Usage of Artificial Intelligence and Spectral Analysis for Predicting the Behavior of Stock Prices

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Usage of Artificial Intelligence and Spectral Analysis for Predicting the Behavior of Stock Prices

Popis výsledku v původním jazyce

In this paper methods of artificial intelligence and spectral analysis to build an algorithm for predicting the behavior of stock prices are applied. Spectral decomposition of a time series was calculated using known methods based on Fourier transformation. The results obtained from periodogram analysis simply provide information about periodicities. Significance analysis was not performed and we worked with four frequencies. This spectral information is then used in clustering of data. Comparison of behavior of price oscillation in clusters was carried out. The presented contribution aims to describe a new algorithm for predicting the behavior of stock prices. The clustering algorithm is based on spectral analysis and SOM. The whole procedure is tested on selected time sections of Dow Jones Industrial Averages, where the algorithm is performed. Results of analysis and final discussion, presented in the Case Study, show that the new method successfully signalizes the trend of stock market prices.

Název v anglickém jazyce

Usage of Artificial Intelligence and Spectral Analysis for Predicting the Behavior of Stock Prices

Popis výsledku anglicky

In this paper methods of artificial intelligence and spectral analysis to build an algorithm for predicting the behavior of stock prices are applied. Spectral decomposition of a time series was calculated using known methods based on Fourier transformation. The results obtained from periodogram analysis simply provide information about periodicities. Significance analysis was not performed and we worked with four frequencies. This spectral information is then used in clustering of data. Comparison of behavior of price oscillation in clusters was carried out. The presented contribution aims to describe a new algorithm for predicting the behavior of stock prices. The clustering algorithm is based on spectral analysis and SOM. The whole procedure is tested on selected time sections of Dow Jones Industrial Averages, where the algorithm is performed. Results of analysis and final discussion, presented in the Case Study, show that the new method successfully signalizes the trend of stock market prices.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

16th Conference on Applied Mathematics APLIMAT 2017 : proceedings

ISBN

978-80-227-4650-2

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

11

Strana od-do

1264-1275

Název nakladatele

Spektrum STU

Místo vydání

Bratislava

Místo konání akce

Bratislava

Datum konání akce

31. 1. 2017

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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