Corporate VC firms struggle with internal conflicts despite growth.
problem Internal conflicts among corporate VC investment teams.
method Conducted interviews with corporate VC firms to identify issues.
result Executive-level attention and commitment are lacking, leading to conflicts.
Paper analyzes venture capital exit decisions under inconsistent preferences.
problem Time-inconsistent preferences in venture capital exit timing.
method Modeling four types of venture capitalists with varying levels of inconsistency.
result Time-inconsistent venture capitalists exit earlier than consistent ones.
Proposes a venture bank using equity default swaps to multiply VC capital.
problem Lack of public markets for venture investments and derivative instruments.
method Introduces equity default swaps and a clawback lien to create a new derivative instrument (EDCS).
result EDCS can multiply VC capital and provide full coverage, with a clawback feature to prevent failure incentives.
Investment strategy depends on many factors for venture capital funds.
problem Finding the optimal portfolio size for venture capital funds.
method Analyzes various factors affecting fund returns and optimal portfolio size, starting with basic assumptions and increasing complexity.
result Investment strategy depends on many factors, not a one-size-fits-all formula.
Enhances VC startup success predictions using graph augmented time series models.
problem Challenges in predicting startup success due to limited financial data and subjective forecasts.
method Integrates inter-company relationships into time series analysis using GraphRAG.
result Significantly outperforms previous models in startup success predictions.
Geoeconomic analysis of venture capital portfolios reveals key emerging tech domains and countries.
problem Quantifying geoeconomic power and technological sovereignty through venture capital data.
method Economic complexity methods applied to venture capital portfolios and RVA metrics.
result Cloud Computing, Cybersecurity Tools, and Medtech have the highest concentration of specialization among high-GCI countries.
Study uses LLMs to optimize VC exit timing after IPO.
problem Optimal exit timing after IPO is crucial but not well studied.
method Uses LLMs to analyze financial data and market signals.
result LLMs can improve VC exit timing and generate better returns.
Proposes a new framework to manage venture capital portfolio risk by focusing on deal-level correlations.
problem Managing venture capital portfolio risk, especially extreme outcomes.
method Gaussian-copula-based framework that learns deal-level dependence from observed joint success frequencies.
result Correlation amplifies extreme upside outcomes, shifting portfolio distribution toward heavier right tails.
Biotech startups are found to be similar to tech startups overall.
problem The uniqueness of biotech startups was previously overemphasized.
method Extensive research from new databases analyzed similarities and differences.
result Biotech startups share similarities in venture capital, exit time, and geography with tech startups.
This research examines relationship between staging of Venture Capital (VC) investments and social feedback visible in publicly available data on the Web. We address the question of Venture Capital investment sensitivity to performance and prospects of new venture, given as likelihood of obtaining future financing, ava…
Analyzes how venture investment strategies have evolved over time in different sectors.
problem Understanding changes in venture investment strategies across sectors over time.
method Applied PCA and TCA to analyze a dataset of 52,000 startups and 110,000 funding rounds.
result There has been a shift in venture investment towards lower-tech sectors and a rise in accelerator investments.
A new method prevents insurance derivatives from incentivizing risky behavior.
problem Perverse incentives in insurance derivatives that encourage risky behavior.
method A clawback lien that returns part of the payment value as a lien on the firm.
result Removes the incentive for insured holders to commit acts that result in payment.
We study a practical optimization problems for venture capital investments and/or Research and Development (R&D) investments. The first problem is that, given the amount of the initial investment and the reward function at the initial public offering (IPO) market, the venture capitalist wants to maximize overall discou…
Paper uses time series transformers to predict investment success.
problem Optimizing investment sourcing in VC and GC.
method Transformer-based Multivariate Time Series Classifier (TMTSC).
result TMTSC improves decision making in VC and GC investments.
This study examines the execution phase of corporate share buy-backs, highlighting inefficiencies and costs.
problem Lack of research on share buy-back execution practices and associated costs.
method Comparative analysis of execution practices and fees charged to corporations and investors.
result Uncovered inefficiencies and frictional costs in share buy-back executions, advocating for transparency and fairness.
LIBOR-linked borrowing exposes venture banks to systemic risk without improving profitability.
problem LIBOR-linked borrowing exposes venture banks to systemic risk without improving profitability.
method A scenario where venture banks use interbank borrowed funds for investment loans with minimal default insurance.
result Venture banks can survive and have excellent returns with minimal risk, but face rapid failure if returns fall or interest rates rise.
New risk-sharing rules induced by capital allocation principles.
problem Risk sharing in corporate structures.
method Randomizing existing capital allocation principles.
result Derives new risk-sharing rules complementing existing literature.
This study finds ESG rating disagreement reduces corporate productivity, especially in certain types of firms.
problem The impact of ESG rating disagreement on corporate productivity.
method Analysis of A-share listed companies data from 2015 to 2022 using XGBoost regression and SHAP.
result ESG rating disagreement reduces corporate productivity, especially in certain types of firms.
ChatGPT scores corporate investment plans, predicting future spending and returns.
problem Measuring and predicting corporate investment plans.
method Created a firm-level ChatGPT investment score based on conference calls.
result The investment score predicts future capital expenditures and returns.
Business cycles affect startup valuations, both directly and indirectly.
problem How do business cycles impact startup valuations?
method Structural Equation Model approach using a dataset of 1,089 venture capital investments.
result Business cycles impact startup valuations both directly and indirectly.
New approach identifies offshore financial centers in global corporate network.
problem Political scrutiny of offshore financial centers facilitating tax avoidance.
method Data-driven approach using a global corporate ownership network.
result Identification of 24 sink-OFCs and a set of five conduit-OFCs.
Predicting startup success using Crunchbase data and deep learning.
problem Predicting startup success in a volatile entrepreneurial ecosystem.
method Novel deep learning model integrating funding metrics, founder features, and industry category.
result Achieved 14 times capital growth and identified high-potential startups.
Proposes a more robust rating scale for banks.
problem Inconsistent rating scale validation leading to higher capital requirements.
method Develops a new rating scale that is statistically distinguishable and robust.
result Reduces the calibration probability of default, saving capital requirements.
I explain the root of persistent failure of efforts to remove tax-induced distortions of economic incentives. It lies in FUNDAMENTAL IMPOSSIBILITY of objectively evaluating tax base. Distortions can be entirely avoided in the sector of publicly traded corporations. Evaluation can be bypassed by taxing it in shares (to …
Dual risk models are popular for modeling a venture capital or high tech company, for which the running cost is deterministic and the profits arrive stochastically over time. Most of the existing literature on dual risk models concentrated on the optimal dividend strategies. In this paper, we propose to study the optim…
We compare observed corporate cumulative default probabilities to those calculated using a stochastic model based on an extension of the work of Black and Cox and find that corporations default as if via diffusive dynamics. The model, based on a contingent-claims analysis of corporate capital structure, is easily calib…
China Vanke Co. faced a hostile takeover by Baoneng Group, sparking controversy.
problem A hostile takeover of China Vanke Co. by Baoneng Group.
method No specific method mentioned in the abstract.
result National controversy over corporate governance and government role in capital markets.
Deep learning helps identify promising startups.
problem Identifying successful startups amidst many.
method Literature review and synthesis of DL-based startup evaluation methods.
result Deep learning shows promise in startup success prediction.
This letter assesses model risk in credit capital requirements and finds substantial tail risk.
problem Uncertainty in the probability of default and loss-given-default parameters in credit capital requirements.
method Models estimation risk in a simple way, analyzing two datasets and testing parameter dependency.
result Parameter dependency significantly increases tail risk in capital requirements, requiring substantial increases in regulatory capital.
Blockchain disrupts corporate finance, but challenges remain.
problem Challenges in adopting blockchain for corporate finance.
method Exploring the impact of blockchain on corporate finance valuation and capital allocation.
result Blockchain offers new perspectives but faces regulatory, environmental, and legal challenges.
Defines market-consistent value of insurance liabilities under capital requirements.
problem Value of insurance liabilities subject to repeated capital requirements.
method Optimal stopping problems and backward recursion to compute value.
result Defines the value of insurance liabilities as no-arbitrage price optimally stopped.
We study ranking quantilized mean-field games to select top-performing agents.
problem Selecting top-performing agents in competitive scenarios.
method Developed two formulations: target-based and threshold-based, and provided analytic and semi-explicit solutions.
result Analytic and semi-explicit solutions for quantilized mean-field consistency conditions.
I studied what role the US stock markets and money markets have possibly played in the Gross Private Domestic Investment (GPDI) of the United States from the year 1959 to the year 2001, Gross Private Domestic Investment refers to the total amount of investment spending by businesses and firms located within the borders…
The paper addresses dynamic capital structure models with defaultable debt, proving existence and uniqueness.
problem Dynamic capital structure models with an investor break-even condition may not generate a contraction mapping.
method Provided an example and used a dual problem and change of measure to prove existence and uniqueness.
result A unique Markov-perfect equilibrium exists where firm decisions reflect state-dependent targets.
This article deals with the problem of optimal allocation of capital to corporate bonds in fixed income portfolios when there is the possibility of correlated defaults. Using a multivariate normal Copula function for the joint default probabilities we show that retaining the first few moments of the portfolio default l…
Model shows how firms manage risk and capital in default-prone markets.
problem Managing risk and capital in firms facing default risk.
method Developed a model to characterize optimal dividend and capital structure policies.
result Optimal policy involves paying dividends to keep equity value below a critical threshold.
In a dual risk model, the premiums are considered as the costs and the claims are regarded as the profits. The surplus can be interpreted as the wealth of a venture capital, whose profits depend on research and development. In most of the existing literature of dual risk models, the profits follow the compound Poisson …
Matrix factorization generates investment recommendations for investors.
problem Generating accurate investment recommendations for investors.
method Used matrix factorization and an iterative conjugate gradient method to optimize investment recommendations.
result Achieved highest average prediction accuracy of 13.3% for investors.
The dual risk model is a popular model in finance and insurance, which is often used to model the wealth process of a venture capital or high tech company. Optimal dividends have been extensively studied in the literature for a dual risk model. It is well known that the value function of this optimal control problem do…
Membership in the Russell 1000 and 2000 Indices is based on a ranking of market capitalization in May. Each index is separately value weighted such that firms just inside the Russell 2000 are comparable in size to firms just outside (i.e. at the bottom of the Russell 1000) but have much higher index weights. These feat…
Corporate governance struggles to curb fraud in a globalized economy.
problem Lack of effective international regulations against corporate fraud.
method Analyzes historical economic crises and the role of corporate governance.
result Corporate governance is insufficient to prevent large business fraud.
Machine learning predicts corporate bankruptcy with high accuracy.
problem Predicting corporate insolvency to mitigate economic disruption.
method Applied machine learning techniques like SVM, boosting, neural networks, and Gaussian processes.
result Achieved predictions with over 95% accuracy using expert assessments.
This article deals with the problem of optimal allocation of capital to corporate bonds in fixed income portfolios when there is the possibility of correlated defaults. Under fairly general assumptions for the distribution of the total net assets of a set of firms we show that retaining the first few moments of the por…
Digital transformation boosts corporate financial asset allocation, especially short-term.
problem Understanding how digital transformation affects corporate financial decisions.
method Fixed-effects models and staggered DID design using A-share listed companies data.
result Digital transformation significantly promotes corporate financial asset allocation, more pronounced in short-term.
We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language desce…
The study examines how to assess skill when outcomes are noisy and insufficient.
problem Determining skill when outcomes are unreliable and insufficiently numerous.
method Characterizes decision domains with noise and effective sample size, using population-level validation methods.
result Domains with noisy outcomes are unreliable for individual skill assessment.
Model predicts startup success based on data-driven analysis.
problem Evaluating the quality of startup companies.
method Developed a model using a dataset of startup companies, their founders, and investors. Used a Bayesian approach to calculate features and exit probabilities.
result Model constructs portfolios with high exit rates, nearly double that of top venture capital firms.
Study of public and private VC relationships in France using qualitative methods.
problem Understanding interactions between public and private venture capitalists in France.
method Qualitative approach with semi-structured interviews and thematic content analysis.
result Formal or informal relationships between public and private VCs are a 'economico-cognitive' approach to networking and innovation.