Innovations in management forecast: Time development of stock prices with neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F20%3A00001791" target="_blank" >RIV/75081431:_____/20:00001791 - isvavai.cz</a>
Result on the web
<a href="https://mmi.fem.sumdu.edu.ua/sites/default/files/392-2020_Vochozka_et%20al.pdf" target="_blank" >https://mmi.fem.sumdu.edu.ua/sites/default/files/392-2020_Vochozka_et%20al.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Innovations in management forecast: Time development of stock prices with neural networks
Original language description
This paper aims to innovate the prediction management when predicting the share price development over time by the use of neural networks. For the contribution, the data on the prices of CEZ, a.s. shares obtained from the Prague Stock Exchange database. The stock price data are available for the period 2012-2017. In the case of Statistica software, the multilayer perceptron networks (MLP) and the radial basis function networks (RBF) are generated. Inthe case of Matlab software, the Support Vector Regression (SVR) and the Back-Propagation Neural Network (BPNN) are generated. The networks with the best characteristics are retained and based on the statistical interpretation of the results, and all are applicable in practice.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50200 - Economics and Business
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Marketing and Management of Innovations
ISSN
2218-4511
e-ISSN
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Volume of the periodical
2020
Issue of the periodical within the volume
2
Country of publishing house
UA - UKRAINE
Number of pages
16
Pages from-to
324-339
UT code for WoS article
000545377200024
EID of the result in the Scopus database
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