Forecasting the periodic net discount rate with genetic programming

Neal F. Wagner, Mark Andrew Thompson

Research output: Contribution to journalArticle

Abstract

This paper examines the periodic net discount rate using genetic programming (GP) techniques to build better short-term forecasts. Standard GP techniques require human judgment as to which data window to use, which may be problematic due to structural breaks and persistence (or long memory) in the net discount rate. We use a recently developed extension of GP to overcome this problem. While our results show no significant out-of-sample forecast improvement relative to the linear alternative or random walk model over the full sample, they do provide evidence as to the stochastic nature of the net discount rate considering the AR(3) model yielded lower forecasting errors in the post-1982 sample.

Original languageEnglish (US)
Article number4
JournalJournal of Business Valuation and Economic Loss Analysis
Volume4
Issue number1
DOIs
StatePublished - Dec 1 2009
Externally publishedYes

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Genetic programming
Discount rate
Long memory
Structural breaks
Forecasting error
Random walk model
Persistence
Out-of-sample forecasting

Keywords

  • Forecasts
  • Genetic programming
  • Periodic net discount rate

ASJC Scopus subject areas

  • Business and International Management
  • Accounting
  • Finance
  • Economics and Econometrics
  • Strategy and Management

Cite this

Forecasting the periodic net discount rate with genetic programming. / Wagner, Neal F.; Thompson, Mark Andrew.

In: Journal of Business Valuation and Economic Loss Analysis, Vol. 4, No. 1, 4, 01.12.2009.

Research output: Contribution to journalArticle

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