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Department of Mathematics & Computing Science Saint Mary's University Halifax, Nova Scotia, B3H3C3, Canada

A volatile trading pattern on a given day in a financial market presents an opportunity for traders to maximize the difference between their buying and selling prices. In order to formulate trading strategies it may be advantageous to study typical trading patterns. This paper first describes how clustering can be used to profile typical volatile trading patterns. Fuzzy cmeans provides a better description of individual trading patterns, since they can display certain aspects of different trading profiles. While daily volatility profile is a useful indicator for trading a stock, the volatility history is also an important part of the decision making process. This paper further proposes a fuzzy temporal meta-clustering algorithm that not only captures the daily volatility but also puts it in a historical perspective by including the volatility of previous two weeks in the meta-profile.
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Citation: Pawan Lingras, Farhana Haider, Matt Triff. FUZZY TEMPORAL META-CLUSTERING OF FINANCIAL TRADING VOLATILITY PATTERNS. Big Data and Information Analytics, 2017, 2(3&4): 219-238. doi: 10.3934/bdia.2017018


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