Venture Capital

Financing Ventures with Fungible Tokens ArXiv ID: ssrn-3137213 “View on arXiv” Authors: Unknown Abstract This paper explores how entrepreneurs can use fungible tokens—whereby they issue digital assets and commit to only accept those tokens as payment for future pro Keywords: Fungible Tokens, Initial Coin Offerings (ICOs), Venture Capital, Blockchain, Alternative Investments Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 2.0/10 Quadrant: Lab Rats Why: The paper presents a formal economic model with proofs and an impossibility result, indicating significant theoretical math density, but it lacks any implementation-heavy backtesting, datasets, or statistical metrics, relying instead on theoretical analysis. flowchart TD A["Research Question:<br>How do entrepreneurs use fungible tokens for venture financing?"] --> B["Methodology: Conceptual Model & Case Studies"] B --> C{"Data & Inputs"} C --> C1["Token Economics"] C --> C2["ICO Whitepapers"] C --> C3["Blockchain Ledgers"] C --> C4["Regulatory Frameworks"] D["Computational Processes<br>Simulation of Funding Rounds"] --> E["Key Findings & Outcomes"] E --> E1["Tokens as Equity Alternatives"] E --> E2["Reduced Barriers to Entry"] E --> E3["Regulatory Uncertainties"] C1 & C2 & C3 & C4 --> D

Venture Capital and Private Equity: A Course Overview ArXiv ID: ssrn-79148 “View on arXiv” Authors: Unknown Abstract Over the past fifteen years, there has been a tremendous boom in the private equity industry. The pool of U.S. private equity funds (partnerships specializing i Keywords: Private Equity, Venture Capital, Leveraged Buyouts, Fund Performance, Private Equity Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a course overview that discusses concepts like agency theory and valuation methods (Monte Carlo, options) but presents them descriptively without advanced derivations or formulas. It lacks any code, backtesting, datasets, or statistical metrics, focusing instead on institutional knowledge and pedagogical structure. flowchart TD A["Research Goal<br>Analyze Private Equity & Venture Capital<br>Industry Growth & Fund Performance"] --> B["Methodology<br>Conceptual Analysis & Course Overview"] B --> C["Data & Inputs<br>15-Year Industry Trends<br>LBO & VC Fund Structures"] C --> D["Computational Process<br>Comparative Framework<br>Performance Evaluation"] D --> E["Key Findings & Outcomes<br>Industry Boom Identified<br>Strategic Course Structure Defined"]

Can Large Language Models Improve Venture Capital Exit Timing After IPO? ArXiv ID: 2601.00810 “View on arXiv” Authors: Mohammadhossien Rashidi Abstract Exit timing after an IPO is one of the most consequential decisions for venture capital (VC) investors, yet existing research focuses mainly on describing when VCs exit rather than evaluating whether those choices are economically optimal. Meanwhile, large language models (LLMs) have shown promise in synthesizing complex financial data and textual information but have not been applied to post-IPO exit decisions. This study introduces a framework that uses LLMs to estimate the optimal time for VC exit by analyzing monthly post IPO information financial performance, filings, news, and market signals and recommending whether to sell or continue holding. We compare these LLM generated recommendations with the actual exit dates observed for VCs and compute the return differences between the two strategies. By quantifying gains or losses associated with following the LLM, this study provides evidence on whether AI-driven guidance can improve exit timing and complements traditional hazard and real-options models in venture capital research. ...

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. ...

Startup success prediction and VC portfolio simulation using CrunchBase data ArXiv ID: 2309.15552 “View on arXiv” Authors: Unknown Abstract Predicting startup success presents a formidable challenge due to the inherently volatile landscape of the entrepreneurial ecosystem. The advent of extensive databases like Crunchbase jointly with available open data enables the application of machine learning and artificial intelligence for more accurate predictive analytics. This paper focuses on startups at their Series B and Series C investment stages, aiming to predict key success milestones such as achieving an Initial Public Offering (IPO), attaining unicorn status, or executing a successful Merger and Acquisition (M&A). We introduce novel deep learning model for predicting startup success, integrating a variety of factors such as funding metrics, founder features, industry category. A distinctive feature of our research is the use of a comprehensive backtesting algorithm designed to simulate the venture capital investment process. This simulation allows for a robust evaluation of our model’s performance against historical data, providing actionable insights into its practical utility in real-world investment contexts. Evaluating our model on Crunchbase’s, we achieved a 14 times capital growth and successfully identified on B round high-potential startups including Revolut, DigitalOcean, Klarna, Github and others. Our empirical findings illuminate the importance of incorporating diverse feature sets in enhancing the model’s predictive accuracy. In summary, our work demonstrates the considerable promise of deep learning models and alternative unstructured data in predicting startup success and sets the stage for future advancements in this research area. ...

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 &amp; $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 &amp; suboptimal portfolio management"]

Fintech and the Future ofFinance ArXiv ID: ssrn-3021684 “View on arXiv” Authors: Unknown Abstract The application of technological innovations to the finance industry (Fintech) has been attracting tens of billions of dollars in venture capital in recent year Keywords: Fintech, venture capital, technological innovation, financial services, disruption, Private Equity Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper presents a qualitative, case-study based policy analysis without any advanced mathematics or statistical models, focusing on regulatory frameworks rather than algorithmic trading strategies, and its empirical evidence is limited to descriptive case studies rather than backtest-ready data. flowchart TD A["Research Goal<br>How does Fintech reshape the future of finance?"] --> B["Methodology"] B --> B1["Quantitative: VC Data Analysis"] B --> B2["Qualitative: Literature Review"] B1 & B2 --> C["Data Inputs"] C --> C1["Global VC Deal Data"] C --> C2["Financial Services Market Reports"] C --> C3["Academic Studies on Disruption"] C1 & C2 & C3 --> D["Computational Process"] D --> D1["Cluster Analysis of Investment Trends"] D --> D2["Comparative Analysis vs. Traditional Finance"] D1 & D2 --> E["Key Findings & Outcomes"] E --> E1["Fintech VC funding correlates with market disruption"] E --> E2["Shift from incumbents to agile startups"] E --> E3["Future outlook: Hybrid models dominate"]

Crowdfunding Creative Ideas: The Dynamics of Project Backers in Kickstarter ArXiv ID: ssrn-2234765 “View on arXiv” Authors: Unknown Abstract Entrepreneurs are turning to crowdfunding as a way to finance their creative ideas. Crowdfunding involves relatively small contributions of many consumer-inves Keywords: crowdfunding, consumer-investors, entrepreneurial finance, alternative financing, venture capital, Private Equity / Alternative Investments Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses two years of Kickstarter panel data with fixed effects models to analyze backer dynamics over time, demonstrating strong empirical data usage and implementation. However, the mathematics involved is primarily descriptive statistics and econometric regressions without advanced derivations or complex formulas, placing it in the Street Traders quadrant. flowchart TD A["Research Goal<br>What drives consumer-investors to back creative projects?"] B["Data Input<br>Kickstarter public project & backing data"] C["Methodology<br>Statistical analysis of funding dynamics & social networks"] D["Computation<br>Regression models & survival analysis"] E["Key Finding 1<br>Social networks & early momentum significantly predict success"] F["Key Finding 2<br>Backer motivation is primarily social & identity-based<br>not purely financial"] A --> B B --> C C --> D D --> E D --> F

Crowdfunding: The New Frontier for Financing Entrepreneurship? ArXiv ID: ssrn-2157429 “View on arXiv” Authors: Unknown Abstract This paper aims to take stock of the extant knowledge on an emerging practice in the entrepreneurial finance landscape: crowdfunding, which seems to play Keywords: Crowdfunding, Entrepreneurial Finance, Venture Capital, Alternative Finance, Startups, Private Equity Complexity vs Empirical Score Math Complexity: 0.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a conceptual review and taxonomy-building exercise with minimal advanced mathematics, focusing on defining and categorizing crowdfunding phenomena rather than quantitative models; empirical rigor is low, relying on a descriptive survey of Italian platforms without backtesting, datasets, or statistical analysis. flowchart TD A["Research Goal:<br/>Assess Crowdfunding's Role<br/>in Entrepreneurial Finance"] --> B["Method: Systematic Literature Review"] B --> C["Data: 75 Studies<br/>2005-2015 Period"] C --> D{"Analysis: Compare<br/>Crowdfunding vs.<br/>Traditional VC/PE"} D --> E["Computational Process:<br/>Thematic &<br/>Comparative Analysis"] E --> F{"Key Findings"} F --> G["Outcome: Crowdfunding<br/>complements, not replaces<br/>traditional finance"] F --> H["Outcome: Enables financing<br/>for non-fundable<br/>early-stage projects"]