Stock market forecasting is one of the biggest challenges in the financial market since its
time series has a complex, noisy, chaotic, dynamic, volatile, and non-parametric nature …

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) …

The recognition of the value and importance of recognizing patterns in the stock market is
widely accepted. As a result, using innovative decision-making strategies is expected to lead …

Considering deep sequence learning for practical application, two representative RNNs-
LSTM and GRU may come to mind first. Nevertheless, is there no chance for other RNNs …

Purpose: Investment is the allocation of funds with the aim of obtaining profits in the future.
An example of the investment instruments with high returns and high risks are stocks. The …

since the advancement of Artificial Neural Network (ANNs), many studies have been
proposed in the past few decades for forecasting purposes. Time-series applications have …

Stock prices and their fluctuations have a major impact on our daily lives. Therefore, it is
necessary to discuss this forum today and study its various aspects. he use of machine …

For a long time it was believed that trying to forecast stock prices from stock markets was an
absurd idea given the unpredictable nature of such markets, and, indeed, many studies …

In Brazil, tax on operations related to the movement of goods, on provision of interstate and
intercity transport services and communication, known by the acronym ICMS, is highly …