Deep Neural Networks for Stock Market Price Predictions in VUCA Environments
2nd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2022
; 302:665-674, 2022.
Article
in English
| Scopus | ID: covidwho-2014051
ABSTRACT
The purpose of this paper is to examine the useful application of deep neural networks in stock price prediction in efficient markets and under Volatile, uncertain, complex and ambiguous (VUCA) environments, especially in the covid-induced USA financial of 2021 crisis. VUCA environments such as stock markets have made it difficult to predict stock prices. This study investigates the usefulness of deep learning architectures in stock price prediction for S&P 500’s top 3 stocks namely Apple, Microsoft and Amazon. The Bidirectional Long Short Term Memory (BLSTM) and Bidirectional Gated Recurrent Unit (BGRU) were implemented in this study and provided excellent accuracy results, the highest been 95.04% using the BGRU for Microsoft stock. The novelty of this study is the successful application of bidirectional deep neural networks to financial time series and forecasting of stock prices under financial crisis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Complex and ambiguous environments; Covid-19; Deep learning technologies; Financial crisis; Stock market predictions; Uncertain; Volatile; Commerce; Complex networks; Costs; Electronic trading; Financial markets; Forecasting; Recurrent neural networks; Complex and ambiguous environment; Deep learning technology; Learning technology; Stock market prediction; Stock price; Stock price prediction; Deep neural networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2nd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2022
Year:
2022
Document Type:
Article
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