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M. E. MalliarisLoyola University Chicago, [email protected]
S. G. Malliaris
Yale University, [email protected]
Crude oil Heating oil Gasoline Natural gas Propane
CL HO PN HU NG
CL 1 - - - -
HO 0.959721 1 - - -
PN 0.842248 0.881154 1 - -
HU 0.964905 0.926191 0.847288 1 -
NG 0.669869 0.731288 0.677979 0.657551 1
Daily Spot Prices Five Variables From Jan 3, 1994 and Dec 31, 2002 The input variables:
daily closing spot pricepercent change in daily closing spot price
from the previous daystandard deviation over the previous 5
trading daysStandard 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.
Avg. Absolute Error Mean Squared Error
SimpleRegression
Neural Net Simple
Regression
Neural Net
CL 1.973 2.126 1.120 6.013 6.653 2.269
HO 0.051 0.055 0.035 0.004 0.005 0.002
HU 0.057 0.053 0.029 0.006 0.004 0.001
NG 0.388 0.414 0.218 0.240 0.242 0.075
PN 0.041 0.061 0.080 0.003 0.006 0.009
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