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Identifying Stock Similarity Based on Multi-event Episodes
Dattasharma, A., Tripathi, P.K. and G, S.
Predicting stock market movements is always difficult. Investors try to guess a stock's behavior, but
it often backfires. Thumb rules and intuition seems
to be the major indicator. One approach suggested
that instead of trying to predict one particular stock's
movement with respect to the whole market, it may
be easier to predict a stock A's movement based on
another stock B's movement; the reason being that
A may get affected by B after B's movement, giving
the investor invaluable time advantage. Evidently, it
would be very useful if a general framework can be
introduced that can predict such dependence based
on any user defined criterion. A previous paper laid
a basic framework for a single event based criterion,
but that was not enough where multiple criteria were
involved. This paper gives a general framework for
multiple events. We show that it is possible to encode
a time series as a string, where the final representation
depends on the user defined criterion. Then
finding string distances between two such encoded
time series can e ectively measure dependence. We
show that this technique is more powerful than the
'Pairs Trading strategy' as varied user defined criterion
can be handled while detecting similarity. We
apply our technique with one practical user defined
criterion. To the best of our knowledge, this is the
first attempt to find similarity between stock trends
based on user defined multiple event criteria. |
Cite as: Dattasharma, A., Tripathi, P.K. and G, S. (2008). Identifying Stock Similarity Based on Multi-event Episodes. In Proc. Seventh Australasian Data Mining Conference (AusDM 2008), Glenelg, South Australia. CRPIT, 87. Roddick, J. F., Li, J., Christen, P. and Kennedy, P. J., Eds. ACS. 153-162. |
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