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Using a Genetic Algorithm to Determine an Index of Leading Economic Indicators

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  • Farley, Arthur M
  • Jones, Samuel

Abstract

In this paper, we report results of experiments investigating use of a genetic algorithm to select an index of leading economic indicators. Genetic algorithms apply operations of mutation, reproduction, and crossover to candidate solutions according to their relative fitness scores in successive populations of candidates. For our problem, a candidate solution is a subset of the publicly available economic indicators, considered at varying temporal offsets. We use several methods to focus search for an index, including reusing economic indicators from best solution candidates found during previous runs of the algorithm. Indices of leading economic indicators were found that were able to predict, with reasonable accuracy, previously observed troughs in economic activity. Citation Copyright 1994 by Kluwer Academic Publishers.

Suggested Citation

  • Farley, Arthur M & Jones, Samuel, 1994. "Using a Genetic Algorithm to Determine an Index of Leading Economic Indicators," Computational Economics, Springer;Society for Computational Economics, vol. 7(3), pages 163-173.
    • Handle: RePEc:kap:compec:v:7:y:1994:i:3:p:163-73
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    Cited by:

    1. Mariano Matilla-Garcia, 2006. "Are trading rules based on genetic algorithms profitable?," Applied Economics Letters, Taylor & Francis Journals, vol. 13(2), pages 123-126.
    2. Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.

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