Genetic Algorithms: Genesis of Stock Evaluation

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