Employing profits data of Japanese companies in 2002 and 2003, we confirm that Pareto's law and the Pareto index are derived from the law of detailed balance and Gibrat's law. The last two laws are observed beyond the region where Pareto's law holds. By classifying companies into job categories, we find that companies …
We introduce a model for the adaptive evolution of a network of company ownerships. In a recent work it has been shown that the empirical global network of corporate control is marked by a central, tightly connected "core" made of a small number of large companies which control a significant part of the global economy.…
Automates investor/company matching with AI, explaining decisions.
problem Matching companies and investors is hard due to limited data and need for explanations.
method Representation learning for small datasets + parameterized explanation generation.
result System performs well on matching task and explains decisions.
Following the work of Okuyama, Takayasu and Takayasu [Okuyama, Takayasu and Takayasu 1999] we analyze huge databases of Japanese companies' financial figures and confirm that the Zipf's law, a power law distribution with the exponent -1, has been maintained over 30 years in the income distribution of Japanese companies…
Study reveals cash flow data is non-linear, impacting forecasting methods.
problem Assumptions of normality, correlation, and stationarity for daily cash flows are inaccurate.
method Comprehensive empirical analysis of real-world cash flow data from small and medium companies.
result Non-linearity is crucial for forecasting cash flows, challenging traditional methods.
Study examines how COVID-19 vaccine companies' popularity affects their stock prices.
problem Impact of COVID-19 vaccine development and rollout on stock prices and company popularity.
method Used Python and various libraries to analyze Google Trends data and stock prices of five vaccine companies.
result Significant correlation between Google Trend data and stock prices, with post-rollout periods showing a slight negative correlation.
As the number of publicly traded companies as well as the amount of their financial data grows rapidly, it is highly desired to have tracking, analysis, and eventually stock selections automated. There have been few works focusing on estimating the stock prices of individual companies. However, many of those have worke…
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 assesses e-readiness of Mexican SMEs and proposes a model.
problem Small and medium enterprises in Mexico struggle with e-business adoption.
method Survey research design, statistical analysis, adoption models.
result Data will be collected and analyzed to assess e-readiness and propose recommendations.
Machine learning predicts greenhouse gas emissions for undisclosed companies.
problem Lack of GHG emissions data for most companies.
method Trained machine learning model on disclosed data to estimate emissions.
result Model accurately predicts emissions for undisclosed companies.
We analyze the sequence of time intervals between consecutive stock trades of thirty companies representing eight sectors of the U. S. economy over a period of four years. For all companies we find that: (i) the probability density function of intertrade times may be fit by a Weibull distribution; (ii) when appropriate…
Study identifies key ESG variables for assessing financial risk.
problem Assessing financial risk from ESG data with many variables.
method Proposed framework for hierarchical ESG data, selecting relevant variables.
result Selected ESG variables are more relevant to financial risk than aggregated scores.
In this paper we consider an information theoretic approach for the accounting classification process. We propose a matrix formalism and an algorithm for calculations of information theoretic measures associated to accounting classification. The formalism may be useful for further generalizations and computer-based imp…
Paper examines how crypto-assets affect corporate governance of SMEs and public companies.
problem Impact of crypto-assets on corporate governance of SMEs and public companies.
method Analyzes various use cases of DLT technology and their effects on corporate governance.
result New stakeholders (crypto-assets holders) change governance of SMEs and public companies.
We investigate the problem of optimal dividend distribution for a company in the presence of regime shifts. We consider a company whose cumulative net revenues evolve as a Brownian motion with positive drift that is modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the…
Study on insurance risk management and sustainable development.
problem Lack of attention to non-climate change aspects of sustainable development in insurance.
method Analysis of recent developments and legislative initiatives in insurance risk management.
result Strategies for small- and medium-sized enterprises to manage sustainable development risks.
This thesis identifies share buybacks and predicts their impact on stock performance.
problem Recognizing and predicting the impact of share buybacks on stock performance.
method NLP approaches for automated detection of share buybacks, machine learning models for prediction.
result Most companies underperform after a share buyback, but some significantly outperform.
Optimizes dividend payments to balance risk and reward.
problem Maximizing mean-variance of accumulated discounted dividends up to ruin.
method Develops a time-consistent equilibrium strategy using a verification lemma.
result Equilibrium strategy is a barrier strategy for low risk aversion.
We study a credit risk model which captures effects of economic interactions on a firm's default probability. Economic interactions are represented as a functionally defined graph, and the existence of both cooperative, and competitive, business relations is taken into account. We provide an analytic solution of the mo…
Predicts stock price changes based on clinical trial announcements.
problem Forecasting the impact of clinical trial results on pharma stock prices.
method BERT for sentiment analysis, Temporal Fusion Transformer for forecasting, graph convolution network for event relationships, gradient boosting for price change prediction.
result Identifies two crucial factors: drug portfolio size and network effect of related events.
Study finds similar companies in Dhaka Stock Exchange using technical data.
problem Analyzing all companies in Dhaka Stock Exchange is impractical.
method Used technical data to identify companies moving together.
result Technical data can reveal company relationships without fundamental data.
A geometric analysis of the time series of returns has been performed in the past and it implied that the most of the systematic information of the market is contained in a space of small dimension. Here we have explored subspaces of this space to find out the relative performance of portfolios formed from the companie…
Study tests financial market efficiency using random number generator tests.
problem Check for informational efficiencies in financial markets.
method Analysed binary daily returns as random number generators, split analysis by annual and company levels, investigated longer-term efficiency over Nasdaq-listed companies.
result Information efficiency varies across years and reflects large-scale market impacts.
Developed Merton's model for public companies using observed liabilities.
problem Estimating default risk for public companies.
method Campbell and Shiller's approximation method for risk-neutral values and default probabilities.
result Formulas and ML estimators for public companies' default probabilities.
Company2Vec creates embeddings from company websites for fine-grained business analytics.
problem Lack of fine-grained company labels for analytics.
method Word2Vec and dimensionality reduction on company website data.
result Semantic company embeddings for various applications.
Develops Merton's model for private companies using DDM.
problem Lack of observable asset values for private companies.
method Uses dividend discount model (DDM) to develop structural model.
result Obtains closed-form formulas for equity and liability values, default probability.
This paper develops a valuation model for private companies.
problem Lack of pricing and hedging models for private companies.
method Dynamic Gordon growth model, Maximum Likelihood (ML) estimators, Expectation Maximization (EM) algorithm.
result Closed-form pricing and hedging formulas for private companies.
We present an analytical study of an insurance company. We model the company's performance on a statistical basis and evaluate the predicted annual income of the company in terms of insurance parameters namely the premium, total number of the insured, average loss claims etc. We restrict ourselves to a single insurance…
This paper evaluates financial competitiveness of Indian real estate companies using entropy method.
problem Improving financial competitiveness of Indian real estate companies in a competitive market.
method Financial competitiveness evaluation index system using key financial ratios and a scoring system.
result Companies with high scores have strong profitability and operational capacity, while those with lower scores struggle with solvency and working capital.
Investment level and timing predict SME performance, especially during financial crises.
problem Estimating effective performance measurement systems for SMEs.
method Extreme value statistics applied to TTA and financial indicators.
result Low but growing TTA is key to positive SME performance during crises.
The article proposes a dynamic model for a company's life cycle under competitive influence.
problem Modeling a company's life cycle in a competitive environment.
method Utilized Markov model with known action costs and transition probabilities, affected by outside factors.
result Demonstrates the usefulness of the model in determining future actions of a company.
Algorithm measures sentiment-based network risk in companies.
problem Understanding the relationship between news sentiment and stock price movements.
method Algorithm ranks companies based on news sentiment and co-occurrences, calculating individual and aggregated risks.
result The highest quarterly risk value correlates with a higher chance of stock price decline up to 70 days later.
The paper analyzes how news sentiment of companies can affect market movements.
problem Understanding how news sentiment impacts market performance and volatility.
method Applied NLP techniques to analyze news sentiment of 87 companies over 7 years.
result Strong media sentiment towards one company can indicate significant changes in sentiment towards related companies.
Large language models learn company embeddings from SEC filings.
problem Lack of a rigorous definition of company similarity.
method Pre-trained and finetuned large language models (LLMs) to learn embeddings from SEC filings.
result LLMs can reproduce GICS classifications and indicate similar financial performance.
In a stock market, the price fluctuations are interactive, that is, one listed company can influence others. In this paper, we seek to study the influence relationships among listed companies by constructing a directed network on the basis of Chinese stock market. This influence network shows distinct topological prope…
The model is aimed to discriminate the 'good' and the 'bad' companies in Russian corporate sector based on their financial statements data based on Russian Accounting Standards. The data sample consists of 126 Russian public companies- issuers of Ruble bonds which represent about 36% of total number of corporate bonds …
In this study we consider relations between companies in Poland taking into account common branches they belong to. It is clear that companies belonging to the same branch compete for similar customers, so the market induces correlations between them. On the other hand two branches can be related by companies acting in…
Study compares sentiment spillover networks from news and social media in tech companies.
problem Understanding how sentiment information flows between companies through news and social media.
method Network-based transfer entropy method to measure and compare sentiment spillover.
result News shows stronger information flow among tech companies after COVID-19.
New method builds business taxonomies from corporate reports.
problem Challenging to classify emerging market industries.
method Concept-level hierarchical clustering of annual reports.
result Automatic construction of business taxonomies.
A new method to value IPOed companies.
problem Valuing companies after IPO.
method Growth Average U1 method.
result Benchmark stocks using linear extrapolation of revenues and profits.
Audit fees change based on company and economic factors during auditor switching.
problem Understanding how audit fees change when auditors switch firms.
method Examined the impact of auditor switching on audit fees, considering company characteristics and economic data.
result The direction and magnitude of audit fee changes during switching depend on economic stability and company characteristics.
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.
Model predicts default risk based on company's financial forecasts and credit conditions.
problem Estimating the risk of a company defaulting on its financial obligations.
method Developed an equilibrium model linking interest rates to corporate performance and credit supply.
result Estimates idiosyncratic default risk and provides forward-looking probability of default (PD).
The real estate is a pillar industry of China's national economy. Due to changes in policy and market conditions, the real estate companies are facing greater pressures to survive in a competitive environment. They must improve their financial competitiveness. Based on the conceptual framework of financial competitiven…
Deep learning models outperform classical methods in forecasting company fundamentals.
problem Forecasting company fundamentals for investment and econometrics.
method Compared 24 deterministic and probabilistic models on real company data.
result Deep learning models provide superior forecasting performance, especially in uncertainty estimation.
Study shows activist board representation improves Japanese companies' performance.
problem Lack of innovation and improvement in Japanese companies.
method Examined two Japanese companies with activist board representation, analyzing performance metrics.
result Companies with activist board representation experienced significant improvements in stock returns and operational metrics.
We first estimate the average growth of a company's annual income and its variance by using both real company data and a numerical model which we already introduced a couple of years ago. Investment strategies expecting for income growth is evaluated based on the numerical model. Our numerical simulation suggests the p…
Deep learning predicts customer churn from abstract features.
problem Predicting customer churn in subscription-based companies.
method Unsupervised feature learning using deep neural networks on abstract feature vectors.
result Deep learning achieves excellent churn prediction performance across different companies.