This systematic literature review analyses the recent advances of machine learning and
deep learning in finance. The study considers six financial domains: stock markets, portfolio …

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 …

This study proposes a novel approach for modeling financial time series, concentrating on
data pre-processing and selecting effective features in conventional and proposed modeling …

Abstract Artificial Neural Network (ANN) is an effective machine learning technique for
addressing regression tasks. Nonetheless, the performance of ANN is highly dependent on …

In financial engineering, portfolio optimization has been of consistent interest. Portfolio
optimization is a process of modulating asset distributions to maximize expected returns and …

The stock market is a dynamic, complex, and chaotic environment, which makes predictions
for the stock market difficult. Many prediction methods are applied to the stock market, but …

Forecasting stock markets is an important challenge due to leptokurtic distributions with
heavy tails due to uncertainties in markets, economies, and political fluctuations. To forecast …

In a multi-objective setting, a portfolio manager's highly consequential decisions can benefit
from assessing alternative forecasting models of stock index movement. The present …

Stock price trend prediction is a fascinating but difficult research topic. Recently, GNN-based
models have been continuously proposed, which are believed to be more effective since …

Predicting stock market fluctuations is a difficult task due to its intricate and ever-changing
nature. To address this challenge, we propose an approach to minimize forecasting errors …