Gold has multiple attributes and its price is affected by various factors in the market. This paper studies the dynamic relationship between the gold price returns and its affecting factors. Then we use the STL-ETS, neural network and Bayesian structural time series model to predict the gold...

Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge...

In this paper, a feed-forward artificial neural network (ANN) is used to price Johannesburg Stock Exchange (JSE) Top 40 European call options using a constructed implied volatility surface. The prices generated by the ANN were compared to the prices obtained using the Black-Scholes (BS) model....

Energy modeling and forecasting are key to providing insightful energy-related policy decisions that could propel positive economic growth and sustainable development. In this study, the Non-Linear Autoregressive Exogenous Neural Networks (NARX) and Multiple Non-Linear Regression (MNLR) models...

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 cycle time of a wafer lot refers to the time that the wafer lot has experienced from its input to output. Predicting the cycle time of each wafer lot is a crucial task for a wafer fabrication factory (wafer fab), but existing prediction methods cannot achieve 100% accuracy. Therefore, if the...