Private Equity / Venture Capital

Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method ArXiv ID: 2408.09420 “View on arXiv” Authors: Unknown Abstract In the Venture Capital (VC) industry, predicting the success of startups is challenging due to limited financial data and the need for subjective revenue forecasts. Previous methods based on time series analysis often fall short as they fail to incorporate crucial inter-company relationships such as competition and collaboration. To fill the gap, this paper aims to introduce a novel approach using GraphRAG augmented time series model. With GraphRAG, time series predictive methods are enhanced by integrating these vital relationships into the analysis framework, allowing for a more dynamic understanding of the startup ecosystem in venture capital. Our experimental results demonstrate that our model significantly outperforms previous models in startup success predictions. ...

Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems ArXiv ID: 2309.16888 “View on arXiv” Authors: Unknown 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. ...

Predictably Bad Investments: Evidence from Venture Capitalists ArXiv ID: ssrn-4135861 “View on arXiv” Authors: Unknown Abstract Do institutional investors invest efficiently? To study this question I combine a novel dataset of over 16,000 startups (representing over $9 billion in investm Keywords: Venture Capital, Institutional Investors, Startup Investment, Portfolio Management, Efficiency, Private Equity / Venture Capital Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses standard machine learning methods rather than advancing novel mathematics, but it employs a large, novel dataset and rigorous empirical analysis (counterfactual portfolio construction, robustness checks, and measurement of economic magnitude) to backtest investment strategies. flowchart TD RQ["Research Question: Do institutional investors invest efficiently?"] --> I["Inputs: 16,000+ startups & $9B+ investments"] I --> M["Methodology: Performance vs. Investment Timing analysis"] M --> CP["Computation: Out-of-sample return predictions"] CP --> F1["Predictably Bad Investments: Poor timing leads to predictable low returns"] F1 --> F2["Outcomes: Evidence of inefficiency & suboptimal portfolio management"]

Venture Capital and theFinanceof Innovation ArXiv ID: ssrn-929145 “View on arXiv” Authors: Unknown Abstract This article contains the front matter plus the first chapter from the textbook, Venture Capital and the Finance of Innovation. The book is intended for finance Keywords: venture capital, innovation financing, startup valuation, private equity, Private Equity / Venture Capital Complexity vs Empirical Score Math Complexity: 5.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The book employs advanced financial models like option pricing and discounted cash flow (DCF), which require significant mathematical sophistication, but it is primarily a textbook focused on conceptual frameworks and valuation tools rather than providing backtest-ready code or heavy empirical data analysis. flowchart TD A["Research Goal: How Venture Capital<br>Finances Innovation"] --> B["Data/Inputs: Private Equity/Venture<br>Capital Deal Flow & Valuations"] B --> C["Methodology: Financial Analysis<br>of VC-Backed Startups"] C --> D["Computational Process:<br>Valuation & Risk Assessment Models"] D --> E["Key Findings: VC serves as<br>optimal financing for high-risk innovation"] E --> F["Outcomes: Structured investment<br>framework for startups"]