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Genetic Algorithms: Genesis of Stock Evaluation

Author

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  • Rama Prasad Kanungo

    (Asian Accounting, Finance & Business Research Unit, CARBS)

Abstract

The uncertainty of predicting stock prices emanates pre-eminent concerns around the functionality of the stock market. The possibility of utilising Genetic Algorithms to forecast the momentum of stock price has been previously explored by many optimisation models that have subsequently addressed much of the scepticism. In this paper the author proposes a methodology based on Genetic Algorithms and individual data maximum likelihood estimation using logit model arguing that forecasting discrepancy can be rationalised by combined approximation of both the approaches. Thus this paper offers a methodological overture to further investigate the anomalies surrounding stock market. In the main, this paper attempts to provide a temporal dimension of the methods transposed on recurrent series of data over a fixed window conjecturere

Suggested Citation

  • Rama Prasad Kanungo, 2004. "Genetic Algorithms: Genesis of Stock Evaluation," Experimental 0404007, EconWPA.
  • Handle: RePEc:wpa:wuwpex:0404007
    Note: Type of Document - pdf; pages: 17
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    File URL: http://econwpa.repec.org/eps/exp/papers/0404/0404007.pdf
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    References listed on IDEAS

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    1. Lee Altenberg, 1994. "The Evolution of Evolvability in Genetic Programming," Working Papers 94-02-007, Santa Fe Institute.
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    More about this item

    Keywords

    Genetic Algorithms; Individual Maximum Likelihood Estimation; Stock Price;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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