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Прогнозирование финансово-экономических показателей деятельности предприятий с использованием графиков

Authors: Parmacli, D.M.; Parmakli, D.M.; Parmacli, D.;

Прогнозирование финансово-экономических показателей деятельности предприятий с использованием графиков

Abstract

Подчеркивается, что прогнозирование финансово-экономических показателей деятельности предприятий имеет свои особенности, вызванные тем, что доход от реализации продукции, валовая и чистая прибыль, как правило, носят нестабильный характер. Это особенно актуально для сельскохозяйственных организаций и перерабатывающих предприятий. АТО Гагаузия расположена в эпицентре зоны рискованного неустойчивого земледелия. Методика прогнозирования финансово-экономических показателей деятельности предприятий требует уточнений и по возможности применения единых подходов, доступных для использования на практике, как в учебных целях, так и в сельскохозяйственных организациях. Предлагается графический метод прогнозирования, как наиболее простой и доступный для использования на практике. Он базируется на применение графиков, анализа их трендов и коэффициентов аппроксимации, отражающих тенденцию развития экономических явлений за последние 5 и более лет. если коэффициент аппроксимации составляет 0,8 и выше для прогнозирования можно использовать линейный тренд. Если коэффициент аппроксимации находится в пределах 0,5 - 0,8, прогнозное значение показателя следует определять, как среднее значение, рассчитанное по линейному и полиномиальному трендам. При коэффициента аппроксимации ниже 0,5 прогноз рассчитывается как среднее значение по линейному и полиномиальному трендам на основе использования среднегодовых скользящих показателей. В статье представлена методика расчетов прогнозных значений доходов от реализации, собственного капитала и чистой прибыли на примере конкретного предприятия АТО Гагаузия за 2013-2022 годы.

It is emphasized that the forecasting of financial and economic performance of enterprises has its own characteristics, due to the fact that income from product sales, gross and net profits, as a rule, are unstable. This is especially true for agricultural organizations and processing enterprises. ATU Gagauzia is located in the epicenter of the zone of risky unsustainable agriculture. The methodology for forecasting the financial and economic performance of enterprises requires clarification and, if possible, the use of uniform approaches available for use in practice, both for educational purposes and in agricultural organizations. A graphical forecasting method is proposed as the simplest and most accessible for practical use. It is based on the use of charts, analysis of their trends and approximation coefficients, reflecting the trend in the development of economic phenomena over the past 5 years or more. if the approximation coefficient is 0.8 or higher, a linear trend can be used for forecasting. If the approximation coefficient is in the range of 0.5 - 0.8, the forecast value of the indicator should be determined as an average value calculated from linear and polynomial trends. With an approximation coefficient below 0.5, the forecast is calculated as the average of linear and polynomial trends based on the use of annual moving averages. The article presents a methodology for calculating the forecast values of income from sales, equity and net profit on the example of a specific enterprise of ATU Gagauzia for 2013-2022.

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Keywords

equity, среднегодовые скользящие показатели, net profit, average annual moving indicators, доход от реализации, equation of linear and polynomial trends, собственный капитал, уравнение линейного и полиномиального трендов, sales income, чистая прибыль

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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