@article{MAKHILLIJSC20138321144,
    title = {Computationally Intellectual Structure for Forecasting Share Price},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {3},
    pages = {218-222},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.218.222},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.218.222},
    author = {M.P. and},
    keywords = {Price to earnings ratio,price to earnings growth ratio,neural network,fuzzy inference system,NSE},
    abstract = {Earnings significant profit is the prime concern of the investor 
  and it is rather competent to determine the future value of a company’s 
  stock. To introspect challenges in stock market researchers need to overcome 
  the impediments and strive for further improving the focus on prediction of 
  share market. As the market prices are flexible it is nevertheless to say a 
  volatile and dynamic pattern of prediction is inevitable. In the present scenario 
  application of soft computing in stock market has taken a faster face of advancement 
  thereby inducing the hope of extracting market patterns at a speeder rate. The 
  research is concerned with development of forecasting the company’s 
  stock price by utilizing the facilities of fuzzy inference system and neural 
  network. The methodology employed is based on fundamental analysis and financial 
  market theory. Based on the literature review done the current valuation of 
  the stock-price to earnings ratio and future growth of the stock-price to earnings 
  growth ratio could have been employed to build successful investment strategies 
  in predicting stock market high. The empirical results obtained with stock data 
  of NSE shows that the proposed system can be effective to improve the accuracy 
  of stock price prediction.}
    }