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|Title: ||Reliability analysis of technical trading rules in security market based on conditional random field model|
|Other Titles: ||Ji yu tiao jian sui ji chang mo xing de zheng quan shi chang ji shu fen xi jiao yi zhun ze de ke kao xing fen xi|
|Authors: ||Yang, Zonghang ( 楊宗杭)|
|Department: ||Department of Information Systems|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2011|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Technical analysis (Investment analysis)|
Stocks -- Prices.
Random fields -- Mathematical models.
|Notes: ||CityU Call Number: HG4529 .Y38 2011|
vii, 83 leaves : ill. 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2011.
Includes bibliographical references (leaves -83)
|Abstract: ||Conditional random field model is a powerful model for dealing with time series with hidden
states and is becoming more and more widely used in various fields. This model generalizes
the hidden Markov chain model by releasing the independence assumptions between observations and obtains more accurate estimation results. Although conditional random field
model has been successfully applied in both academics and industry, it has rarely been used
for analyzing financial time series.
In financial industry, there are mainly two types of skills for investment analysis: fundamental analysis, and technical analysis. Although these two kinds of analysis are equally
important to security market participants, technical analysis does not receive enough attention from academics. Technical analysis is deemed to be a pseudoscience such as astrology
and alchemy by the researchers of mainstream finance. One of the most important reasons is
that technical analysis is contradictory with efficient market hypothesis, which is the theoretical foundation of modern finance. Also, the subjective nature of technical analysis makes it
unfalsifiable, which constricts its applications. There is positive evidence of the profitability
of technical analysis resulting from continued empirical studies testing the effectiveness of
technical analysis in different security markets. The rapid development of computer science
makes it possible to process large-scale calculations allowing increased interest in technical
analysis. Some researchers have used behavioral economics to explain extra profits of technical analysis and several have used statistics and machine learning techniques to investigate
the profitability of technical analysis. However, a uniform framework for technical analysis
is still not available. Even the definitions of technical analysis in different studies are not
In this thesis, we concentrate on technical analysis. Given trading signal sequence indicated by technical trading rules, we apply conditional random field to analyze this time
Considering the issues mentioned above technical trading signals may be unreliable.
The reliability analysis is important for the application of technical analysis. The research
objective of this thesis is to apply conditional random field model to improve the reliability
of technical analysis. Technical analysis depends on trading signals generated by technical
trading rules, and these signals form a time series which is the target for our research. To
achieve this objective the following problems should be solved.
1. How to scientifically measure the reliability of technical analysis?
2. What are the quantitative characteristics of trading signals for conditional random
3. How to use conditional random model to improve the reliability?
For solving these research questions, we firstly redefine technical analysis scientifically, and
then develop a framework based on conditional random field model for reliability of technical
analysis. The technical analysis is defined quantitatively. The framework for reliability
analysis is based on conditional random field model and also employs statistical methods
such as two tailed t-test. Heng Seng Index stocks in Hong Kong stock market were taken as
examples for experiments, and the famous simple moving average rule and Bollinger Bands
rule were tested. Compared with the trading strategy without technical trading rules, i.e.,
buy-and-hold strategy, it has been found that most simple moving average rules cannot bring
significantly larger returns while a Bollinger Bands rule is effective to indicate good trading
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b4085899|
|Appears in Collections:||IS - Doctor of Philosophy |
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