Stock market is one of the attractive domains for researchers as well as academicians. It
represents highly complex non-linear fluctuating market behaviours where traders …

Predicting long-term stock index prices is a challenging and debatable task. Most of the
studies focus on predicting next-day stock prices. However, those are not useful to long-term …

Feature selection facilitates uncertainty disposal and information mining, and it has received
widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind …

Using machine learning coupled with stock price data to predict stock price trends has
attracted increasing attention from data mining and machine learning communities. An …

For the purpose of managing financial risk and making investment decisions, interval stock
price forecasting is essential. Currently, decomposition integration frameworks are widely …

Forecasting stock market via historical financial data is an important issue for market
participants because even if the prediction accuracy is only slightly improved, better trading …

The uncertainty associated with option price predictions has largely been overlooked in the
literature. This paper aims to fill this gap by quantifying such uncertainty using conformal …

Stock price trend prediction is an important and challenging issue, and accurate forecasting
will effectively improve investment decisions and contribute to investment returns. Improving …

This research paper provides a comprehensive review of the emerging trends in AI-based
stock market prediction. The paper highlights the key concepts, approaches, and techniques …

Securing networks is becoming increasingly crucial due to the widespread use of
information technology. Intrusion Detection System (IDS) plays a crucial role in network …