Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27739
Title: Discovering pattern associations in Hang Seng Index constituent stocks
Authors: Lui, KM
Hu, L
Chan, KCC 
Keywords: Efficient market hypothesis
Hang Seng Index
Price behavior
Associative relationships
Artificial intelligence
Issue Date: 2010
Publisher: Canadian Center of Science and Education
Source: International journal of economics and finance, 2010, v. 2, no. 2, p. 43-52 How to cite?
Journal: International journal of economics and finance 
Abstract: The problem of finding patterns in financial time series has been tackled by systematic observations of trends, statistical analysis or the use of artificial intelligence techniques in trend analysis. These techniques are more for the discovering of patterns in data rather than the understanding of association relationships between the discovered patterns. As time series patterns often overlap with each other, identifying and discovering association relationships among them can be very challenging. To tackle these problems, we propose here a method to determine if there exists any association relationship between two sequential patterns in a financial time series. The method is based on the use of machine learning techniques and has been tested with data from Hang Seng Index (HSI) constituent stocks. The results reveal that there is statistical evidence of association relationships between some of the stocks whereas there is no evidence for such a relationship between some others. We conclude that the price behavior of these HSI stocks is easier to understand than that of the HSI index.
URI: http://hdl.handle.net/10397/27739
ISSN: 1916-971X(Print)
1916-9728 (Online)
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

49
Last Week
2
Last month
Checked on Jul 23, 2017

Google ScholarTM

Check



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.