• DocumentCode
    603361
  • Title

    Stock Market Prediction Accuracy Analysis Using Kappa Measure

  • Author

    Gupta, Rajesh ; Garg, Nidhi ; Singh, Sushil

  • Author_Institution
    Dept. of Inf. & Commun. Technol. Manipal Inst. of Technol., Manipal Univ., Manipal, India
  • fYear
    2013
  • fDate
    6-8 April 2013
  • Firstpage
    635
  • Lastpage
    639
  • Abstract
    The nature of stock market is highly stochastic which can only be predicted. There are various companies and news channels which uses different data analysis tool to forecast the behavior of the stocks on day to day basis. They also provide tips and recommendations to buy and sell certain stocks which will lead to more profit. As there are many news channels, websites and organizations which are doing this, it is very difficult for the buyer/seller, to judge whom to believe and whom to ignore. In this paper, we have applied kappa measure to quantify the accuracy of stock market prediction by various media houses. The stock with the highest kappa measure can be considered to be the best stock to buy. Moreover, Kappa measure also finds the risk involved in the purchase/sale of each shares. Thus instead of believing on a particular channel, newspaper or website for the stocks that should be purchased/sold, its combinations are used which improves the confidence in stock market recommendation.
  • Keywords
    Web sites; data analysis; recommender systems; stock markets; Web sites; data analysis tool; kappa measure; stock market prediction accuracy analysis; stock market recommendation; Accuracy; Companies; Investment; Media; Neural networks; Prediction algorithms; Stock markets; Equity; Kappa Analysis; Market Recommendation; Shares; Stock Market Forecasting; Stock tips;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication Systems and Network Technologies (CSNT), 2013 International Conference on
  • Conference_Location
    Gwalior
  • Print_ISBN
    978-1-4673-5603-9
  • Type

    conf

  • DOI
    10.1109/CSNT.2013.136
  • Filename
    6524479