TITLE

Stock Price Prediction of Oil and Gas Corporation using Modified Genetic Algorithm Simulated Annealing Approach

AUTHOR(S)
Kopperundev, S.; Iyemperumal, A.
PUB. DATE
June 2014
SOURCE
Australian Journal of Basic & Applied Sciences;Jun2014, Vol. 8 Issue 9, p375
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background:Stock Market is amessy place for predicting since there are no significant rules to estimate or predict the price of shares in the stock market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis, etc. are all used to attempt to forecast the price in the stock market, but none of these methods are proven as a consistently acceptable prediction tool. Objective:In this paper, an artificial neural network based on Modified Genetic Algorithm-Simulated Annealing (MGASA) is used to predict the stock price index.In designing the model, the data of oil and gas company is taken from Bombay Stock Exchange (BSE) (2010- 2014).Result: The network is trained by 60% of the experimental data. 30% of the essential information which had been acknowledged for testing the appropriateness has been fed into the model. The predicted values were compared with the experimental values for evaluating the performance. The result obtained by using MGASA are in astounding concurrence with the experimental results and has high execution in stock price prediction. Conclusion: It is observed that the proposed algorithm significantly outperforms resulting in more profits. Hence, it can be concluded that the proposed algorithm is well suited for prediction of the stock prices.
ACCESSION #
97368437

 

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