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This paper addresses the growing application of data-driven approaches withinthe Private Equity (PE) industry, particularly in sourcing investment targets(i.e., companies) for Venture Capital (VC) and Growth Capital (GC). We presenta comprehensive review of the relevant approaches and propose a novel approachleveraging a Transformer-based Multivariate Time Series Classifier (TMTSC) forpredicting the success likelihood of any candidate company. The objective ofour research is to optimize sourcing performance for VC and GC investments byformally defining the sourcing problem as a multivariate time seriesclassification task. We consecutively introduce the key components of ourimplementation which collectively contribute to the successful application ofTMTSC in VC/GC sourcing: input features, model architecture, optimizationtarget, and investor-centric data augmentation and split. Our extensiveexperiments on four datasets, benchmarked towards three popular baselines,demonstrate the effectiveness of our approach in improving decision makingwithin the VC and GC industry.

Sourcing Investment Targets for Venture and Growth Capital Using Multivariate Time Series Transformer