Кафедра цифрових технологій та проєктно-аналітичних рішень (ЦТПАР)
Постійне посилання колекціїhttps://dspace.mipolytech.education/handle/mip/22
Переглянути
2 результатів
Результати пошуку
Документ Information technology of determination the company's financial condition for the financial planning subsystem of the EPM system(Національний аерокосмічний університет ім. М. Є. Жуковського «Харківський авіаційний інститут», 2022) Moskalenko, V. V.; Fonta, N.; Grinchenko, M.; Nikulina, O. M.; Yershova, S.; Москаленко, В. В.; Фонта, Н. Г.; Нiкулiна, О. М.; Гринченко, М. А.; Єршова, С. І.The subject matterof this article is the process of forming a company's development finance program. The goal is to develop the information technology to determine the company's financial condition for the financial plan-ning subsystem of an enterprise performance management (EPM) System. The tasksare to develop a methodfor forming a company's development finance program as the basis for the financial planning subsystem of the EPM system; develop a methodology of determining the financial condition of the company as a component of the method; develop an information technology (IT) for determining the company’s financial condition; develop a method for forecasting financial states on the strategic period using a neural network. The following resultswere obtained. The methodfor forming a company's development finance program is implemented as the finan-cial planning subsystem for the EPM system. A methodology for determining the financial condition of a com-pany as a component of this method is presented in this article. Information technology for the implementation of this methodology has been developed. The components of the IT are the calculation of financial indicators based on data from financial statements for a certain period; the analysis of return on equity; the determination of the company financial stability; the determination of the financial condition in dynamics; the forecasting of the company's financial condition for the strategic period; the formation of development strategies for forecast-ing financial condition. The method for forecasting financial states in the strategic period was implemented using a neural network with the Temporal Fusion Transformer architecture. Conclusions. The scientific novelty of the results obtained is as follows: 1) the stages of the process of forming a company's development finance program were improved by methodology for determining the financial condition of the company, by model for determining the rational ratio of own and borrowed funds, by technology for selecting possible sources of fi-nancing development projects, by method for determining investment project financing schemes;2) methodology for determining the financial condition of the company was further developed byincluding a component for predicting financial indicators using a neural network; 3) the company's financial condition module for EPM System was further developed by IT implementation, which implements the assessment and forecast of the com-pany's financial condition is carried out and the financial strategy of the company's development is formed.Документ The value of shares prediction in an unstable economy using neural networks(CEUR Workshop Proceedings, 2022) Moskalenko, V. V.; Santalova, A. R. ; Fonta, N.; Nikulina, O. M.; Москаленко, В. В.; Нікуліна, О. М.The relevance of this research topic is due to the need to develop algorithmic provision of the market value forecasting for shares in Ukraine and the introduction of the concept for increasing information transparency of the domestic stock market. All this will help improve the investment market, provide investment and development of Ukrainian companies and the economy as a whole. An analysis of researchon the use of methods for computational intelligence, including neural networks to model the behavior of stock market participants and solve the problem of forecasting. A study was conducted based on using neural networks of different architecture to predict the market value of shares in the stock markets of Ukraine, which are in the process of formation and development. The following models of neural networks were chosen for experimental research: Long short-term memory; Convolutional neural network; a hybrid model that combines two neural network architectures CNN and LSTM; a hybrid model consisting of a variational mode decomposition algorithm and a long-term memory neural network (VMD-LSTM). Four shares of the Ukrainian Stock Exchange were selected forforecasting: Tsentrenergo (CEEN); Ukrtelecom (UTLM); Kriukivs’kyi Vahonobudivnyi Zavod PAT (KVBZ); Raiff Bank Aval (BAVL). Estimates of forecast quality are calculated. It was concluded that it is advisable to use the LSTM network to forecast stocks that are on the stock exchanges of countries with unstable economies.