The paper forecasts corporate distress using a novel MIDAS logistic regression method.
problem Forecasting corporate distress with right-censored data, high-dimensional predictors, and mixed-frequency data.
method The paper introduces a novel high-dimensional censored MIDAS logistic regression method that handles censoring through inverse probability weighting and employs a sparse-group penalty for mixed-frequency predictors.
result The method achieves accurate estimation and superior performance in predicting financial distress of Chinese-listed firms.
Corporate distress models typically only employ the numerical financial variables in the firms' annual reports. We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements. Our model consists of a convolutional recurrent neural network w…
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.
This study proposes a new model for predicting financial distress in SMEs using machine learning.
problem Challenges in predicting financial distress for SMEs due to ambiguity and limited data.
method Feature selection algorithm based on element credits and data source collection. Incorporates financial statements, governance qualities, and market data with a Relevant Vector Machine.
result The proposed model improves financial distress prediction efficiency with fewer characteristic factors.
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 introduces a new index to measure financial and workplace resilience of firms.
problem Corporate resilience and its types in turbulent markets.
method Quantitative analysis of earnings expectations and implied discount rates.
result Evidence of workplace resilience amplification by financial status in the COVID-19 era.
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%.
We introduce a dynamic optimization framework to analyze optimal portfolio allocations within an information driven contagious distress model. The investor allocates his wealth across several stocks whose growth rates and distress intensities are driven by a hidden Markov chain, and also influenced by the distress stat…
Paper uses machine learning to estimate IRI from pavement distress types, densities, and severities.
problem Costly IRI measurements exclude many road classes; estimating IRI from distress data is needed.
method Data from in-service pavements; machine learning methods used to predict IRI.
result Machine learning can reliably estimate IRI based on distress types, densities, and severities.
Study develops a new tool for assessing asphalt pavement conditions using deep learning.
problem Challenges in automated pavement distress detection via road images.
method Developed a hybrid model using YOLO for classification and U-net for segmentation, creating a comprehensive pavement condition tool.
result Created a new asphalt pavement condition index using deep learning.
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.
We report a study of a stylized banking cascade model investigating systemic risk caused by counter party failure using liabilities and assets to define banks' balance sheet. In our stylized system, banks can be in two states: normally operating or distressed and the state of a bank changes from normally operating to d…
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.
Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community. Recent developments in deep learning has led to considerable research activity directed towards improving the efficacy of automated pavement distress identification and rating. Deep learning …
Federated learning predicts financial distress across U.S. states without centralizing data.
problem Predicting financial distress across U.S. states using sensitive data without centralization.
method Cross-silo federated learning, interpretable AI techniques, machine learning model for categorical data.
result Identifies both global and state-specific predictors of financial hardship.
Model shows how banks' fears of future defaults can cause immediate financial stress.
problem How banks' future default worries cause immediate financial stress.
method Dynamic interbank model with endogenous distress contagion, mark-to-market valuation adjustment, forward-backward approach.
result Distress contagion acts as a stochastic volatility term leading to clustering and down-market spikes.
We consider a dynamical model of distress propagation on complex networks, which we apply to the study of financial contagion in networks of banks connected to each other by direct exposures. The model that we consider is an extension of the DebtRank algorithm, recently introduced in the literature. The mechanics of di…
We discuss a simple, exactly solvable model of stochastic stock dynamics that incorporates regime switching between healthy and distressed regimes. Using this model, which is analytically tractable, we discuss a way of extracting expected returns for stocks from realized CDS spreads, essentially, the CDS market sentime…
New risk measures assess cryptocurrency market vulnerabilities during financial distress.
problem Capturing systemic risk in cryptocurrency markets during financial distress.
method Introducing Vulnerability Conditional Risk Measures (VCoES) and related measures.
result Validated theoretical insights and demonstrated practical relevance in cryptocurrency market.
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.
In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to tex…
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.
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.
In the context of the current financial crisis, when more companies are facing bankruptcy or insolvency, the paper aims to find methods to identify distressed firms by using financial ratios. The study will focus on identifying a group of Romanian listed companies, for which financial data for the year 2008 were availa…
News is a pertinent source of information on financial risks and stress factors, which nevertheless is challenging to harness due to the sparse and unstructured nature of natural text. We propose an approach based on distributional semantics and deep learning with neural networks to model and link text to a scarce set …
The VIX is used to model corporate bond volatility and returns.
problem Modeling volatility and returns for corporate bonds using observable data.
method Applied stochastic volatility models using the VIX index to corporate bond rates and spreads.
result Residuals of corporate bond returns divided by VIX are closer to Gaussian white noise.
Large corporate credit models may be adapted for small business risk assessment.
problem Limited data and lack of credit analysts for small businesses.
method Adapting large corporate credit risk models for small businesses.
result Adapted models can predict small business credit risk effectively.
The European sovereign debt crisis has impaired many European banks. The distress on the European banks may transmit worldwide, and result in a large-scale knock-on default of financial institutions. This study presents a computer simulation model to analyze the risk of insolvency of banks and defaults in a bank credit…
AI analyzes corporate ESG filings to identify key dimensions and investor reactions.
problem Lack of reliable ESG ratings systems in corporate filings.
method AI techniques to separate and measure ESG dimensions and investor responses.
result AI can improve ESG ratings systems by identifying key dimensions and investor reactions.
Religious adherence reduces corporate greenwashing behavior.
problem Greenwashing behavior by corporations.
method Analysis of a large US firm sample (2005-2019), focusing on selective disclosure.
result Religious adherence correlates with lower greenwashing behavior.
Develops a new model to better predict corporate bond yields.
problem Persistent shifts in interest rates undermine single-regime models.
method Regime-switching generalized CIR model with two-state short-rate process and credit factors.
result The model improves joint curve fit and delivers interpretable probabilities.
Traded corporations are required by law to have a majority of outside directors on their board. This requirement allows the existence of directors who sit on the board of two or more corporations at the same time, generating what is commonly known as interlocking directorates. While research has shown that networks of …
In a market system, regulations are designed to prevent or rectify market failures that inhibit fair exchange, such as monopoly or transactions with hidden costs. Because regulations reduce profits to those possessing unfair advantage, these advantaged corporations (whether individuals, companies, or other collective o…
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.
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.
Paper proposes a new trading strategy using corporate event detection from news articles.
problem Predicting stock movements based on corporate events from news articles.
method Bi-level event detection model: low-level for token-level event identification, high-level for article-level event identification.
result The proposed strategy outperforms existing models in stock prediction metrics.
New measures detect asymmetries, non-linearity in stock returns.
problem Detecting asymmetries and non-linearity in stock returns.
method Proposed non-linear, local, invariant dependence measures; nonparametric estimator proven.
result Measures show tail asymmetry, non-linearity, risk buildup during market distress.
China integrates ESG into corporate strategy for sustainable growth.
problem Corporate focus on short-term financial metrics.
method Deep integration of ESG principles into corporate culture and strategy.
result Companies are expected to fulfill social responsibilities and create long-term value.
The paper models systemic risk in European and U.S. banks using factor copulas.
problem Modeling the joint and conditional distress probabilities of banks across Europe and the U.S.
method Employing Credit Default Swaps (CDS) and factor copulas, the paper proposes multi-factor, structured factor, and factor-vine models.
result Systematic contagion channel drives distress probabilities in the banking system as a whole, while regional factors are important within each region.
A new portfolio optimization model minimizes maximum drawdown, offering faster and more robust solutions.
problem Optimizing portfolios during financial distress, especially during crises.
method Linearization of Markowitz model based on maximum drawdown, with a Mixed-Integer Linear Programming variation.
result 200 times faster solving time with a more profitable and robust solution.
In this three-part series of papers, we argue that the conventional spread measures are not well defined for credit-risky bonds and introduce a set of credit term structures which correct for the biases associated with the strippable cash flow valuation assumption. We demonstrate that the resulting estimates are signif…
Corporate bond factor research is flawed due to measurement errors and ex-post filtering.
problem Replication crisis in corporate bond factor research.
method Analysis of 108 signals across nine thematic clusters, correction of transaction prices and return filtering.
result Majority of previously documented factors do not produce statistically significant alphas after correction.
Corporate transparency reduces investors' disposition effect by increasing confidence in holding profitable and losing stocks.
problem Irrational disposition effect in investors selling profitable assets too soon and holding onto losing assets for too long.
method Examined the impact of corporate transparency on individual investors' disposition effect.
result Increased corporate transparency significantly reduces the disposition effect.