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.
New model forecasts power consumption with high accuracy over months to years.
Neural network predicts daily power consumption with high accuracy.
Labor productivity was studied at the microscopic level in terms of distributions based on individual firm financial data from Japan and the US. A power-law distribution in terms of firms and sector productivity was found in both countries' data. The labor productivities were not equal for nation and sectors, in contra…
Hybrid quantum neural networks predict continuous variables.
Labour productivity distribution (dispersion) is studied both theoretically and empirically. Superstatistics is presented as a natural theoretical framework for productivity. The demand index is proposed within this framework as a new business index. Japanese productivity data covering small-to-medium to large firm…
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…
RNN(p) improves power consumption forecasts with interpretable models.
We construct a theoretical model for equilibrium distribution of workers across sectors with different labor productivity, assuming that a sector can accommodate a limited number of workers which depends only on its productivity. A general formula for such distribution of productivity is obtained, using the detail-bala…
The growth of business firms is an example of a system of complex interacting units that resembles complex interacting systems in nature such as earthquakes. Remarkably, work in econophysics has provided evidence that the statistical properties of the growth of business firms follow the same sorts of power laws that ch…
We study soft persistence (existence in subsequent temporal layers of motifs from the initial layer) of motif structures in Triangulated Maximally Filtered Graphs (TMFG) generated from time-varying Kendall correlation matrices computed from stock prices log-returns over rolling windows with exponential smoothing. We ob…
The paper extends topological field theory to noncompact surfaces using symmetric powers.
LSTM model predicts stock prices with high accuracy in stable sectors but struggles with volatile ones.
TDA detects stock market crashes across continents.
The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring …
The Hype Index measures media attention to equities using NLP.
The paper analyzes cryptocurrency and equity markets using advanced statistical methods.
Generative AI predicts economic activity from corporate transcripts.
Estimates point counts in Teichmüller space for mapping class groups.
India's 2020-21 GDP growth forecast is projected at 1.9% due to COVID-19.
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…
Framework ranks sectors influenced by Indian Union Budgets.
For a finitely generated discrete group , the -sectors of an orbifold are a disjoint union of orbifolds corresponding to homomorphisms from into a groupoid presenting . Here, we show that the inertia orbifold and -multi-sectors are special cases of the -sectors, and that the -sectors are orbif…
Study compares information flow between Chinese and US stock sectors.
Market sectors play a key role in the efficient flow of capital through the modern Global economy. We analyze existing sectorization heuristics, and observe that the most popular - the GICS (which informs the S&P 500), and the NAICS (published by the U.S. Government) - are not entirely quantitatively driven, but rather…
Interpretable machine learning uncovers ESG's explanatory power on equity returns across sectors and capitalizations.
In the coming years, the satellite broadband market will experience significant increases in the service demand, especially for the mobility sector, where demand is burstier. Many of the next generation of satellites will be equipped with numerous degrees of freedom in power and bandwidth allocation capabilities, makin…
Study uses multidimensional SE-NBD process to analyze default portfolios and identify shock amplification.
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…
Different shares of distinct commodity sectors in production, trade, and consumption illustrate how resources and capital are allocated and invested. Economic progress has been claimed to change the share distribution in a universal manner as exemplified by the Engel's law for the household expenditure and the shift fr…
This study analyzes information flow networks in Chinese stock sectors using transfer entropy.
In this paper, we explore the detection of clusters of stocks that are in synergy in the Indian Stock Market and understand their behaviour in different circumstances. We have based our study on high frequency data for the year 2014. This was a year when general elections were held in India, keeping this in mind our da…
The paper analyzes Indian stock sectors using multifractal analysis for long and short-term investment.
The study finds significant financial sector volatility and tail risk spillovers to real economy sectors.
Proposes a two-stage sector rotation method using machine learning and deep learning.
This paper models default data to capture dynamic dependence across sectors.
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 …
Study develops sector rotation models using factor and fundamental analysis.
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…
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…
Study reveals risk transmission channels among Chinese sectors.
Paper uses LLMs for sector allocation, showing better returns.
Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.
New techniques identify shifts in financial market sectors.
Bangladesh's banking sector improved through financial reforms, but challenges remain.
GARCH models predict stock volatility in Indian sectors.
Identifies key industrial sectors in S&P 500 states.
Paper proposes a new method for hourly load forecasting using smart meter data.
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…