Fuzzy Cluster Analysis of Financial Time Series and Their Volatility Assessment

Every company listed on the London Stock Exchange is classified into an industry sector based on its primary activity, however, it may be both more interesting and valuable to group similarly performing companies based on their historical stock price record over a long period of time. Using fuzzy clustering analysis with a correlation-based metric, we obtain a more insightful categorization of the companies into groups with fuzzy boundaries, giving arguably a more realistic and detailed view of their relationships. Once cluster analysis is performed, we analyze the behaviour of discovered groups in terms of the volatility of their returns using both standard deviation and exponentially weighted moving average. This approach has the potential to be of practical relevance in the context of diversified portfolio construction as it can detect fuzzy clusters of correlated stocks that have lower inter-cluster correlation, analyze their volatility and sample potentially less risky combination of assets.