Visualizing stock market data with self‐organizing map

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dc.contributor.authorJoseph, Joel
dc.contributor.authorIndratmo, Indratmo
dc.date.accessioned2017-05-10
dc.date.accessioned2022-05-28T00:37:09Z
dc.date.available2022-05-28T00:37:09Z
dc.date.issued2013
dc.description.abstractFinding useful patterns in stock market data requires tremendous analytical skills and effort. To help investors manage their portfolios, we developed a tool for clustering and visualizing stock market data using an unsupervised learning algorithm called Self-Organizing Map. Our tool is intended to assist users in identifying groups of stocks that have similar price movement patterns over a period of time. We performed a visual analysis by comparing the resulting visualization with Yahoo Finance charts. Overall, we found that the Self-Organizing Map algorithm can analyze and cluster the stock market data reasonably.
dc.format.extent752.22 KB
dc.format.mimetypePDF
dc.identifier.citationJoseph, J. & Indratmo (2013). Visualizing stock market data with self-organizing map. In Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference, St. Pete Beach, Florida, USA, 488–491. Retrieved from https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS13/paper/view/5937/6123
dc.identifier.urihttps://hdl.handle.net/20.500.14078/863
dc.languageEnglish
dc.language.isoen
dc.rightsAll Rights Reserved
dc.titleVisualizing stock market data with self‐organizing map
dc.typePresentation
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