First Advisor

Harold A. Linstone

Date of Publication

1-1-1984

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Systems Science

Department

Systems Science

Language

English

Subjects

Interest rates -- Forecasting, Interest rates -- Mathematical models, Finance

DOI

10.15760/etd.149

Physical Description

4, xv, 270 leaves

Abstract

Much depends on the future course of interest rates. The decisions of families to make major purchases, the willingness of businesses to expand and invest, the rise and fall of the economy and stock market, the ability of lesser developed countries to repay their debts, the tenure of presidents and prime ministers--all of these may turn on whether interest rates increase or decrease in the months ahead. Several decision functions developed in the dissertation permit the direction of change of interest rates on long-term U. S. government bonds to be forecast correctly about 60% of the time. When the different models are combined, effectiveness is increased, and when the forecasts are dollar-weighted, performance in excess of 70% is possible. The results are evaluated in comparison with a Bayesian forecasting model and a 10,000-event Monte Carlo simulation of a random decision rule. The forecasting ability of the models is statistically significant at the 99% level of confidence. The dissertation reports on one of the first application of powerful techniques recently developed in cybernetics and engineering to forecasting the direction of change in interest rates. Two forecasting algorithms, called linear decision functions or linear classifiers, are derived using the principles of pattern recognition. Because they are recursively updated, both algorithms operate dynamically and adapt their performance to changes in the economic environment. One classifier, a modification of the widely used least-mean-squared-error algorithm, is adapted to permit monthly revision and to allow larger movements of interest rates to have greater weight in future decisions. The second algorithm permits refinement of the parameter estimates generated by the first. These formal, mathematical constructs are then supplied with financial variables--leading indicators of inflation and investment activity--to permit unconditional, ex-ante forecasts of the direction of change of interest rates on long-term government bonds over a one-month time horizon throughout the period 1969-82. The results should be of interest to investment managers, speculators, corporate treasurers, policymakers, economists and forecasters.

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Comments

Portland State University. Systems Science Ph. D. Program.

Persistent Identifier

http://archives.pdx.edu/ds/psu/4405

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