Deep Neural Networks for Stock Market Price Predictions in VUCA Environments

Abstract

AbstractThe 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.KeywordsVolatileUncertainComplex and ambiguous environmentsStock market predictionsFinancial crisisCovid-19Deep learning technologies