• DocumentCode
    3539455
  • Title

    Indonesia stock exchange liquid stocks identification using self-organizing map

  • Author

    Widiputra, Harya ; Christianto, Leo

  • Author_Institution
    Fac. of Inf. Technol., Perbanas Inst., Jakarta, Indonesia
  • fYear
    2012
  • fDate
    14-15 Aug. 2012
  • Firstpage
    131
  • Lastpage
    136
  • Abstract
    Being able to gain profit is one of the main objectives of people who work in the financial area. Yet, the volatility of the stock price movement makes the task of predicting future condition of a stock market difficult to accomplish. One approach known to provide a safe strategy in stock trading is by collecting only those stocks, which are considered as liquid. The Indonesia Stock Exchange market (IDX) publishes this list of liquid stocks every six months (known as the LQ45 list). Having prior knowledge of stocks that will be in the upcoming LQ45 list would then be a great help to assist people who work in the Financial area in planning their future investment and gain profit. This study proposed the use of unsupervised data mining technique called the Self-Organizing Map algorithm (SOM) to perform early identification of liquid stocks from all listed companies in the IDX by dynamically creating a group of liquid stocks based on their historical states.
  • Keywords
    data mining; investment; pricing; profitability; self-organising feature maps; stock markets; IDX; Indonesia stock exchange liquid stocks identification; LQ45 list; SOM; investment; profit gaining; selforganizing map algorithm; stock price movement volatility; stock trading; unsupervised data mining technique; Clustering algorithms; Companies; Data mining; Investments; Lattices; Liquids; Stock markets; Self-Organizing Map; Unsupervised learning; liquid stocks identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Uncertainty Reasoning and Knowledge Engineering (URKE), 2012 2nd International Conference on
  • Conference_Location
    Jalarta
  • Print_ISBN
    978-1-4673-1459-6
  • Type

    conf

  • DOI
    10.1109/URKE.2012.6319526
  • Filename
    6319526