UDC 338.1
4DOI: 10.36871/2618-9976.2024.09.002
Authors
Yury A. Malyukov,
Candidate of Technical Sciences, ViceRector
for Economic Development and Informatization,
A.N. Kosygin Russian State University (Technologies. Design. Art), Moscow, Russia
Alexey O. Nedosekin,
Doctor of economics, candidate of engineering sciences, academician of IAELPS, CEO of LLC Institute
of Financial Technologies, SaintPetersburg,
Russia
Zinaida I. Abdulaeva,
Doctor of economics, candidate of engineering sciences, academician of IAELPS, CEO of LLC Institute
of Financial Technologies, SaintPetersburg,
Russia
Abstract
The authors present a new approach to analyzing the complex
characteristic of industrial sectors anisotropy based on layered
Pareto optimization in the «resilienceefficiency
» coordinates.
The sector is viewed as a collection of the largest international
companies within it, with an observation period of
8–10 years based on annual measurements. Two factors are
measured: the Resilience Index (RI, a metric of stability) and
Return on Equity (ROE, a metric of efficiency) based on net
profit. In the «resilienceefficiency
» space, all enterprises are
represented by their corresponding points (across individual
years of measurement). Layered Pareto optimization allows
for the identification of clusters of nondominated
Pareto alternatives
(layers), which, in turn, contribute to the anisotropic
nature of the »resilienceefficiency
» sector space. The
work also introduces a probabilistic Markov chain, derived
from the traditional probabilistic model by replacing axiological
probabilities with possibilities.
The phenomenon of sectoral anisotropy is illustrated through a
dynamic model of the international oil and gas sector. This dynamic
model of sectoral anisotropy, presented in the paper, enables
researchers to predict the behavior of individual enterprises
within their layers and assess the possibilities of transitioning
from one Pareto layer to another. Such forecasts can serve as a
basis for predicting the stock price dynamics of the corresponding
companies in the mediumand
longterm
perspectives.
Keywords
Linguistic normalization, Quasistatistics, Linguistic variable, Economic stability, Probabilistic Markov chain, Industrial sector
