A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
FinTradeBench benchmarks LLMs for financial reasoning combining company fundamentals and market signals.
AI uses KGs to assess economic impact of selective lockdowns on Italian companies.
Develops Merton's model for private companies using DDM.
The problem of detecting terms that can be interesting to the advertiser is considered. If a company has already bought some advertising terms which describe certain services, it is reasonable to find out the terms bought by competing companies. A part of them can be recommended as future advertising terms to the compa…
This paper develops a valuation model for private companies.
This paper considers an optimal control of a big financial company with debt liability under bankrupt probability constraints. The company, which faces constant liability payments and has choices to choose various production/business policies from an available set of control policies with different expected profits and…
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 …
Analyzes how inclusion/exclusion from STOXX Europe 600 Index affects company prices.
Predicting bankruptcy using financial data and news sentiment.
In order to study the phenomenon in detail that income distribution follows Pareto law, we analyze the database of high income companies in Japan. We find a quantitative relation between the average capital of the companies and the Pareto index. The larger the average capital becomes, the smaller the Pareto index becom…
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…
FinReflectKG benchmarks financial QA by linking relevant context from a financial KG, improving model performance and efficiency.
The paper proposes an original methodology for constructing quantitative statistical models based on multidimensional distribution functions constructed on the basis of the insurance companies' data on inshurance policies (including policies with deductible) and claims incurred. Real data of some Russian insurance comp…
Within the Private Equity (PE) market, the event of a private company undertaking an Initial Public Offering (IPO) is usually a very high-return one for the investors in the company. For this reason, an effective predictive model for the IPO event is considered as a valuable tool in the PE market, an endeavor in which …
UK hosts 62.89% of all HYIPs, many registered as 'limited company'.
Method identifies potential customers from limited data.
UniFinEval benchmarks financial models across text, images, and videos.
We statistically investigate the distribution of share price and the distributions of three common financial indicators using data from approximately 8,000 companies publicly listed worldwide for the period 2004-2013. We find that the distribution of share price follows Zipf's law; that is, it can be approximated by a …
A new explainable CBR system predicts financial risks with interpretability and good performance.
New model uses financial filings to predict bankruptcy, even without MDA sections.
Business taxonomies are indispensable tools for investors to do equity research and make professional decisions. However, to identify the structure of industry sectors in an emerging market is challenging for two reasons. First, existing taxonomies are designed for mature markets, which may not be the appropriate class…
This paper uses CNN-LSTM to predict stock market performance.
Proposes a new method to explain model predictions for consumer recourse.
Paper uses LLMs for financial forecasting, overcoming sequence reasoning and multi-modal challenges.
DBOT uses AI to automate long-term stock valuation.
Study proposes a data-driven CBR system for improved bankruptcy prediction.
FinMaster benchmarks LLMs in financial tasks, revealing gaps in reasoning.
Study finds similar companies in Dhaka Stock Exchange using technical data.
Developed Merton's model for public companies using observed liabilities.
Company2Vec creates embeddings from company websites for fine-grained business analytics.
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.
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 …
Strategic Workforce Planning is a company process providing best in class, economically sound, workforce management policies and goals. Despite the abundance of literature on the subject, this is a notorious challenge in terms of implementation. Reasons span from the youth of the field itself to broader data integratio…
We propose an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test the…
The paper analyzes how news sentiment of companies can affect market movements.
Large language models learn company embeddings from SEC filings.
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…
A blindfolded LLM trading framework validates market signals without ticker memorization.
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…
A new method to value IPOed companies.
Study compares sentiment spillover networks from news and social media in tech companies.
Audit fees change based on company and economic factors during auditor switching.
RAG-IT automates financial analysis using LLMs and specialized datasets.
We consider the problem of evaluating the quality of startup companies. This can be quite challenging due to the rarity of successful startup companies and the complexity of factors which impact such success. In this work we collect data on tens of thousands of startup companies, their performance, the backgrounds of t…
Model predicts default risk based on company's financial forecasts and credit conditions.
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…
To understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies, we define an algorithm for measuring sentiment-based network risk. The algorithm ranks companies in networks of co-occurrences, and measures sentiment-based risk, by calculatin…