Market prices trend forecasting supported by Elliott Wave's theory
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10237667" target="_blank" >RIV/61989100:27240/17:10237667 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Market prices trend forecasting supported by Elliott Wave's theory
Popis výsledku v původním jazyce
The forecasting of the stock markets' trends is one of the most frequently applied point of interests in machine learning (ML) industry from its beginning. The theory of Elliott waves' (EW) patterns based on Fibonacci's ratios is also heavily applied in several trading strategies and tools which are available on the market and also there are many studies based on analysis and application of those patterns. This paper covers market's trend prediction by ML algorithms such as Random Forest and Support Vector Machine. The trend prediction is supported by application of recognized Elliot waves which was performed by custom developed algorithm based on available knowledge about the patterns. The combination of ML algorithms and EW pattern detector achieved significantly higher performance compare to the ML algorithms only.
Název v anglickém jazyce
Market prices trend forecasting supported by Elliott Wave's theory
Popis výsledku anglicky
The forecasting of the stock markets' trends is one of the most frequently applied point of interests in machine learning (ML) industry from its beginning. The theory of Elliott waves' (EW) patterns based on Fibonacci's ratios is also heavily applied in several trading strategies and tools which are available on the market and also there are many studies based on analysis and application of those patterns. This paper covers market's trend prediction by ML algorithms such as Random Forest and Support Vector Machine. The trend prediction is supported by application of recognized Elliot waves which was performed by custom developed algorithm based on available knowledge about the patterns. The combination of ML algorithms and EW pattern detector achieved significantly higher performance compare to the ML algorithms only.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
1st EAI International Conference on Computer Science and Engineering (COMPSE 2016) : conference proceedings : November 11 - 12, 2016, Penang, Malaysia
ISBN
978-1-63190-136-2
ISSN
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e-ISSN
neuvedeno
Počet stran výsledku
11
Strana od-do
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Název nakladatele
European Alliance for Innovation
Místo vydání
Gent
Místo konání akce
Penang
Datum konání akce
11. 11. 2016
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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