Forecasting of the Stock Price Using Recurrent Neural Network - Long Short-term Memory
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018194" target="_blank" >RIV/62690094:18450/21:50018194 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.36689/uhk/hed/2021-01-014" target="_blank" >http://dx.doi.org/10.36689/uhk/hed/2021-01-014</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.36689/uhk/hed/2021-01-014" target="_blank" >10.36689/uhk/hed/2021-01-014</a>
Alternative languages
Result language
angličtina
Original language name
Forecasting of the Stock Price Using Recurrent Neural Network - Long Short-term Memory
Original language description
We employ a recurrent neural network with Long short-term memory for the task of stock price forecasting. We chose three stocks from the same sub-industry: Visa, Mastercard, and PayPal. This paper aims to test the LSTM network's prediction on stock prices and propose the best settings for selected stock price forecasting. The secondary goal is to assess how the settings differed in the case of two highly correlated stocks (Visa-Mastercard year correlation coefficient average: 0.97) and the case of only weak correlated stock (Visa-PayPal correlation coefficient average: 0.39). We tested 117 different settings of LSTM neural networks. The settings differed by the number of epochs/splits (from ten to fifty-eight by the step of four) and the range (minute, hour, and day). Our dataset was the stock price from 1.6.2020 to 15.1.2021. The best performing network has been trained on a 10-day period for Visa and 10-minute for Mastercard and PYPL. However, the differences were negligible, so we did not find the number of epochs as a key setting, unlike in the case of FOREX.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
50201 - Economic Theory
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
HRADEC ECONOMIC DAYS, VOL 11(1)
ISBN
978-80-7435-822-7
ISSN
2464-6059
e-ISSN
2464-6067
Number of pages
10
Pages from-to
145-154
Publisher name
UNIV HRADEC KRALOVE
Place of publication
HRADEC KRALOVE 3
Event location
Hradec Kralove
Event date
Mar 25, 2021
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000670596900014