Identifying Stock Similarity Based on Multi-event Episodes

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 fin...

• Type: Conference
• Year: 2008
• Where: AUSDM
• Authors: Abhi Dattasharma, Praveen Kumar Tripathi, Sridhar G
• Keywords: AUSDM 2008, Data Mining, Particular Stock Movement, Stock Market Movements, User Defined Criterion