| dc.creator (作者) | 陳樹衡;T.-W. Kuo | zh_TW |
| dc.creator (作者) | Chen,Shu-Heng | - |
| dc.date (日期) | 2004 | en_US |
| dc.date.accessioned | 9-一月-2009 11:26:22 (UTC+8) | - |
| dc.date.available | 9-一月-2009 11:26:22 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-一月-2009 11:26:22 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/23057 | - |
| dc.description.abstract (摘要) | Using Quinlan’s Cubist, this paper examines whether there is a consistent, interpretation of the efficient market hypothesis between financial econometrics and machine learning. In particular, we ask whether machine learning can be useful only in the case when the market is not efficient. Based on the forecasting performance of Cubist in our artificial returns, some evidences seems to support this consistent interpretation. However, there are a few cases whereby Cubist can beat the random walk even though the series is independent. As a result, we do not consider that the evidence is strong enough to convince one to give up his reliance on machine learning even though the efficient market hypothesis is sustained. | - |
| 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) | Computational Intelligence in Economics and Finance Advanced Information Processing 2004, pp 288-296 | en_US |
| dc.title (題名) | Are Efficient Markets Really Efficient?: Can Financial Econometric Tests Convince Machine-Learning People? | en_US |
| dc.type (資料類型) | conference | en |
