Researchers and practitioners have attempted to predict the financial market by analyzing
textual (eg, news articles and social media) and numeric data (eg, hourly stock prices, and …

In stock market forecasting, the identification of critical features that affect the performance of
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

The future is unknown and uncertain, but there are ways to predict future events and reap
the rewards safely. One such opportunity is the application of machine learning and artificial …

Stock market forecasting has been a subject of interest for many researchers; the essential
market analyses can be integrated with historical stock market data to derive a set of …

Real-time market prediction tool tracking public opinion in specialized newsgroups and
informative market data persuades investors of financial markets. Previous works mainly …

Forecasting the stock composite index is a challenge on account of the abundant noise-
induced high degree of non-linearity and non-stationarity. Numerous predictive models …

Abstract Stock Market Forecasting (SMF) has become a spotlighted area and is receiving
increasing attention due to the potential that investment returns can generate profound …

Abstract Information fusion is one of the critical aspects in diverse fields of applications;
while the collected data may provide certain perspectives, a fusion of such data can be a …

Numerous machine learning models have been developed to achieve the 'real-life'financial
objective of optimising the risk/return profile of investment strategies. In the current article:(a) …