Since stock markets are volatile, dynamic and complicated, forecasting stock market return is considered as a challenging task. Nevertheless, researchers have developed various linear and non linear methods for effective forecasting. Among these neural networks are most suitable for forecasting...

The following work aims to review the most important research from computational intelligence applied to the financial price prediction problem. The article is organized as follows: The first section summarizes the role of predictability in the Neoclassical financial world. This section also...

We compare parametric and machine learning techniques (namely: Neural Networks) for in-sample modeling of the yield curve of the BRICS countries (Brazil, Russia, India, China, South Africa). To such aim, we applied the Dynamic De Rezende-Ferreira five-factor model with time-varying decay...

The COVID-19 pandemic spread rapidly around the world and is currently one of the most leading causes of death and heath disaster in the world. Turkey, like most of the countries, has been negatively affected by COVID-19. The aim of this study is to design a predictive model based on artificial...

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 intelligence for stock market prediction. The stock market is turbulent, yet using artificial intelligence to...