Paper: arXiv 2309.16888

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel Transformer-based architecture for time series classification, which involves advanced deep learning concepts and multivariate analysis, while also demonstrating extensive experiments on real-world investment datasets with clear backtesting protocols.

Research Flowchart

  flowchart TD
  A["Research Goal: Optimize VC/GC Sourcing<br/>using TMTSC Prediction"] --> B["Methodology: TMTSC Framework"]
  
  B --> C["Data Inputs<br/>Company Time Series & Features"]
  C --> D["Computational Process<br/>Transformer Architecture Training"]
  D --> E["Optimization Target<br/>Investor-Centric Data Processing"]
  
  E --> F["Key Findings:<br/>1. Improved Decision Making<br/>2. Superior Sourcing Performance<br/>3. Validated on Real-World Tasks"]