Crowdsourced data science competitions improve social sector decision-making.
problem Improving decision-making in social sector organizations.
method Data science competitions to solve specific problems.
result Competition models accurately predict social sector outcomes.
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.
Study investor sentiment and disagreement on StockTwits during COVID-19.
problem Understanding investor beliefs and sentiment during the pandemic.
method Analysis of social media data (StockTwits) for investor messages.
result Sentiment and disagreement sharply decreased in early March 2020, followed by a reversal.
Study examines how social media sentiment impacts biotech stocks.
problem Understanding the impact of social media on biotech stock prices.
method VADER sentiment analysis, ARIMA, and VAR models were used to forecast stock market performance.
result Complex interplay between tweet sentiment and stock market performance was identified.
ICT intensification impacts employment across sectors in India.
problem Impact of ICT on employment in Indian sectors.
method Analysis of ICT investment intensity across sectors.
result ICT intensification correlates with employment changes across sectors.
Study tracks cryptocurrency news on social media.
problem Real-time tracking of relevant cryptocurrency news.
method Matched web news with social media tweets, analyzed tweet activity, and used machine learning models.
result Random forest autoregressive model performs well in predicting article mentions.
Study examines cyber losses across sectors, finds high severity and frequency.
problem Understanding the nature of cyber losses and their variability across sectors.
method Analysis of a leading industry dataset of cyber events, focusing on frequency and severity.
result Cyber risks are heavy-tailed, with high probability of extreme losses.
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.
Agent-based model uses SAM to create realistic economic system.
problem Lack of tools to understand and predict economic crises.
method Agent-based modeling (ABM) with Social Accounting Matrix (SAM) calibration.
result ABM can produce economic systems close to real-world data.
The study explores when it's best to remove a real estate broker from the process.
problem Optimal conditions for removing a real estate broker.
method New models analyzing information asymmetry, social capital, and contract types.
result Dis-intermediation can be optimal under certain conditions.
Predicts stock volatility using Twitter data and random forests.
problem Predicting stock implied volatility using Twitter data.
method Random forests with ablation study on different predictors, including Twitter attention and sentiment features.
result Certain sectors like Consumer Discretionary, Technology, Real Estate, and Utilities are easier to predict.
AI monitors social distancing and masks at manufacturing plants.
problem Ensuring safety of workers during post-COVID production.
method Computer vision and AI techniques for social distancing and mask detection.
result Real-time alerts prevent violations of social distancing and mask-wearing.
Improved volatility forecasts for U.S. stocks using social media and news data.
problem Challenges in forecasting equity market volatility due to infrequency and variability of macroeconomic announcements.
method Estimating public attention and sentiment towards scheduled macroeconomic variables using various data sources and machine learning.
result Significant improvement in volatility forecasts for U.S. stocks, up to 14.99% on average.
Network analysis helps prevent money laundering by identifying risky clients and suspicious clusters.
problem Preventing money laundering using social network analysis.
method Real-world data analysis, network metrics, predictive models, visual analysis.
result Risk profiles can be predicted using social network metrics.
Examines insurance market development and similarity post-2004 EU enlargement.
problem Comparing insurance markets of EU old and new members post-enlargement.
method Analyzes data from 2004 to present to compare insurance markets.
result Identifies similarities and differences in insurance markets post-2004 enlargement.
Method constructs hedging portfolio for carbon risk but not ESG risk.
problem Hedging carbon risk with ESG risk.
method Triangulated Maximally Filtered Graph and node2vec algorithms.
result Efficient hedging portfolio strategy for carbon risk but not ESG risk.
Study uses trillion internet observations to analyze social science insights.
problem Understanding social science insights from internet data.
method Unified dataset of over 1.5 trillion observations, applied to urban growth, sleep duration, and economic productivity.
result Internet growth reaches saturation at 1 IP per 3 people, taking 16.1 years.
Study finds Twitter sentiment analysis useful for retail finance analysis.
problem Determining if Twitter sentiment correlates with financial metrics.
method Comparative analysis of Twitter sentiment, stock returns, and volume.
result Social media sentiment analysis is valuable for retail finance analysis.
Mathematical approach assesses human resource competences accurately.
problem Accurate assessment and representation of human resource competences.
method Detailed quantification scheme and mathematical approach.
result Flexible tools for optimal job assignment and recruitment.
Interpretable machine learning uncovers ESG's explanatory power on equity returns across sectors and capitalizations.
problem Explaining equity returns beyond market factors using ESG data.
method Interpretable machine learning models, cross-validation scheme, random company-wise validation.
result Gradient boosting models explain unaccounted price returns, with ESG data outperforming basic fundamental features.
This research examines relationship between staging of Venture Capital (VC) investments and social feedback visible in publicly available data on the Web. We address the question of Venture Capital investment sensitivity to performance and prospects of new venture, given as likelihood of obtaining future financing, ava…
Framework ranks sectors influenced by Indian Union Budgets.
problem Real-time analysis of budgetary impacts on sector-specific equity performance.
method Fine-tuned embeddings and language models for sector identification and performance ranking.
result 0.997 NDCG score in predicting sector ranks based on post-budget performances.
A new sector classification method outperforms existing ones in risk-adjusted returns.
problem Subjective sector classification heuristics like GICS and NAICS are not optimal.
method Learned sector classification using hierarchical clustering and reIndexer evaluation tool.
result 17-sector learned sector universe outperforms GICS and NAICS in backtests.
For a finitely generated discrete group Γ, the Γ-sectors of an orbifold Q are a disjoint union of orbifolds corresponding to homomorphisms from Γ into a groupoid presenting Q. Here, we show that the inertia orbifold and k-multi-sectors are special cases of the Γ-sectors, and that the Γ-sectors are orbif…
Study compares information flow between Chinese and US stock sectors.
problem Analyzing how information flows between sectors in Chinese and US stock markets.
method Daily sector indices, transfer entropy of daily returns, comparing 2000-2017.
result Most active sectors in information exchange differ between China and US, reflecting market dynamics.
Study uses multidimensional SE-NBD process to analyze default portfolios and identify shock amplification.
problem Analyzing interactions and shock propagation in default portfolios with multiple sectors.
method Applied multidimensional self-exciting negative binomial distribution (SE-NBD) process to 13 sectors.
result Identified upstream and downstream sectors, showing shock amplification in default portfolios.
Machine learning identifies 'canonical sectors' in US stocks.
problem Classifying companies into sectors for economic analysis and investments.
method Unsupervised machine learning of historical stock price returns.
result Emergent canonical sectors identified and their weights over time.
We present a novel methodology to determine the fundamental value of firms in the social-networking sector based on two ingredients: (i) revenues and profits are inherently linked to its user basis through a direct channel that has no equivalent in other sectors; (ii) the growth of the number of users can be calibrated…
With the network methods and random matrix theory, we investigate the interaction structure of communities in financial markets. In particular, based on the random matrix decomposition, we clarify that the local interactions between the business sectors (subsectors) are mainly contained in the sector mode. In the secto…
This study analyzes information flow networks in Chinese stock sectors using transfer entropy.
problem Understanding information transmission and market dynamics in Chinese stock sectors.
method Daily closing price data of 28 sectors from 2000 to 2017, transfer entropy, maximum spanning arborescence (MSA).
result The composite sector is an information source, and the non-bank financial sector is an information sink.
The paper analyzes Indian stock sectors using multifractal analysis for long and short-term investment.
problem Investment risk and stability in Indian stock sectors.
method Sector-wise multifractal analysis of Bombay Stock Exchange, India, over short and long time scales.
result Long-term investment in stable sectors is more profitable, while sectors with large fluctuations may lead to downturns.
The study finds significant financial sector volatility and tail risk spillovers to real economy sectors.
problem Volatility and tail risk spillovers from financial to real economy sectors.
method New measure of tail risk spillover, empirical analysis of U.S. economy 2001-2011.
result Significant volatility and tail risk spillovers from financial to real economy sectors, especially during crises.
Proposes a two-stage sector rotation method using machine learning and deep learning.
problem Identifying sectors with high investment attractiveness based on market conditions.
method Two-stage methodology: 1) Predict ETF prices using market indicators and feature selection, 2) Rank sectors based on predicted returns and select top sectors.
result The proposed methodology outperforms equally weighted portfolios and Echo State Networks show outstanding performance.
This paper models default data to capture dynamic dependence across sectors.
problem Static models fail to explain monthly default dependence.
method Dynamic low-rank state-space model for monthly multi-sector default-count data.
result Effective correlation matrices and copulas are induced from monthly data.
In this paper we consider a multivariate model-based approach to measure the dynamic evolution of tail risk interdependence among US banks, financial services and insurance sectors. To deeply investigate the risk contribution of insurers we consider separately life and non-life companies. To achieve this goal we apply …
Analyzes global economic sectors' interdependence using Google matrix analysis.
problem Understanding interdependencies and interactions among world economies and sectors.
method Reduced Google matrix algorithm applied to OECD-WTO network data.
result Shows sensitivity of sectors to petroleum activity and interdependencies among countries.
Factor analysis is a statistical technique employed to evaluate how observed variables correlate through common factors and unique variables. While it is often used to analyze price movement in the unstable stock market, it does not always yield easily interpretable results. In this study, we develop improved factor mo…
Study develops sector rotation models using factor and fundamental analysis.
problem Understanding and predicting sector shifts in financial markets.
method Systematic sector classification, factor analysis, and fundamental metrics evaluation.
result Developed predictive models with notable predictive capabilities.
The Indian IT sector is strongly linked to global markets, while CG sector reflects domestic growth.
problem Analyzing the structural characteristics of Indian IT and CG sectors.
method Time series analysis and forecasting using R programming.
result The IT sector is strongly associated with global markets (DJIA and USD/INR), while CG sector is linked to domestic growth (NIFTY).
Study reveals risk transmission channels among Chinese sectors.
problem Understanding risk transmission within Chinese economic sectors.
method Volatility spillovers analysis using VAR model and rolling window approach.
result 17 sectors are risk transmitters and 11 are risk takers.
Estimates production function of Iran's mining sector, finding capital and labor intensive.
problem Estimating production function of Iran's mining sector.
method Used co-integration method and time-series data for 1976-2006, augmented Dickey-Fuller and Phillips-Perron tests for stationarity.
result Elasticity of production with respect to capital and labor are 0.44 and 0.41, respectively; technological progress positively affects output.
Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.
problem Explaining the difference in default dependence between monthly and annual aggregation.
method Dynamic low-rank state-space model with AR(1) latent credit-state factors.
result Effective correlation matrices and rank copulas are generated from monthly default count data.
Analyzes how venture investment strategies have evolved over time in different sectors.
problem Understanding changes in venture investment strategies across sectors over time.
method Applied PCA and TCA to analyze a dataset of 52,000 startups and 110,000 funding rounds.
result There has been a shift in venture investment towards lower-tech sectors and a rise in accelerator investments.
Paper uses LLMs for sector allocation, showing better returns.
problem Automated trading sector allocation inefficiencies.
method Systematic analysis of macroeconomic data and sentiment.
result LLM-based sector allocation outperforms traditional strategies.
New techniques identify shifts in financial market sectors.
problem Identifying shifts in financial market structure and composition.
method Developed new mathematical techniques to identify nonlinear shifts in market sectors.
result Identified meaningful sector-to-sector mappings and optimal portfolio styles.
The paper analyzes sectoral diversity in startup ecosystems in Europe and the USA.
problem Investigating sectoral diversity in startup ecosystems.
method Analysis of 20+ startup ecosystems in Europe and the USA using a new visualization tool and numerical simulations.
result Emerging diversity of startup ecosystems can be explained by a preferential attachment model based on sectoral funding.
This paper explores differential and sector forms in tangent categories, finding rich structures and connections.
problem Understanding differential and sector forms in tangent categories.
method Investigates differential and sector forms in tangent categories, developing new equational presentations and structures.
result Sector forms in tangent categories form a symmetric cosimplicial object, with a subcomplex isomorphic to the de Rham complex of differential forms.
New method detects financial clustering influenced by sector and geography.
problem Detecting overlapping clusters in financial networks with multiple factors.
method Robust regression to remove sector and geography influences.
result Geography became more important for clustering after the 2008 financial crisis.