| dc.contributor | 經濟系 | |
| dc.creator (作者) | Chen, Shu-heng;He, Hongxing;Chen, Jie;Jin, Huidong | |
| dc.creator (作者) | 陳樹衡 | zh_TW |
| dc.date (日期) | 2006 | |
| dc.date.accessioned | 28-Apr-2015 14:28:58 (UTC+8) | - |
| dc.date.available | 28-Apr-2015 14:28:58 (UTC+8) | - |
| dc.date.issued (上傳時間) | 28-Apr-2015 14:28:58 (UTC+8) | - |
| dc.identifier.isbn (ISBN) | 10.2991/jcis.2006.135 | |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/74888 | - |
| dc.description.abstract (摘要) | This paper outlines a data mining approach to analysis and prediction of the trend of stock prices. The approach consists of three steps, namely parti- tioning, analysis and prediction. A modification of the commonly used k-means clustering algorithm is used to partition stock price time series data. After data partition, linear regression is used to analyse the trend within each cluster. The results of the linear regression are then used for trend prediction for windowed time series data. The approach is efficient and effective at predicting forward trends of stock prices. Using our trend prediction methodology, we propose a trading strategy TTP (Trading based on Trend Prediction). Some preliminary results of applying TTP to stock trading are reported. | |
| dc.format.extent | 47102 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Joint Conference on Information Sciences, Advances in Intelligent Systems Research | |
| dc.subject (關鍵詞) | Data Mining; Clustering; k-means; Time Series; Stock Trading | |
| dc.title (題名) | Stock Trend Analysis and Trading Strategy | |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.2991/jcis.2006.135 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.2991/jcis.2006.135 | en_US |
