RANDOM NOISE EFFECT IDENTIFICATION ON STOCK MARKET EFFICIENCY USING MST AND RMT+MST IN 21 STOCK EXCHANGES AROUND THE WORLD
One of the most used method in Econophysics to study stock market is Random Matrix Theory. This theory emerge from nuclear physics to compare eigenvector and eigenvalue of a system with random matrix to get the eigenvalues and eigenvectors free from noise influence. In other side, Minimum Spanning T...
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| Format: | Final Project |
| Language: | Indonesia |
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| Online Access: | https://digilib.itb.ac.id/gdl/view/39167 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | One of the most used method in Econophysics to study stock market is Random Matrix Theory. This theory emerge from nuclear physics to compare eigenvector and eigenvalue of a system with random matrix to get the eigenvalues and eigenvectors free from noise influence. In other side, Minimum Spanning Tree often used to draw the stock network. These methods rarely used together to analyze stock market. This paper try to combined them to make a more complete analysis. In this paper, Random Matrix Theory was used to do noise filtering and Minimum Spanning Tree used to express the the denoised correlation coefficient into tree graph. Then we will compare allometric coefficient of data with only MST and RMT+MST to analyze stock market efficiency. |
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