Trading decisions often encounter risk and uncertainty complexities, significantly influencing
their overall performance. Recognizing the intricacies of this challenge, computational …

In the decade passed, considerable affords were made to develop effective trading systems
based on different assumptions concerned with the market nature, methods for data …

A new Forex multi-criteria trading model based on the Belief-Plausibility (B el− P l) extension
is developed and validated. The B el− P l approach is, in fact, a meta-theory in relation to the …

This paper is based on some assumptions validated using real data and the most used
Forex trading platform Meta Trader 4. First, we assume that any reasonable and relatively …

Time series, including noise, non-linearity, and non-stationary properties, are frequently
used in prediction problems. Due to these inherent characteristics of time series data …

In light of recent cryptocurrency value fluctuations, Bitcoin is gradually gaining recognition as
an investment vehicle. Given the market's inherent volatility, accurate forecasting becomes …

Accurately forecasting financial indicators is challenging given their volatile nature,
dependence on various market-related factors and impact on supply chains. Therefore …

The stock market plays an imperative role in the entire financial market. The intricate and
multifaceted nature of the stock market poses a challenge for investors seeking to establish …

Use of machine learning for equity price prediction is a novel and famous research area.
Equity returns can beat the inflation and tax rate which leads to the best choice of …

Whereas many studies have been done on forecasting different time series, it has always
been associated with challenges such as uncertainty. For example, in financial time series, if …