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Genetic Algorithms: Genesis of Stock Evaluation

Listed author(s):
  • Rama Prasad Kanungo

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

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

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Paper provided by EconWPA in its series Experimental with number 0404007.

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Length: 17 pages
Date of creation: 25 Apr 2004
Handle: RePEc:wpa:wuwpex:0404007
Note: Type of Document - pdf; pages: 17
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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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