Private Equity

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. ...

Optimal Capital Deployment Under Stochastic Deal Arrivals: A Continuous-Time ADP Approach ArXiv ID: 2508.10300 “View on arXiv” Authors: Kunal Menda, Raphael S Benarrosh Abstract Suppose you are a fund manager with $100 million to deploy and two years to invest it. A deal comes across your desk that looks appealing but costs $50 million – half of your available capital. Should you take it, or wait for something better? The decision hinges on the trade-off between current opportunities and uncertain future arrivals. This work formulates the problem of capital deployment under stochastic deal arrivals as a continuous-time Markov decision process (CTMDP) and solves it numerically via an approximate dynamic programming (ADP) approach. We model deal economics using correlated lognormal distributions for multiples on invested capital (MOIC) and deal sizes, and model arrivals as a nonhomogeneous Poisson process (NHPP). Our approach uses quasi-Monte Carlo (QMC) sampling to efficiently approximate the continuous-time Bellman equation for the value function over a discretized capital grid. We present an interpretable acceptance policy, illustrating how selectivity evolves over time and as capital is consumed. We show in simulation that this policy outperforms a baseline that accepts any affordable deal exceeding a fixed hurdle rate. ...

A Dynamic Model of Private Asset Allocation ArXiv ID: 2503.01099 “View on arXiv” Authors: Unknown Abstract We build a state-of-the-art dynamic model of private asset allocation that considers five key features of private asset markets: (1) the illiquid nature of private assets, (2) timing lags between capital commitments, capital calls, and eventual distributions, (3) time-varying business cycle conditions, (4) serial correlation in observed private asset returns, and (5) regulatory constraints on certain institutional investors’ portfolio choices. We use cutting-edge machine learning methods to quantify the optimal investment policies over the life cycle of a fund. Moreover, our model offers regulators a tool for precisely quantifying the trade-offs when setting risk-based capital charges. ...

Evaluating Investment Risks in LATAM AI Startups: Ranking of Investment Potential and Framework for Valuation ArXiv ID: 2410.03552 “View on arXiv” Authors: Unknown Abstract The growth of the tech startup ecosystem in Latin America (LATAM) is driven by innovative entrepreneurs addressing market needs across various sectors. However, these startups encounter unique challenges and risks that require specific management approaches. This paper explores a case study with the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics within the context of the online food delivery industry in LATAM, serving as a model for valuing startups using the Discounted Cash Flow (DCF) method. By analyzing key emerging powers such as Argentina, Colombia, Uruguay, Costa Rica, Panama, and Ecuador, the study highlights the potential and profitability of AI-driven startups in the region through the development of a ranking of emerging powers in Latin America for tech startup investment. The paper also examines the political, economic, and competitive risks faced by startups and offers strategic insights on mitigating these risks to maximize investment returns. Furthermore, the research underscores the value of diversifying investment portfolios with startups in emerging markets, emphasizing the opportunities for substantial growth and returns despite inherent risks. ...

Predicting public market behavior from private equity deals ArXiv ID: 2407.01818 “View on arXiv” Authors: Unknown Abstract We process private equity transactions to predict public market behavior with a logit model. Specifically, we estimate our model to predict quarterly returns for both the broad market and for individual sectors. Our hypothesis is that private equity investments (in aggregate) carry predictive signal about publicly traded securities. The key source of such predictive signal is the fact that, during their diligence process, private equity fund managers are privy to valuable company information that may not yet be reflected in the public markets at the time of their investment. Thus, we posit that we can discover investors’ collective near-term insight via detailed analysis of the timing and nature of the deals they execute. We evaluate the accuracy of the estimated model by applying it to test data where we know the correct output value. Remarkably, our model performs consistently better than a null model simply based on return statistics, while showing a predictive accuracy of up to 70% in sectors such as Consumer Services, Communications, and Non Energy Minerals. ...

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. ...

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 und Crowdinvesting: State-of-the-Art der wissenschaftlichen Literatur (Crowdfunding and Crowdinvesting: A Review of the Literature) ArXiv ID: ssrn-2274141 “View on arXiv” Authors: Unknown Abstract German Abstract: Crowdfunding gewinnt in der Gründungs- und Innovationsfinanzierung an Bedeutung. Ein Überblick der wissenschaftlichen Arbeiten zu Crowdf Keywords: Crowdfunding, Start-up financing, Innovation finance, Private Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a literature review defining crowdfunding concepts and classifying models, with no mathematical formulas or advanced statistical methods presented. It lacks any empirical data, backtests, or implementation details, serving primarily as a theoretical classification. flowchart TD A["Research Goal: Literature Review<br>on Crowdfunding & Crowdinvesting"] --> B{"Methodology: Systematic<br>Literature Review"}; B --> C["Data: 73 Empirical Studies<br>(2010-2018)"]; C --> D{"Computational Process:<br>Descriptive & Thematic Analysis"}; D --> E["Key Findings / Outcomes"]; E --> F["Emergence of Crowdfunding<br>as alternative finance"]; E --> G["Risk-Return Profile of<br>Crowdinvesting vs. Traditional PE"]; E --> H["Identification of<br>Research Gaps"];

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"]