Paper presents a faster method for computing cost of equity and performing comparable company analysis.
problem Tedium and subjectivity in traditional cost of equity and comparable company analysis methods.
method Uses spectral and agglomerative clustering to compute cost of equity and perform comparable company analysis.
result Reduces time required for comps by orders of magnitude and improves consistency and reliability.
Study compares LSTM models with sentiment analysis for stock price prediction.
problem Efficient stock price prediction models using LSTM with sentiment analysis.
method Various types of LSTM models combined with sentiment analysis.
result Identifies the most effective model for short and long-term stock price prediction.
Study evaluates if LLMs have company-specific biases in financial sentiment analysis.
problem Evaluating if large language models exhibit company-specific biases in financial sentiment analysis.
method Comparing sentiment scores with and without company names, constructing economic models, and empirical analysis.
result LLMs show company-specific biases in sentiment analysis, impacting investor behavior and stock prices.
This paper analyzes stock market data to predict share prices using regression models.
problem Predicting stock prices in the share market of Bangladesh.
method Thorough linear regression analysis on Dhaka Stock Exchange data, compared with random forest.
result Random forest model performs better than linear regression for predicting stock prices.
Study uses web search data to analyze tech startups growth.
problem Analyzing growth dynamics of tech startups.
method Utilized Google Trends data for 241 US-based tech startups.
result Web search traffic correlates positively with tech startup growth.
Study compares ruin probabilities under independence vs. dependence assumptions.
problem Underestimation of ruin probability when claims are dependent.
method Copulas for claim dependence analysis, sensitivity analysis.
result Dependent claims lead to underestimation of ruin probability.
This paper analyzes the connection between innovation activities of companies -- implemented before crisis -- and their performance -- measured at time of crisis. The companies listed in the STAR Market Segment of the Italian Stock Exchange are analyzed. Innovation is measured through the level of investments in total …
Analyzes ESG impact on stock market performance using social media and news data.
problem Understanding the impact of ESG news on stock market performance.
method Summarized live ESG data from social media and news, created sentiment index, calculated stock price changes, and compared sentiment to performance.
result ESG sentiment correlates with stock price changes, indicating its impact on market performance.
The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.
problem Mapping financial, environmental, and social performance of Spanish processing companies.
method Used compositional data and principal-component analysis biplot for statistical analysis.
result Identified clusters of companies with similar financial, environmental, and social performance.
The paper analyzes market risk factors for a mining company using a VAR model with stable distribution.
problem Understanding mid- and long-term dynamics of market risk factors for a mining company.
method Two-dimensional vector autoregressive (VAR) model with α-stable distribution, identifying two regimes.
result Derives dynamics of copper price in PLN, crucial for company risk exposure.
Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each sto…
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.
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…
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.
This work models the interconnection of company's investment managers' representations and the market attraction of its shares. The models that reflect the connection of the company's market effectiveness indices and parameters of its economic activity are created on the basis of the Mean-Variance Analysis and Regressi…
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.
Anonymizing company names in financial news improves trading performance, contrary to initial expectations.
problem Look-ahead and distraction biases in sentiment analysis of financial news.
method Investigated trading strategies based on original and anonymized headlines, comparing performance.
result Anonymized headlines outperform original in-sample, suggesting distraction effect is stronger.
Study introduces new financial ratios for better predicting company performance.
problem Lack of progress in predicting company performance and assessing financial risks.
method Developed new financial and macroeconomic ratios, supervised learning models, and Bayesian models.
result New proposed variables improve model accuracy and FNN performs best across multiple tasks.
The paper analyzes how companies' investments before crises affect their performance after crises.
problem Understanding how companies' investments before financial crises impact their performance afterward.
method Cluster analysis using Voronoi tessellation with statistical outliers identified.
result Positive investments before crises are associated with better performance after crises.
In terms of transfer entropy, we investigated the strength and the direction of information transfer in the US stock market. Through the directionality of the information transfer, the more influential company between the correlated ones can be found and also the market leading companies are selected. Our entropy analy…
Study analyzes fintech terms in news and blogs, revealing specialized attributes of fintech companies.
problem Understanding specialized attributes of fintech companies through term analysis.
method Large scale analysis of fintech terms in news and blogs, using complex networks and statistically validated networks.
result Companies with fintech terms have over-expressions of specific attributes related to geography and economy.
The paper monitors stock market relationships using network analysis and statistical control charts.
problem Detecting abnormal changes in the financial market network structure.
method Network construction using distance methods, hierarchical clustering, and Shewhart control charts.
result Abnormal changes in financial market relationships can be detected using statistical process control.
New machine learning method classifies companies effectively.
problem Classifying companies for financial analysis.
method Unsupervised machine learning with t-SNE and spectral clustering.
result Improved portfolio performance through better company classification.
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…
Out of the companies, Dolby is the company with the best overall financial and operation health. According to the table that accounted its financial statements for the past three years, Dolby has stable profit margins that generates a revenue in the billions, the only company in ten figures. Corporate competition to ga…
Study uses LLMs to generate investor briefs from company reports and SEC filings.
problem Improving data analysis for individual investors.
method Preprocessed data, used gpt-4o model in RAG regime, evaluated by investors.
result LLMs can generate useful investor briefs from company reports and SEC filings.
Based on a point of view that solvency and security are first, this paper considers regular-singular stochastic optimal control problem of a large insurance company facing positive transaction cost asked by reinsurer under solvency constraint. The company controls proportional reinsurance and dividend pay-out policy to…
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.
Modeling government intervention's impact on company market technology growth.
problem Understanding how governments influence technology growth and innovation diffusion.
method Proposed a simple extension of TGID models, incorporating a government intervention parameter.
result High government intervention can destabilize market development, lowering technology levels.
Study examines stock price correlations between Indonesian holding companies and their subsidiaries.
problem Understanding stock price relationships between holding companies and their subsidiaries.
method Spearman correlation analysis over 2013-2022, focusing on MNC Group and Emtek Group.
result Varying degrees of correlation between holding companies and their subsidiaries, with some showing inverse relationships.
We present a model of an economy inspired by individual based model approaches in evolutionary ecology. We demonstrate that evolutionary dynamics in a space of companies interconnected through a correlated interaction matrix produces time dependencies of the total size of the economy total number of companies, companie…
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 pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial ti…
In this paper we consider a utility maximization problem with defaultable stocks and looping contagion risk. We assume that the default intensity of one company depends on the stock prices of itself and other companies, and the default of the company induces immediate drops in the stock prices of the surviving companie…
RAG-IT automates financial analysis using LLMs and specialized datasets.
problem Manual financial analysis is time-consuming and requires expertise.
method Retrieval-Augmented Instruction Tuning (RAG-IT) fine-tunes an LLM for financial tasks.
result RAG-IT improves financial report generation performance compared to commercial systems.
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…
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…
Study reveals clusters of resilient and vulnerable Spanish agri-food firms post-Ukraine-Russia war.
problem Financial resilience of agri-food companies in Spain during the Ukraine-Russia conflict.
method Cluster analysis using centred log-ratios for compositional data of financial ratios.
result Increase in resilient firms by 2023, highlighting sectoral adaptation to economic challenges.
Study on implied certainty equivalent rates in financial markets and electric vehicles.
problem Investment risk in financial markets.
method Mathematical derivation of implied certainty equivalent rate, empirical analysis of stock and option data.
result Positive implied certainty equivalent rates are more suitable for investment than negative ones, but higher values increase risk.
Model estimates non-reported GHG emissions for companies using machine learning.
problem Incomplete GHG emissions reporting by companies.
method Interpretable machine learning model tailored for non-reporting companies.
result Model accurately estimates emissions for diverse company groups.
We consider a two-dimensional optimal dividend problem in the context of two insurance companies with compound Poisson surplus processes, who collaborate by paying each other's deficit when possible. We solve the stochastic control problem of maximizing the weighted sum of expected discounted dividend payments (among a…
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.
Python models predict stock sentiment for market-beating returns.
problem Predicting public sentiment for stock trading.
method Crowd-sourced labeled data, trained and evaluated various models.
result Best models predict market-beating returns from public sentiment.
Neural model learns company embeddings from data and news.
problem Subjective industry classification schemes in finance.
method Multimodal neural model training company embeddings.
result Objective company representations capture nuanced relationships.
Study clusters Kenyan medical insurance companies based on financial performance and reporting consistency.
problem Identifying financial health and reporting consistency in Kenyan medical insurance companies.
method Advanced clustering techniques (KMeans, DTW) on financial ratios and time series data.
result Four distinct clusters identified, each representing different financial performance and reporting consistency combinations.
Analyzes premium data of Indian non-life insurers, finding GEV distribution best fits Lognormal and GEV extremes.
problem Modeling premiums of non-life insurance companies in India.
method Empirical analysis using Lognormal, GEV, and GPD distributions.
result Generalized Extreme Value distribution best fits premium data for ten Indian non-life insurers.
Research shows franchised fast food companies' stock prices decline more during recessions.
problem Impact of recession on franchised fast food companies' stock prices.
method Analyzed stock price data with Weibull distribution.
result Recessions have a more severe impact on franchised fast food companies' stock prices.
Dataset analyzes tweets' impact on stock returns.
problem Understanding how public opinion affects stock market outcomes.
method Created a dataset of 862,231 labeled tweets, provided baselines and multi-view learning approach.
result Demonstrated the impact of tweets on stock returns over various time frames.