Visualizing stock market data with self‐organizing map

  • Title: Visualizing stock market data with self‐organizing map
    Author: Joseph, Joel; Indratmo, Indratmo
    Year: 2013
    Description: Finding 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.
    Peer Reviewed: Yes
    Type of Item: Conference Materials
    Publication Information: Joseph, 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
    Language: English

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