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Visualizing stock market data with self‐organizing map

Faculty Advisor

Date

2013

Keywords

Abstract (summary)

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.

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

DOI

Notes

Item Type

Presentation

Language

English

Rights

All Rights Reserved