Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems
This addresses the need for data-driven decision-making in private equity sourcing, though it appears incremental as it applies an existing method to a new domain.
The paper tackles the problem of predicting investment success for venture and growth capital by proposing a Transformer-based multivariate time series classifier, showing effectiveness in experiments on real-world tasks compared to baselines.
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.