arXiv research

Study analyzes impacts of COVID-19 on French forestry sector, finds mixed results in supply chain.

Study examines how disturbances affect financial returns in Austrian forests.

Study values and optimizes forestry leases under risk and uncertainty.

Kriging is the predominant method used for spatial prediction, but relies on the assumption that predictions are linear combinations of the observations. Kriging often also relies on additional assumptions such as normality and stationarity. We propose a more flexible spatial prediction method based on the Nearest-Neig…

Framework ranks sectors influenced by Indian Union Budgets.

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.

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…

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…

This study analyzes information flow networks in Chinese stock sectors using transfer entropy.

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…

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.

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…

Tech sector decouples from non-tech sectors post-2015, predicting economic growth.

Uncovering the risk transmitting path within economic sectors in China is crucial for understanding the stability of the Chinese economic system, especially under the current situation of the China-US trade conflicts. In this paper, we try to uncover the risk spreading channels by means of volatility spillovers within …

We apply the recently developed reduced Google matrix algorithm for the analysis of the OECD-WTO world network of economic activities. This approach allows to determine interdependences and interactions of economy sectors of several countries, including China, Russia and USA, properly taking into account the influence …

Enhanced indexation with sector constraints using SSD for better portfolio performance.

Kurdistan Region is a tourist hub. This research analyzes other Non-Oil Sectors that have huge attractions of Foreign Direct Investments into the Kurdistan Region from 2005 to 2013. Comparative analysis was carried out between Iraq and the Region, and among influential Sectors of the Economy. T-test and ANOVA are stati…

The purpose of this study is to estimate the production function and examine the structure of production in the mining sector of Iran. Several studies have already been conducted in estimating production functions of various economic sectors; however, less attention has been paid to mining sectors. After examining the …

We consider the isoperimetric problem in planar sectors with density $r^{p}$, and with density $a>1$ inside the unit disk and $1$ outside. We characterize solutions as a function of sector angle. We also solve the isoperimetric problem in $\mathbb{R}^{n}$ with density $r^{p},\; p<0$.

Study uses Hawkes processes to analyze stock market contagion in China.

This paper generalizes Moody's correlated binomial default distribution for homogeneous (exchangeable) credit portfolio, which is introduced by Witt, to the case of inhomogeneous portfolios. As inhomogeneous portfolios, we consider two cases. In the first case, we treat a portfolio whose assets have uniform default cor…

Deep learning LSTM predicts stock prices for portfolio design in Indian sectors.

In this paper, we perform a comparative segmentation and clustering analysis of the time series for the ten Dow Jones US economic sector indices between 14 February 2000 and 31 August 2008. From the temporal distributions of clustered segments, we find that the US economy took one and a half years to recover from the m…

This paper studies business cycle patterns in UK sectoral output. It analyzes the distinction between white noise processes and their non-white noise counterparts in the frequency domain and further examines the associated features and patterns for the process where white noise conditions are violated. The characterist…

This study optimizes stock portfolios for Indian sectors using historical data.

We analyze the sectoral dynamics of startup venture financing. Based on a dataset of 52000 start-ups and 110000 funding rounds in the United States from 2000 to 2017, and by applying both Principal Component Analysis (PCA) and Tensor Component Analysis (TCA) in sector space, we visualize and measure the evolution of th…

The paper models financial markets and real economy interactions using a large agent framework.

Tangent categories provide an axiomatic framework for understanding various tangent bundles and differential operations that occur in differential geometry, algebraic geometry, abstract homotopy theory, and computer science. Previous work has shown that one can formulate and prove a wide variety of definitions and resu…

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…

Paper studies estimating asset correlations across sectors.

This paper optimizes portfolios of thematic sector stocks using LSTM models.

Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality la…

Geospatial framework assesses climate risks for California's banking and exposed sectors.

Rigidity theorem for spherical sectors in Riemannian manifolds.