Sourcing Investment Targets for Venture and Growth Capital Using
  Multivariate Time Series Transformer
Lele Cao Gustaf Halvardsson Andrew McCornack Vilhelm von Ehrenheim Pawel Herman
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.