In recent years technical trading rules are widely known by more and
more people, not only the academics many investors also learn to apply
them in financial markets. One approach of constructing technical
trading rules is to use technical indicators, such as moving average(MA)
and filter rules. These trading rules are widely used possibly because
the technical indicators are simple to compute and can be programmed
easily. An alternative approach of constructing technical trading rules
is to rely on some chart patterns. However, the patterns and signals
detected by these rules are often made by the visual inspection through
human eyes. As for as I know, there are no universally acceptable methods
of constructing the chart patterns. In 2000, Prof. Andrew Lo and
his colleagues are the first ones who define five pairs of chart patterns
mathematically. They are Head-and-Shoulders(HS) & Inverted Headand-
Shoulders(IHS), Broadening tops(BTOP) & bottoms(BBOT), Triangle
tops(TTOP) & bottoms(TBOT), Rectangle tops(RTOP) & bottoms(
RBOT) and Double tops(DTOP) & bottoms(DBOT).
The basic formulation of a chart pattern consists of two steps: detection
of (i) extreme points of a price series; and (ii) shape of the pattern.
In Lo et al.(2000), the method of kernel smoothing was used to identify
the extreme points. It was admitted by Lo et al. (2000) that the
optimal bandwidth used in kernel method is not the best choice and
the expert judgement is needed in detecting the bandwidth. In addition,
their work considered chart pattern detection only but no buy/sell
signal detection. It should be noted that it is possible to have a chart
pattern formed without a signal detected, but in this case no transaction
will be made. In this thesis, I propose a new class of technical
trading rules which aims to resolve the above problems. More specifically,
each chart pattern is parameterized by a set of parameters which
governs the shape of the pattern, the entry and exit signals of trades.
Then the optimal set of parameters can be determined by using genetic
algorithms (GAs). The advantage of GA is that they can deal with a
high-dimensional optimization problems no matter the parameters to
be optimized are continuous or discrete. In addition, GA can also be
convenient to use in the situation that the fitness function is not differentiable
or has a multi-modal surface. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/161561 |
| Date | January 2011 |
| Creators | Shen, Rujun, 沈汝君 |
| Contributors | Yu, PLH |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B47870011 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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