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Full-Text Articles in Engineering
Crude Oil Price Prediction With Decision Tree Based Regression Approach, Engu Chen, Xin James He
Crude Oil Price Prediction With Decision Tree Based Regression Approach, Engu Chen, Xin James He
Journal of International Technology and Information Management
Crude oil is an essential commodity for industry and the prediction of its price is crucial for many business entities and government organizations. While there have been quite a few conventional statistical models to forecast oil prices, we find that there is not much research using decision tree models to predict crude oil prices. In this research, we develop decision tree models to forecast crude oil prices. In addition to historical crude oil price time series data, we also use some predictor variables that would potentially affect crude oil prices, including crude oil demand and supply, and monthly GDP and …
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
SMU Data Science Review
The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …