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Unsupervised machine learning can interpret logarithmic returns and conditional volatility in commodity markets. k-means and hierarchical clustering can generate a financial ontology of markets for fuels, precious and base metals, and agricultural commodities. Manifold learning methods such as...
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Machine-learning regression models lack the interpretability of their conventional linear counterparts. Tree- and forest-based models offer feature importances, a vector of probabilities indicating the impact of each predictive variable on a model’s results. This brief note describes how to...
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This study extends previous work applying unsupervised machine learning to commodity markets. "Clustering Commodity Markets in Space and Time" [DOI: 10.1016/j.resourpol.2021.102162] examined returns and volatility in commodity markets. That paper supported the conventional ontology of commodity...
Persistent link: https://www.econbiz.de/10014356740
Demand forecasting relies heavily on traditional methods with well known limitations. Improved accuracy in predicting demand for mortgages, whether for purposes of purchase or refinance, is critical to profitability in home lending. To overcome obstacles to prediction using nonlinear...
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Markets for energy-related commodities figure prominently in developmental economics, international trade, and environmental law and policy. Markets for Brent oil, West Texas intermediate crude, gasoline, and diesel affect not only energy policy but also demand for agricultural commodities that...
Persistent link: https://www.econbiz.de/10014260468