PRAŽÁK, Tomáš. The Importance of Financial Ratios for Predicting Stock Price Trends: The Evidence from the Visegrad Group. Online. In 20th International Scientific Conference "Enterprise and Competitive Environment",. Neuveden: Mendel University in Brno, 2017, p. 663-672. ISBN 978-80-7509-499-5.
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Basic information
Original name The Importance of Financial Ratios for Predicting Stock Price Trends: The Evidence from the Visegrad Group
Authors PRAŽÁK, Tomáš (203 Czech Republic, guarantor, belonging to the institution).
Edition Neuveden, 20th International Scientific Conference "Enterprise and Competitive Environment", p. 663-672, 10 pp. 2017.
Publisher Mendel University in Brno
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50202 Applied Economics, Econometrics
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/47813059:19520/17:00010955
Organization unit School of Business Administration in Karvina
ISBN 978-80-7509-499-5
Keywords in English microeconomic factors; financial ratios; stock prices; Visegrad; regression
Changed by Changed by: RNDr. Daniel Jakubík, učo 139797. Changed: 7/2/2020 10:58.
Abstract
This study examines the effect of the main microeconomic factors on the stock prices of select financial sector companies listed on the Central European Exchanges (Budapest Stock Exchange, Prague Stock Exchange, Bratislava Stock Exchange, or Warsaw Stock Exchange). The microeconomic factors are based on the financial situation in companies. For the analysis are used financial ratios, gained from the financial statements of the individual companies. In general, the paper confirmed that rentability and debt ratios are the most important business factors from the prospective of its impact on stock prices. The relationship between observed variables is explored using panel regression analysis. The ordinary least squares method is used for constructing a regression model. The sample period of our dataset is composed of annual data from 2002 to 2015.
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