K-Means Clustering Approach for Improving Financial Forecasts

Èšole Alexandru - Adrian ()
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Èšole Alexandru - Adrian: The Romanian - American University

Ovidius University Annals, Economic Sciences Series, 2018, vol. XVIII, issue 1, 514-518

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.

Keywords: clustering; k-means; quantitative; qualitative; forecasting (search for similar items in EconPapers)
JEL-codes: G17 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xviii:y:2018:i:1:p:514-518

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