方面的显著优势。