The Study of Entropy Based Classification and Clustering Rules for Semiconductor Industry of Optimal Stock Portfolio
碩士 === 嶺東科技大學 === 高階主管企管碩士在職專班 === 103 === Since the stock market has the characteristics of a high rate of return, the investors generally have trouble on analyzing the stock market information. In this study, we focused on semi-conductor stock market. The data mining techniques are adopted to find...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/29564487452757898125 |
| Summary: | 碩士 === 嶺東科技大學 === 高階主管企管碩士在職專班 === 103 === Since the stock market has the characteristics of a high rate of return, the investors generally have trouble on analyzing the stock market information. In this study, we focused on semi-conductor stock market. The data mining techniques are adopted to find investment value of the stock market. The particle swarm optimization (PSO+kmeans) with entropy-based classification(EBC) is used to develop the stock market portfolio model. The Weighted Price Index of the Taiwan Stock and Return of Interest (ROI) are collected to investigate the analyzed model. The entire study has two stages. The EBC is used as feature extraction on finance attributes from stock company. Then, PSO+kmeans is used as clustering method to select stock company as target portfolio. There are four case studies: (a) Original data with Weighted Price Index of the Taiwan Stock (b) Original data with Return of Interest (c) feature extraction with Weighted Price Index of the Taiwan Stock (d) feature extraction with Return of Interest. All the finicial data is normalized and PSO+kmeans is used as selection tool for portfolio modelling . The model is evaluated and results of accuracy is investigated.
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