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Study on the Macroeconomic Model Based on the Genetic Algorithm.

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This study introduces a genetic algorithm for macroeconomic indicator prediction, enhancing accuracy and prediction cycles for stock prices and indices compared to existing software. The developed high-throughput scheme offers superior forecasting capabilities.

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Area of Science:

  • Computational Economics
  • Financial Forecasting
  • Algorithm Development

Background:

  • Macroeconomic indicators are crucial for financial transactions, trade, and policy.
  • Existing prediction software often lacks the desired accuracy and efficiency.
  • One-dimensional array data presents unique challenges for accurate prediction.

Purpose of the Study:

  • To design a reliable, general-purpose prediction software for macroeconomic indicators.
  • To improve the accuracy and prediction cycle of macroeconomic forecasting.
  • To develop a high-throughput macroeconomic timing prediction scheme using a genetic algorithm.

Main Methods:

  • Utilized a genetic algorithm as the core predictive engine.
  • Employed the least square method to determine data deviation.
  • Implemented binary t-correction for one-dimensional matrix data.
  • Constructed a calculation model in Matlab for algorithm verification.

Main Results:

  • The genetic algorithm-based model demonstrated higher accuracy than paid software (10jqka).
  • Achieved a superior prediction cycle for stock prices and stock indices.
  • The developed simulation software possesses advanced prediction functions.

Conclusions:

  • The genetic algorithm offers a robust and efficient approach to macroeconomic indicator prediction.
  • The proposed high-throughput scheme significantly enhances forecasting capabilities.
  • This method provides a valuable alternative to existing commercial prediction tools.