Testing the profitability of technical analysis in Singapore and Malaysian stock markets
type: CreativeWork
description: Technical Analysis is a graphical method of looking at the history of price of a stock to deduce the probable future trend in its return. Being primarily visual, this technique of analysis is difficult to quantify as there are numerous definitions mentioned in the literature. Choosing one over the other might lead to data-snooping bias. This thesis attempts to create a universe of technical rules, which are then tested on historical data of Straits Times Index and Kuala Lumpur Composite Index. The technical indicators tested are Filter Rules, Moving Averages, Channel Breakouts, Support and Resistance and Momentum Strategies in Price. The technical chart patterns tested are Head and Shoulders, Inverse Head and Shoulders, Broadening Tops and Bottoms, Triangle Tops and Bottoms, Rectangle Tops and Bottoms, Double Tops and Bottoms. This thesis also outlines a pattern recognition algorithm based on local polynomial regression to identify technical chart patterns that is an improvement over the kernel regression approach developed by Lo, Mamaysky and Wang.
abstract: Technical Analysis is a graphical method of looking at the history of price of a stock to deduce the probable future trend in its return. Being primarily visual, this technique of analysis is difficult to quantify as there are numerous definitions mentioned in the literature. Choosing one over the other might lead to data-snooping bias. This thesis attempts to create a universe of technical rules, which are then tested on historical data of Straits Times Index and Kuala Lumpur Composite Index. The technical indicators tested are Filter Rules, Moving Averages, Channel Breakouts, Support and Resistance and Momentum Strategies in Price. The technical chart patterns tested are Head and Shoulders, Inverse Head and Shoulders, Broadening Tops and Bottoms, Triangle Tops and Bottoms, Rectangle Tops and Bottoms, Double Tops and Bottoms. This thesis also outlines a pattern recognition algorithm based on local polynomial regression to identify technical chart patterns that is an improvement over the kernel regression approach developed by Lo, Mamaysky and Wang.
author: ZOHEB JAMAL
keywords: Technical Analysis, Stock Market, Singapore, Malaysia, Chart Patterns, Polynomial Regression
date published: 2010-07-06
date modified: 2026-07-24T03:30:34Z
provider: National University of Singapore
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: National University of Singapore