Index tracking by means of fuzzy clustering using time series features

چکیده مقاله

Index tracking is an investment strategy aimed to replicate the
performance of a particular financial market which is usually shown by
a specific index. The strategy become more popular in recent years, as a
robust and sustainable one with a lower management and transaction
cost in comparison to speculations and active investments. This paper
extracted some basic features of the financial time series in order to
cluster stocks and make an index fund (tracking portfolio) to track
NASDAQ100 index. This is the first time of using fuzzy clustering
methods for index tracking, which trigger us to fill this gap of literature.
In this paper we proposed a new way of weighting to selected stocks for
index fund based on each stock membership degree in each cluster. At
last, the new fuzzy model was compared with an integer OR model, our
model highly reduced the running time of the model compared to OR
model, although its error is more than the OR model. The advantage of
reducing time cost shows itself in a larger markets which consists of
thousands of stocks.

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