Data Mining for Overreaction in Financial Markets

A. Duran and G. Caginalp (USA)


Data mining, overreaction, computational finance software, financial markets, and bubble.


We study overreaction and the cumulative effect of the con secutive local overreaction patterns in financial markets. The ”overreaction diamond” pattern [1] is one of the key components of a financial market bubble. The cumulative effect of the consecutive short term overreactions arising from the deviation of stock prices from their fundamentals can be explained by attribution theory, feedback traders, affect and representativeness theories, and reference points in investments. We study large set of financial data and propose a data mining method by exploiting the relative cumulative sentiment of the investors. This leads to a po tential for the implementation of suitable algorithms and the preparation of software packages that can be useful for prediction of various stages of overreaction and bubbles.

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