• DocumentCode
    2016576
  • Title

    Financial Forecasting: Advanced Machine Learning Techniques in Stock Market Analysis

  • Author

    Yoo, Paul D. ; Kim, Maria H. ; Jan, Tony

  • Author_Institution
    Dept. of Comput. Syst., Univ. of Technol., Sydney, NSW
  • fYear
    2005
  • fDate
    24-25 Dec. 2005
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    The prediction of stock market has been an important issue in the field of finance, mathematics and engineering due to its great potential financial gain. In addition, uncertainty in the prediction of the financial time series has attracted interest from many researchers. In this study, we present recent developments in stock market prediction models, and discuss their strengths and limitations. In addition, we investigate diverse macroeconomic factors and their issues in the prediction of stock market. From this study, we found that incorporating event information into the prediction models plays important roles for more accurate prediction. Hence, an accurate event weighting method and a stable automated event extraction system are required for more accurate and reliable stock market prediction
  • Keywords
    economic forecasting; learning (artificial intelligence); macroeconomics; stock markets; time series; event weighting method; financial forecasting; financial time series; machine learning; macroeconomic factor; stock market analysis; Data mining; Economic forecasting; Finance; Internet; Machine learning; Mathematics; Neural networks; Predictive models; Stock markets; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    9th International Multitopic Conference, IEEE INMIC 2005
  • Conference_Location
    Karachi
  • Print_ISBN
    0-7803-9429-1
  • Electronic_ISBN
    0-7803-9430-5
  • Type

    conf

  • DOI
    10.1109/INMIC.2005.334420
  • Filename
    4133435