Predicting returns in the stock market is usually posed as a forecasting problem where
prices are predicted. Intrinsic volatility in the stock market across the globe makes the task of …

Predicting trends in stock market prices has been an area of interest for researchers for
many years due to its complex and dynamic nature. Intrinsic volatility in stock market across …

Stock market prediction based on machine or deep learning is an essential topic in the
financial community. Typically, models with different structures or initializations provide …

Stock market prediction is the process of forecasting future prices of stocks. Stock market
prediction is a challenging process as a result of uncertainties that influence the market …

In the era of Internet of Things (IoT) and big data, data has increased dramatically.
Computers have been used in various fields. Algorithmic trading is beginning to develop …

Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and
artificial intelligence agents (algorithms) interact, exchanging data and decisions and …

Despite active research on trading systems based on reinforcement learning, the
development and performance of research methods require improvements. This study …

Directional Change (DC) is a technique to summarize price movements in a financial
market. According to the DC concept, data is sampled only when the magnitude of price …

Abstract Machine learning is coming as a significant encroachment in the financial services
industry. Finance has always been about data and is considered a complex field of study …

Stock market return forecasting is currently regarded as a prediction issue. The forecasting
process is challenging due to the financial markets inherent volatility on a global scale. The …