Open AccessFORECASTING OF THE STOCK RATE OF LEADING WORLD COMPANIES USING ECONOMETRIC METHODS AND DCF ANALYSIS
Author(s) -
Oleikolaieva,
Anzhela Petrova,
Rostyslav Lutsenko
Publication year - 2020
Publication title -
international journal of innovative technologies in economy/international journal of innovative technologies in economy
Language(s) - English
Resource type - Journals
eISSN - 2414-1305
pISSN - 2412-8368
DOI - 10.31435/rsglobal_ijite/31052020/7067
Subject(s) - autoregressive integrated moving average , econometrics , econometric model , stock (firearms) , economics , vector autoregression , econometric analysis , autoregressive model , stock market , financial economics , computer science , time series , engineering , mechanical engineering , paleontology , horse , biology , machine learning
In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods.