In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …

Social Media (SM) are the most widespread and rapid data generation applications on the
Internet increase the study of these data. However, the efficient processing of such massive …

The future is unknown and uncertain, but there are ways to predict future events and reap
the rewards safely. One such opportunity is the application of machine learning and artificial …

The success of portfolio construction depends primarily on the future performance of stock
markets. Recent developments in machine learning have brought significant opportunities to …

Integrating return prediction of traditional time series models in portfolio formation can
improve the performance of original portfolio optimization model. Since machine learning …

In stock market forecasting, the identification of critical features that affect the performance of
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …

The creation of trustworthy models of the equities market enables investors to make better-
informed choices. A trading model may lessen the risks that are connected with investing …

Portfolio theory is an important foundation for portfolio management which is a well-studied
subject yet not fully conquered territory. This paper proposes a mixed method consisting of …

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 …

Big data is the collection of large datasets from traditional and digital sources to identify
trends and patterns. The quantity and variety of computer data are growing exponentially for …