The outbreak of COVID‐19 has brought the world to an unprecedented position where
financial and mental resources are drying up. Livelihoods are being lost, and it is becoming …

The proposed model is a Deep Learning (DL) based method employing Long Short-Term
Memory (LSTM) networks for forecasting stocks. The aim of this approach is forecasting …

In recent years, the importance of social media has grown analytically, emphasizing the
importance of an appropriate framework that can clarify its role in financial investment …

Purpose This paper aims to introduce a new perspective on long-term stock return
predictability by focusing on the relative (individual and hybrid) informative power of a wide …

This study has been conducted on forecasting, as accurately as possible, the next day's
stock price using harmony search (HS) and its variants [improved harmony search (IHS) …

In the e-commerce environment, website-based recommendation systems have been
developed in order to help consumers without professional fashion-related knowledge to …

This paper presents an efficient system for accurate, confident, general and responsive stock
market prediction, employing Artificial Neural Networks (ANN). For technical indicators, Multi …

The stock market offers investors the opportunity to trade in shares and equities. Making
profit in the stock market depends on the ability to accurately predict the future stock prices …

Today's markets are rather matured and arbitrage opportunities remain for a very short time.
The main objective of the paper is to devise a stock market ontology-based novel trading …

This article examines the behaviour and responses of stock market indices to various
macroeconomic determinants by using small scale Bayesian VAR model. Our objective is to …