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|Title: ||Data stream[s] mining in financial securities databases|
|Other Titles: ||Jin rong zheng quan shu ju ku de shu ju liu wa jue|
|Authors: ||Liu, Xiaoyan (劉曉艷)|
|Department: ||Department of Information Systems|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2007|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Data mining.|
Streaming technology (Telecommunications)
Finance -- Databases.
Securities -- Databases.
|Notes: ||ix, 166 leaves : ill. 30 cm.|
Thesis (Ph.D.)--City University of Hong Kong, 2007.
Includes bibliographical references (leaves 146-166)
CityU Call Number: QA76.9.D343 L577 2007
|Abstract: ||In recent years, advances in hardware technology have allowed us to automatically
record everyday transactions in stock trading market at a rapid rate. Such processes lead
to large amounts of data which grow at an unlimited rate. These data processes are
referred to as data streams. One of the key issues for data stream mining is online
mining of changes. By understanding the nature of changes in stock data stream, a user
may be able to convert this information into valuable trading decisions. Therefore, it is
useful to develop tools and techniques which track changes in securities database in real
In this research, we focus on the analysis of “T&Q” (Trade and Quote) data, which
contains detailed transaction information of securities tick by tick. One data
preprocessing technique and several online algorithms to monitor the flow changes in
the stock data are presented and their implications in the real securities data are
discussed in the thesis.
Since data streams are often in high volume, to reduce the data dimension, we propose a
new measure of class heterogeneity and develop a heuristic method to find the
approximate optimal discretization scheme in Chapter 3. The numeric evaluation shows
that the proposed method can be a good alternative to entropy-based discretization
Segmentation of data streams is useful to find the change in the trend of stock price. In
Chapter 4, we propose two novel online segmentation algorithms: the Feasible Window
Space method and its extension the Stepwise Feasible Window Space method. They are
piecewise-linear-model-based algorithms and always generate fewer segments with
acceptable representation error and less computation time. Extensive experiments on a
variety of real-world time series are conducted to evaluate the proposed methods. Monitoring the change in the stock order flow is a meaningful topic in the financial
intelligence field since the change in order follow precedes stock price change. In
Chapter 5 and Chapter 6, we propose two online change-point detection methods for the
stock order flow. One is the multilayer change-point detection algorithm which makes
use of the multiresolution property of wavelet transform. It is a non-parametric method.
The change-points obtained by this method are more reliable than those detected only
from the original time series. The other one is based on the Poisson distribution
assumption of the sequence which is a result of empirical finance study. This method
identifies the change-points incrementally.
The contributions of this research are two-fold: 1) From the viewpoint of technique, we
propose a data reduction method and a set of effective online change detection methods
for data streams and develop a series of theoretical results. Detection of changes
supports building data mining models in data streams more effectively and accurately.
And 2) From the viewpoint of application, the mining of changes in T&Q databases
provides a tool to reduce the information asymmetry in the securities market and support
the market short-term players making short-term trading decisions.|
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b2268761|
|Appears in Collections:||IS - Doctor of Philosophy |
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