Download Forecasting Wheat Futures Prices: A Neural Network Application

Forecasting Wheat Futures Prices: A
Neural Network Application
Mary Malliaris Linda Salchenberger
1995 Annual Meeting of the Decision Sciences Institute
Introduction
• Forecasting agricultural commodities futures
prices is essential in determining a profitable
futures trading strategy
• There has been no significant improvement in
the success rate for short-term forecasts when
results from studies conducted prior to 1985
are compared with those conducted since
1985
Purpose
• To test the hypothesis that wheat futures
prices can be modeled as a nonlinear,
deterministic process using neural networks
NEURAL NETWORKS FOR PREDICTION
• Neural Networks are computational models
which have their conceptual origins in neural
physiology
• A set of processing elements or neurons
(nodes) are interconnected and organized in
layers
DATA
• January 1990 through December 1999
• Several agricultural markets (wheat, corn,
oats, and soybeans)
• There are five trading periods for wheat for
each calendar year and wheat contracts expire
in March, May, July, September, and
December.
VARIABLES
• The final networks included the following sixteen
variables: the change in the closing price, change in
volume, change in open interest, and the trend
variable as previously defined, for each of the four
agricultural commodities markets. We also included
the number of days to expiration of the current wheat
contract, for a total of 17 input variables.
• A trend indicator variable was computed for each of
the four agricultural commodities using the formula:
|(pt - pt-1 ) / pt | + |(vt - vt-1 ) / vt |
Measuring Network Performance
• The normalized RMSE
• Thiel's U1 statistic
RESULTS
• The networks achieved a lower MAD, RMSE,
and nRMSE in comparison to the random walk
model
• In every year except 1990, Thiel’s U1 statistic is
less than one, establishing that the network
predictions are better than the random walk
predictions for those years
SUMMARY
• The neural networks developed in this study
outperformed the random walk predictions,
and provided evidence that a nonlinear
structure exists in the data tested
• The implication is that prices can be predicted
using available information, thus, signaling the
existence of profitable trading strategies