Abstract
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.
Highlights
Forecasting of market indicators of the value of stocks has been in demand and relevant since the inception of stock markets as the economic condition of individuals enterprises and the entire state depends on the ability to properly manage valuable financial assets
Making an informed decision regarding the purchase of a financial asset, investing in the development of a company involves determining the relevant price of a share and security
This review showed that each group of methods has its advantages and disadvantages; to build a qualitative forecast it is better to use methods that complement each other, in particular, the comprehensiveness of the discounted cash flow (DCF) analysis and the flexibility of the ARIMA model
Summary
Phys.-Mat. of science Ukraine, V.N. Karazin Kharkiv National University; Department of Economic Cybernetics and Applied Economics http://orcid.org/0000-0003-1773-1427.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Innovative Technologies in Economy
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
