A polygon description based similarity measurement of stock market behavior

Abstract

In this paper. we propose and implement a data modeling system to measure the similarity of stock market behavior in different time periods. To implement this system, we also propose a data distribution approximation model, Polygon descriptor, and a shape difference measurement. called Deforming distance method The Polygon descriptor can model a data distribution by characterizing the data dependency relationship among the data variables, and the Deforming distance is designed to be translation, scale, and rotation Invariant. The Polygon descriptor and Deforming distance method are combined to measure the similarity between data sets based on the shape of data distribution. Stock price and EPS (earn per share) data during different time periods were selected to verify the modeling and measuring power of the proposed model and method. The Polygon descriptor is used to model the collected Stock price and EPS data for their dependency relationship Then, difference between Polygon descriptors can be measured by using the Deforming distance Measurement method for their Deforming distance Values Based oil the Deforming difference Values. the similarity of stock market behaviors in two time periods can be assessed, and the similar stock market behavior of different time periods can be identified To demonstrate the capabilities of the proposed method and system. real-world data of Taiwan stock market during the period from 1986 to 2006 were collected and used. Experimental results show that the Polygon descriptor successfully captures the data dependencies between stock price and EPS, and the Deforming difference based similarity measurement estimates the changes of market behavior better than some commonly used methods. A web-based prototype system is available at http://www.csie.cntu.edu tw/-pslar /TWStockSldx/ for public trial. evaluation, comments and suggestions (C) 2009 Elsevier Ltcl All rights reserved.