M. E. Malliaris Loyola University Chicago, S. G. Malliaris Yale University,

 Crude oil  Heating oil  Gasoline  Natural gas  Propane

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 Daily Spot Prices  Five Variables  From Jan 3, 1994 and Dec 31, 2002  The input variables:  daily closing spot price  percent change in daily closing spot price from the previous day  standard deviation over the previous 5 trading days  Standard deviation over the previous 21 trading days

 Regression  Neural Network  Each neural network model used twenty-one inputs (the 20 original fields, plus the non- numeric cluster identifier), one hidden layer with twenty nodes, and one output node.

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 There is enough information contained in a simple set of price data to allow effective forecasting  An ability to predict the price of a given source good does not necessarily imply an ability to predict the price of such a good’s byproducts  Traditional statistical techniques for understanding and extracting information about trends are often less than ideal in market situations