Researchers and practitioners have attempted to predict the financial market by analyzing
textual (eg, news articles and social media) and numeric data (eg, hourly stock prices, and …

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 …

In the era of big data, deep learning for predicting stock market prices and trends has
become even more popular than before. We collected 2 years of data from Chinese stock …

The investigation of the accuracy of methods employed to forecast agricultural commodities
prices is an important area of study. In this context, the development of effective models is …

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

The stock market forecasting is one of the most challenging application of machine learning,
as its historical data are naturally noisy and unstable. Most of the successful approaches act …

This paper provides a comprehensive state-of-the-art investigation of the recent advances in
data science in emerging economic applications. The analysis is performed on the novel …

Accurate and reliable travel time predictions in public transport networks are essential for
delivering an attractive service that is able to compete with other modes of transport in urban …

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 prediction is a very important problem in the economics field. With the
development of machine learning, more and more algorithms are applied in the stock market …