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.
Novel financial time-series data representation improves industry sector classification.
The paper links labor income risk to stock returns using industry portfolio returns.
We consider the sectoral composition of a country's GDP, i.e. the partitioning into agrarian, industrial, and service sectors. Exploring a simple system of differential equations we characterize the transfer of GDP shares between the sectors in the course of economic development. The model fits for the majority of coun…
The professional services sector is at a turning point, with some industries showing growth opportunities.
Improves industry classification for diversified companies.
Identifies key industrial sectors in S&P 500 states.
Study shows how China's stock market reflects economic demand changes during COVID-19.
We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock portfolio which contains companies from different industrial sectors, located in several different countries. Both sector membership and geograp…
Deep learning predicts employment changes and industry health.
We discuss superstatistics theory of labour productivity. Productivity distribution across workers, firms and industrial sectors are studied empirically and found to obey power-distributions, in sharp contrast to the equilibrium theories of mainstream economics. The Pareto index is found to decrease with the level of a…
The aim of this paper is to present a further contribution to the analysis of absolute convergence (and), associated with the neoclassical theory, and conditional, associated with endogenous growth theory, of the sectoral productivity at regional level. Presenting some empirical evidence of absolute convergence of prod…
The study designs inherently interpretable machine learning models for high-risk sectors.
A classification of companies into sectors of the economy is important for macroeconomic analysis and for investments into the sector-specific financial indices and exchange traded funds (ETFs). Major industrial classification systems and financial indices have historically been based on expert opinion and developed ma…
The study finds significant financial sector volatility and tail risk spillovers to real economy sectors.
The sectoral synchronization observed for the Japanese business cycle in the Indices of Industrial Production data is an example of synchronization. The stability of this synchronization under a shock, e.g., fluctuation of supply or demand, is a matter of interest in physics and economics. We consider an economic syste…
This study analyses, through cross-section estimation methods, the influence of spatial effects and human capital in the conditional productivity convergence (product per worker) in the economic sectors of NUTs III of mainland Portugal between 1995 and 2002. To analyse the data, Moran's I statistics is considered, and …
The paper finds a surprising positive correlation between upstreamness and downstreamness in global value chains.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
Generative AI predicts economic activity from corporate transcripts.
This paper surveys the evolution of industrial concentration of the Brazilian automotive market as well as its positioning in the worldmarket. Data available by OICA (International Organization of Motor Vehicle Manufacturers) were used to better understand the characteristics of the Brazilian market on the world stage.…
Firms with different ownership structures could be argued to have different levels of efficiency.Highly concentrated firms are expected to be more efficient as this type of ownership structure may alleviate the conflict of interest between managers and shareholders.In Malaysia, public-listed firms have been found to ha…
What are East Africa's industrial opportunities? In this article we explore this question by using the Product Space to study the productive structure of five south-east African countries: Kenya, Mozambique, Rwanda, Tanzania and Zambia. The Product Space is a network connecting products that tend to be exported by the …
The paper tests stock return models and uses LSTM to predict stock returns.
Paper uses HPCA for better stock correlation modeling.
We have analyzed the Indices of Industrial Production (Seasonal Adjustment Index) for a long period of 240 months (January 1988 to December 2007) to develop a deeper understanding of the economic shocks. The angular frequencies estimated using the Hilbert transformation, are almost identical for the 16 industrial secto…
This paper investigates how economic shocks propagate and amplify through the input-output network connecting industrial sectors in developed economies. We study alternative models of diffusion on networks and we calibrate them using input-output data on real-world inter-sectoral dependencies for several European count…
This work aims to test the Verdoorn Law, with the alternative specifications of (1)Kaldor (1966), for five regions (NUTS II) Portuguese from 1986 to 1994 and for the 28 NUTS III Portuguese in the period 1995 to 1999. Will, therefore, to analyze the existence of increasing returns to scale that characterize the phenomen…
Firms' collaboration networks can decline but remain resilient.
Study compares information flow between Chinese and US stock sectors.
Study analyzes impacts of COVID-19 on French forestry sector, finds mixed results in supply chain.
Quantum computing promises to revolutionize finance, especially in optimization and modeling.
Myanmar is languishing at the bottom of key international indexes. United Nations considers the country as a structurally weak and vulnerable economy. Yet, from 2011 when Myanmar ended decades of military rule and isolationism and transited towards democracy, its breakneck development has led to many considering the co…
TSML tackles anomaly detection and pattern discovery in industrial time series data.
Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Following [1] a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) app…
This research improves debt collection strategies using advanced machine learning.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
We give a simple explicit algorithm for building multi-factor risk models. It dramatically reduces the number of or altogether eliminates the risk factors for which the factor covariance matrix needs to be computed. This is achieved via a nested "Russian-doll" embedding: the factor covariance matrix itself is modeled v…
The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PM…
Develops MIS, a probabilistic model for multi-industry classification.
Enhanced indexation with sector constraints using SSD for better portfolio performance.
The objective of this paper is to fill a gap in the literature on internationalization, in relation to the absence of objective and measurable performance indicators on the process of how firms sequentially enter external markets. To that end, this research develops a quantitative tool that can be used as a performance…
We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the indu…
Expert system predicts credit card charge-offs using macroeconomic indicators.
We give a detailed account of correlations between credit sector/quality and treasury curve factors, using the robust framework of the Barclays POINT Global Risk Model. Consistent with earlier studies, we find a strong negative correlation between sector spreads and rate shifts. However, we also observe that the correl…
We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over 11 segments within the present financial crisis, to construct minimal spanning trees (…
The expression "wage transition" refers to the fact that over the past two or three decades in all developed economies wage increases have levelled off. There has been a widening divergence and decoupling between wages on the one hand and GDP per capita on the other hand. Yet, in China wages and GDP per capita climbed …
Study examines cyber losses across sectors, finds high severity and frequency.
How technology affects growth or employment has long been debated. With a hiatus, the debate revived once again in the form of how Information and Communications Technology, as a form of new technology, exerts on productivity and employment. Information and Communications Technology perceived as General Purpose Technol…