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Full-Text Articles in Engineering
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 …
Improving Undersampling-Based Ensemble With Rotation Forest For Imbalanced Problem, Huaping Guo, Xiaoyu Diao, Hongbing Liu
Improving Undersampling-Based Ensemble With Rotation Forest For Imbalanced Problem, Huaping Guo, Xiaoyu Diao, Hongbing Liu
Turkish Journal of Electrical Engineering and Computer Sciences
As one of the most challenging and attractive issues in pattern recognition and machine learning, the imbalanced problem has attracted increasing attention. For two-class data, imbalanced data are characterized by the size of one class (majority class) being much larger than that of the other class (minority class), which makes the constructed models focus more on the majority class and ignore or even misclassify the examples of the minority class. The undersampling-based ensemble, which learns individual classifiers from undersampled balanced data, is an effective method to cope with the class-imbalance data. The problem in this method is that the size …