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

Kopperundev, S.; Iyemperumal, A.
June 2014
Australian Journal of Basic & Applied Sciences;Jun2014, Vol. 8 Issue 9, p375
Academic Journal
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.


Related Articles

  • Editorial. Williams, D. // Transactions of the Institute of Measurement & Control;2000, Vol. 22 Issue 2, p123 

    Editorial. Comments on the superior performance of genetic algorithms over the traditional search methods. Utilization of candidate solutions; Effectivity of schedule optimization system for manufacturing; Use of genetic algorithms and simulated annealing to schedule the maintenance of power...

  • A message from the guest editor. Vallejos, Ronny O. // Chilean Journal of Statistics (ChJS);2011, Vol. 2 Issue 2, p1 

    An introduction is presented in which the editor discusses various reports within the issue on topics including positive spatial autocorrelation, simulated annealing and hybrid genetic algorithm.

  • Modelling research and development: How do firms solve design problems? Cooper, Ben // Journal of Evolutionary Economics;2000, Vol. 10 Issue 4, p395 

    Abstract. One way of thinking about research and development is to recognise that firms are trying to solve particular design problems. We often build these design problems into our models, but are forced to oversimplify them in order to make the models solvable. The approach taken in this paper...

  • A Double-Layered Learning Approach to Acquiring Rules for Classification: Integrating Genetic Algorithms with Similarity-Based Learning. Sikora, Riyaz; Shaw, Michael // ORSA Journal on Computing;Spring94, Vol. 6 Issue 2, p174 

    In this paper, we describe a machine learning technique based on a double-layered architecture and Genetic Algorithms (GAs), which can be used to learn decision rules for financial classification. Once the rules have been acquired, they can be stored in an expert system for future application....

  • An Effective Stock Portfolio Trading Strategy using Genetic Algorithms and Weighted Fuzzy Time Series. Yungho Leu; Tzu-I Chiu // International Journal of Digital Content Technology & its Applic;Apr2012, Vol. 6 Issue 6, p333 

    Investments in a stock market may incur risk. To reduce the risk of an investment, many portfolio selection methods have been proposed. By buying several stocks together, a portfolio selection method aims at maximizing the return rate of an investment given a predefined risk level. To build an...

  • APPLICATIONS OF DATA MINING IN STOCK MARKET. KAUR, SAVINDERJIT; MANGAT, VEENU // Journal of Information & Operations Management;2012, Vol. 3 Issue 1, p86 

    Data mining is being actively applied to stock market since 1980s. The various aspects of stock market to which data mining has been applied include predicting stock indices, predicting stock prices, portfolio management, portfolio risk management, trend detection, designing recommender systems...


    The genetic algorithm is numbered among less formal methods which allows using it in different areas including forecasting. However, there is a question about efficiency of that instrument. We decided to check inner procedures of the algorithm affecting gaining speed and quality of solutions. We...

  • Detection of Regime Switching in Stock Prices before "Window Dressing" at the Year End Using Genetic Algorithm. Sudtasan, Tatcha // International Journal of Intelligent Technologies & Applied Stat;2012, Vol. 5 Issue 2, p143 

    The study uses genetic algorithm to discover buying signals of six stocks in the Stock Exchange of Thailand within 30 days before the year end. The buying signals concentrate around 15 to 22 days before the last working day of the year. The regime switching of most stocks takes place not less...

  • Oil, interest rates pressure stocks.  // Dow Theory Forecasts;3/21/2005, Vol. 61 Issue 12, p3 

    The article reports that after reaching significant highs in the first week of March, the major stock exchange averages have been under pressure because of concerns regarding interest rates and oil prices. Near-term volatility would not be surprising, and a pickup in sector rotation seems...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics