Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI

Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI

This study examines the profitability of the fractals method as a confirmation signal to the existing simple moving average trading strategy in the futures market of the Kuala Lumpur Stock Exchange (KLSE). To achieve this objective, historical data back-testing was used to evaluate the cost and risk...

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Main Author: Sim, Lin Shuen
Format: Thesis
Published: 2013
Subjects:
QA Mathematics
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spelling my-mmu-ep.38892013-08-22T00:18:16Z Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI 2013 Sim, Lin Shuen QA Mathematics This study examines the profitability of the fractals method as a confirmation signal to the existing simple moving average trading strategy in the futures market of the Kuala Lumpur Stock Exchange (KLSE). To achieve this objective, historical data back-testing was used to evaluate the cost and risk-adjusted return performance of three different strategies, namely the buy and hold (naïve)strategy, the moving average crossover strategy, and the moving average with fractal confirmation strategy. Sharpe ratio was used to determine the highest level of return with regards to risk levels. One-way ANOVA method was used to determine if there is any strategy which generates statistically higher average return than the rest. Overall, the results indicated that while there is no statistically significant difference among the three strategies, the moving average with fractal confirmation strategy has the highest Sharpe ratio, followed by the buy and hold strategy and the moving average with crossover system. The higher Sharpe ratio of the fractals method implies that the fractals method serve as a better confirmation signal than the moving average cross system. 2013 Thesis http://shdl.mmu.edu.my/3889/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters Multimedia University Graduate School of Management
institution Multimedia University
collection MMU Institutional Repository
topic QA Mathematics
spellingShingle QA Mathematics
Sim, Lin Shuen
Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI
description This study examines the profitability of the fractals method as a confirmation signal to the existing simple moving average trading strategy in the futures market of the Kuala Lumpur Stock Exchange (KLSE). To achieve this objective, historical data back-testing was used to evaluate the cost and risk-adjusted return performance of three different strategies, namely the buy and hold (naïve)strategy, the moving average crossover strategy, and the moving average with fractal confirmation strategy. Sharpe ratio was used to determine the highest level of return with regards to risk levels. One-way ANOVA method was used to determine if there is any strategy which generates statistically higher average return than the rest. Overall, the results indicated that while there is no statistically significant difference among the three strategies, the moving average with fractal confirmation strategy has the highest Sharpe ratio, followed by the buy and hold strategy and the moving average with crossover system. The higher Sharpe ratio of the fractals method implies that the fractals method serve as a better confirmation signal than the moving average cross system.
format Thesis
qualification_level Master's degree
author Sim, Lin Shuen
author_facet Sim, Lin Shuen
author_sort Sim, Lin Shuen
title Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI
title_short Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI
title_full Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI
title_fullStr Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI
title_full_unstemmed Analysis of fractal trading strategy with the FTSE Bursa Malaysia KLCI
title_sort analysis of fractal trading strategy with the ftse bursa malaysia klci
granting_institution Multimedia University
granting_department Graduate School of Management
publishDate 2013
_version_ 1747829562019938304

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