| dc.creator (作者) | 陳樹衡;T.Yu;T.-W. Kuo | zh_TW |
| dc.date (日期) | 2004-07 | en_US |
| dc.date.accessioned | 9-Jan-2009 11:21:15 (UTC+8) | - |
| dc.date.available | 9-Jan-2009 11:21:15 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-Jan-2009 11:21:15 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/23009 | - |
| dc.description.abstract (摘要) | Using GP with lambda abstraction module mechanism to generate technical trading rules based on S&P 500 index, we find strong evidence of excess returns over buy-and-hold after transaction cost on the testing period from 1989 to 2002. The rules can be interpreted easily; each uses a combination of one to four widely used technical indicators to make trading decisions. The consensus among GP rules is high, with most of the time 80% of the evolved rules give the same decision. The GP rules give high transaction frequency. Regardless of market climate, they are able to identify opportunities to make profitable trades and out-perform buy-and-hold | - |
| dc.format | application/ | en_US |
| dc.language | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | No 200, Computing in Economics and Finance 2004 from Society for Computational Economics | en_US |
| dc.title (題名) | Using Genetic Programming with Lambda Abstraction to Find Technical Trading Rule | en_US |
| dc.type (資料類型) | conference | en |
