碩士 / 國立交通大學 / 工業工程與管理系所 / 106 / This research applies a stock trading methodology based on data mining (DM) techniques to the case of Foxconn Inc. A trading day is defined as a “Buy-Signal” if the stock price shall rise over 10% in the coming trading period (say, 90 days). The input of a DM technique involves 25 variables that might affect stock price; and the output is a binary variable (Buy or Not-Buy). This study involves ten years of given input/output data (2008/1-2017/12). The trading methodology involves three phases. Firstly, B-signal predictors are trained using different DM algorithms based on the first 65% of data (training dataset). Trained B-signal predictors are far from perfect in prediction accuracy; and one might propose conservative trading policies that take buy-action only when two or more (m) consecutive B-signal appears. Secondly, the best combination of B-signal predictor and trading policy is selected based on the subsequent 10% of data (validation dataset). Thirdly, the selected combination of B-signal predictor and trading policy is tested using the last 25% of data (testing dataset). Numerical experiments reveal that the return on investment of the selected trading method is 0.18%, which outperforms that -2.33% of the benchmark method.
Keyword: Data Mining, Trading Policy, Return On Investment
| Identifer | oai:union.ndltd.org:TW/106NCTU5031025 |
| Date | January 2018 |
| Creators | Chen, Chun-Tzu, 陳均姿 |
| Contributors | Wu, Muh-Cherng, Hung, Hui-Chih, 巫木誠, 洪暉智 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 37 |
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