Non-transformed principal component technique on weekly construction stock market price
The fast-growing urbanization has contributed to the construction sector be- coming one of the major sectors traded in the world stock market. In general, non- stationarity is highly related to most of the stock market price pattern. Even though stationarity transformation is a common approach, yet...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2019
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/84878/1/MuhammadHisyamLee2019_Non-transformedPrincipalComponentTechnique.pdf http://eprints.utm.my/id/eprint/84878/ https://dx.doi.org/10.11113/matematika.v35.n2.1112 |
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| Summary: | The fast-growing urbanization has contributed to the construction sector be- coming one of the major sectors traded in the world stock market. In general, non- stationarity is highly related to most of the stock market price pattern. Even though stationarity transformation is a common approach, yet this may prompt to originality loss of the data. Hence, the non-transformation technique using a generalized dynamic principal component (GDPC) were considered for this study. Comparison of GDPC was performed with two transformed principal component techniques. This is pertinent as to observe a larger perspective of both techniques. Thus, the latest weekly two-years observations of nine constructions stock market price from seven different countries were applied. The data was tested for stationarity before performing the analysis. As a re- sult, the mean squared error in the non-transformed technique shows eight lowest values. Similarly, eight construction stock market prices had the highest percentage of explained variance. In conclusion, a non-transformed technique can also present a better result outcome without the stationarity transformation. |
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