The study predicts bankruptcy in Indian companies using financial ratios.
problem Predicting early signs of corporate bankruptcy in Indian companies.
method Logistic regression considering profitability, leverage, and efficiency ratios for one and two years before bankruptcy.
result The model accurately predicts bankruptcy with 81.4% and 85.1% accuracy one and two years before filing, respectively.
Gradient boosted trees outperform other models in predicting corporate bankruptcy.
problem Predicting financial distress of publicly traded U.S. firms.
method Benchmarked various machine learning models using a comprehensive sample of bankruptcies.
result Gradient boosted trees outperform other models in one-year-ahead forecasts.
Paper presents ECL dataset for multi-modal bankruptcy prediction.
problem Developing models to predict corporate bankruptcy using textual and numerical data.
method Used ECL dataset to develop and evaluate classical and neural models.
result Textual and numerical data modalities complement each other for bankruptcy prediction.
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.
Research explores how local communities and corporations interact in finance.
problem Impact of local government subsidies and corporate bankruptcy on bond yields.
method Difference-in-differences analysis, econometric models, deep-learning model.
result Corporate subsidies and bankruptcy filings affect bond yields significantly.
Narrative disclosures in 10-K filings improve bankruptcy prediction beyond accounting ratios.
problem Traditional bankruptcy prediction models rely on accounting ratios, which may not capture early warning signals.
method Developed a PB Stress Score based on distress-specific language in 10-K narratives, evaluated against accounting and dictionary benchmarks.
result Adding the PB Stress Score increases AUC from 0.8323 to 0.9019 and improves top-decile bankruptcy capture from 44.12% to 64.71%.
The paper uses daily bond price data to estimate corporate default spreads, improving credit risk assessment.
problem Outdated credit risk information from quarterly accounting items.
method Adapting classic yield curve estimation methods to corporate bonds, using Bayesian estimation.
result High-frequency credit risk proxy via corporate default spreads improves model stability and prediction uncertainty.
Study uses CNN to analyze images of SMEs for bankruptcy risk.
problem Lack of data for risk analysis of SMEs.
method Created images for each SME, trained CNN on these images.
result CNN achieved 97.8% accuracy in predicting bankruptcy.
This study uses TDA to map corporate failure, revealing distinct regions of risk.
problem Understanding and predicting corporate default risk.
method Topological Data Analysis (TDA) applied to Altman's Z-score model.
result Firms do not cluster neatly along default predictors, suggesting complex risk landscapes.
Paper forecasts corporate default risk using Particle MCMC with expert opinions.
problem Predicting corporate default risk in the U.S. market.
method Bayesian approach with Particle Markov Chain Monte Carlo (Particle MCMC) algorithm.
result Volatility and mean reversion of hidden factor significantly impact default intensities.
Diffusion in a linear potential in the presence of position-dependent killing is used to mimic a default process. Different assumptions regarding transport coefficients, initial conditions, and elasticity of the killing measure lead to diverse models of bankruptcy. One "stylized fact" is fundamental for our considerati…
We have conducted an agent-based simulation of chain bankruptcy. The propagation of credit risk on a network, i.e., chain bankruptcy, is the key to nderstanding largesized bankruptcies. In our model, decrease of revenue by the loss of accounts payable is modeled by an interaction term, and bankruptcy is defined as a ca…
Study explores how labour income impacts optimal bankruptcy strategy.
problem Optimal bankruptcy strategy for agents with varying labour income.
method Solved analytically using the duality method, analyzed sensitivity to cost and benefit parameters.
result Introducing labour income affects optimal strategies, revealing new insights.
Optimizes dividend control in a bankruptcy process using a special Levy process.
problem Optimizing dividend payouts in a bankruptcy process.
method Using a non-standard spectrally negative Levy process with endogenous regime switching.
result Optimal dividend control is of the barrier type and the optimal barrier can be identified.
Study examines how event rate affects bankruptcy prediction model performance.
problem Effect of event rate on bankruptcy prediction model performance.
method Oversampled event rates from 0.12% to 50%, developed and evaluated 7 models.
result Bayesian Network is least sensitive to event rate, SVM most sensitive.
Study finds managers' tenure and education influence their choice between in-court and out-of-court restructuring.
problem Exploring managers' characteristics and their impact on restructuring decisions.
method Empirical investigation using upper echelons theory and data from 342 managers of French firms.
result Managers with longer tenure and higher education levels prefer private restructuring over court involvement.
Study proposes a data-driven CBR system for improved bankruptcy prediction.
problem Lack of interpretability in machine learning models for bankruptcy prediction.
method Data-driven explainable case-based reasoning (CBR) system.
result Proposed CBR system outperforms existing CBR and machine learning models.
Interval bankruptcy problems arise in situations where an estate has to be liquidated among a fixed number of creditors and uncertainty about the amounts of the claims is modeled by intervals. We extend in the interval setting the classical results by Curiel, Maschler and Tijs (1987) that characterize division rules wh…
Research aims to predict fallen angel bonds' bankruptcy using machine learning.
problem Predicting which fallen angel bonds will become investment grade or go bankrupt.
method Used four classification methods (logistic regression, KNN, SVM, NN) and Google Cloud's automated machine learning.
result Google Cloud's machine learning model performed best in over-sampled and feature selection data sets.
Predicting bankruptcy using financial data and news sentiment.
problem Predicting company bankruptcy to mitigate its impact.
method Combining financial data with news sentiment analysis.
result A framework for predicting company bankruptcy.
Study revisits Leland-Toft model with Poisson observation intervals.
problem Optimal capital structure under discrete asset value updates.
method Spectrally negative Lévy model with Poisson observation process.
result Optimal bankruptcy strategy and capital structure derived.
Optimal dividends strategy in a two-state regime-switching environment.
problem Maximizing profits from dividends until bankruptcy in a company with fluctuating cash surplus and regime changes in drift, volatility, and bankruptcy levels.
method Analyzes the optimal dividend payout strategy considering four factors: Brownian fluctuations in cash surplus, regime changes in drift, volatility, and bankruptcy levels.
result Rich structure of the optimal strategy, which can be either barrier-type or liquidation-barrier type, depending on model parameters.
New model uses financial filings to predict bankruptcy, even without MDA sections.
problem Lack of complete MDA data limits traditional bankruptcy prediction models.
method Conditional Multimodal Discriminative (CMMD) model learns from accounting, market, and textual data.
result Empirical results show superior classification performance compared to traditional models.
Study combines intra-risk and contagion risk for SME bankruptcy prediction.
problem Predicting bankruptcy risk of SMEs considering both intra-risk and contagion risk.
method Proposes a novel model using Graph Neural Networks to combine intra-risk and contagion risk.
result Model outperforms state-of-the-art methods in bankruptcy prediction.
The study examines how board diversity and CSR committee composition affect corporate governance and financial performance.
problem The relationship between corporate social responsibility (CSR) and corporate governance.
method Theoretical model development based on management and corporate governance theories, focusing on board diversity and CSR committee composition.
result Cognitive and demographic characteristics of board members provide more insights into the link between corporate governance and CSR.
Hybrid ANN model predicts firm bankruptcy with improved PSO and SA.
problem Predicting firm failure for investors and decision makers.
method Hybrid ANN model using improved PSO and SA for variable selection and optimization.
result The hybrid model outperforms in convergence and accuracy compared to traditional methods.
We model a network economy with three sectors: downstream firms, upstream firms, and banks. Agents are linked by productive and credit relationships so that the behavior of one agent influences the behavior of the others through network connections. Credit interlinkages among agents are a source of bankruptcy diffusion…
Using an exhaustive list of Japanese bankruptcy in 1997, we discover a Zipf law for the distribution of total liabilities of bankrupted firms in high debt range. The life-time of these bankrupted firms has exponential distribution in correlation with entry rate of new firms. We also show that the debt and size are high…
We analyze the size dependence and temporal stability of firm bankruptcy risk in the US economy by applying Zipf scaling techniques. We focus on a single risk factor-the debt-to-asset ratio R-in order to study the stability of the Zipf distribution of R over time. We find that the Zipf exponent increases during market …
This note explores the mathematical theory to solve modern gamblers ruin problems. We establish a ruin framework and solve for the probability of bankruptcy. We also show how this relates to the expected time to bankruptcy and review the risk neutral probabilities associated an adjustment to asymmetrical views.
New method predicts bankruptcy by imputing missing data with granular semantics.
problem Missing data, high dimensional data, and class imbalance in bankruptcy prediction.
method Granular computing for missing data imputation with feature semantics and AI-driven pipeline.
result Efficient solution for big datasets with high imputation rates.
Optimal strategy for insurance company dividends and capital injection with restrictions.
problem Managing dividends and capital injection under a surplus process restriction.
method Singular stochastic control problem with optimal strategies identified.
result Optimal strategies change based on capital injection costs and dividend payout barriers.
Credit estimation and bankruptcy prediction methods have been utilizing Altman's z score method for the last several years. It is reported in many studies that z score is sensitive to changes in accounting figures. Researches have proposed different variations to conventional z score that can improve the predicti…
The optimal capital structure model with endogenous bankruptcy was first studied by Leland (1994) and Leland and Toft (1996), and was later extended to the spectrally negative Levy model by Hilberink and Rogers (2002) and Kyprianou and Surya (2007). This paper incorporates the scale effects by allowing the values of ba…
CCR-CNN uses CNN to predict corporate credit ratings from financial data.
problem Lack of data and limited model performance in predicting corporate credit ratings.
method Transform corporations into images and use CNN to analyze complex feature interactions.
result CCR-CNN outperforms state-of-the-art methods in predicting corporate credit ratings.
Study shows corporate governance improves stock liquidity with noise traders' participation.
problem Improving liquidity of listed companies' stocks.
method Theoretical model with heterogeneity of investors' beliefs.
result Corporate governance and noise traders' participation synergistically improve stock liquidity.
New distress dictionary improves bankruptcy prediction from disclosure text.
problem Bankruptcy prediction from financial disclosures.
method Proposes a distress dictionary based on managers' sentences, quantifies linguistic features, and builds predictive models.
result Predictive models based on the distress dictionary outperform existing methods.
Model predicts Mozambique bank failures, aiding risk management.
problem Lack of bankruptcy prediction model in Mozambique banking sector.
method Linear Discriminant Analysis method, using financial indicators.
result Model accurately predicted 84% of bank failures 1 year before Central Bank intervention.
Paper introduces a benchmark for predicting bankruptcy from text data.
problem Lack of a common benchmark dataset and evaluation strategy for unstructured data in bankruptcy prediction.
method Describes and evaluates several baseline models, including a bag-of-words model.
result A lightweight bag-of-words model performs surprisingly well, especially when considering data from multiple years.
Corporate venture capital is in the midst of a renaissance. The end of 2015 marked all-time highs both in the number of corporate firms participating in VC deals and in the amount of capital being deployed by corporate VCs. This paper explores, rather than defines, how these firms find success in the wake of this sudde…
Framework integrates financial and annual report data for better corporate credit ratings.
problem Lack of insights from non-financial data in credit rating models.
method Uses FinBERT to extract features from annual reports and combines them with financial data.
result Improves credit rating accuracy by 8-12%.
Study finds it hard to establish common factor pricing in corporate bonds.
problem Difficulty in establishing common factor pricing in corporate bonds.
method Portfolio- and bond-level analyses using multifactor models.
result Common factor pricing in corporate bonds is not significantly explanatory.
CAI automates extraction and validation of corporate GHG emission metrics.
problem Manual extraction of corporate GHG emission metrics is labor-intensive and error-prone.
method CAI uses LLMs to automate extraction and validation of metrics from corporate disclosures.
result CAI improves data collection efficiency and accuracy by automating the process.
Study finds corporate boards with women appoint more women, leading to better profitability.
problem Influence of female board members on corporate profitability.
method Analysis of Japanese corporate boards and their interlocks.
result Corporate boards with women appoint more women, leading to higher profitability.
We introduce a model in which a regulator employs mechanism design to embed her human capital beta signal(s) in a firm's capital structure, in order to enhance the value of her post career change indexed executive stock option contract with the firm. We prove that the agency cost of this revolving door behavior increas…
Study examines UK firms' financial performance linked to corporate governance.
problem Impact of corporate governance on UK firms' financial performance.
method Cross-sectional regression analysis of 252 firms in 2014.
result Corporate governance mechanisms have mixed effects on financial performance.
New model prices collateralized financial derivatives considering bankruptcy laws.
problem Inaccurate pricing of collateralized financial derivatives due to neglecting collateral and credit risk.
method Developed a new model that incorporates both collateral posting and credit risk.
result Proper accounting for collateralization is crucial for accurate pricing of financial derivatives.
A simplified model for factoring pricing considering assignor's creditworthiness.
problem Factoring pricing considering both debtor and assignor's creditworthiness.
method Modeling factoring pricing with consideration of assignor's default impact.
result The model shows how assignor's default can impact factoring payoffs.