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K-Means Clustering Approach for Improving Financial Forecasts

Author

Listed:
  • Èšole Alexandru - Adrian

    (The Romanian - American University)

Abstract

The following paper treats both types of forecasting: qualitative and quantitative. It highlightsthe importance of using both of them in order to achieve more accurate forecasts. It shows the flaws of quantitative forecasting when applying simple regression on large sets ofdata. Also, by using advanced data analysis techniques, such as Big Data algorithms, the results ofthe quantitative forecasting can be drastically improved and it can be worthy of taking intoconsideration when drawing the conclusions. K-means algorithm it proves to be very effective when a quantitative forecast needs to be done. By using it we can successfully execute “drill-down forecasting†into specific activities.

Suggested Citation

  • Èšole Alexandru - Adrian, 2018. "K-Means Clustering Approach for Improving Financial Forecasts," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 514-518, July.
    • Handle: RePEc:ovi:oviste:v:xviii:y:2018:i:1:p:514-518
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    References listed on IDEAS

    as
    1. J. Scott Armstrong & Kesten C. Green, 2018. "Forecasting methods and principles: Evidence-based checklists," Journal of Global Scholars of Marketing Science, Taylor & Francis Journals, vol. 28(2), pages 103-159, April.
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    More about this item

    Keywords

    clustering; k-means; quantitative; qualitative; forecasting;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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