| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w7613 |
| 来源ID | Working Paper 7613 |
| Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation | |
| Andrew W. Lo; Harry Mamaysky; Jiang Wang | |
| 发表日期 | 2000-03-01 |
| 出版年 | 2000 |
| 语种 | 英语 |
| 摘要 | Technical analysis, also known as charting,' has been part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness to technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution conditioned on specific technical indicators such as head-and-shoulders or double-bottoms we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value. |
| URL | https://www.nber.org/papers/w7613 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/565165 |
| 推荐引用方式 GB/T 7714 | Andrew W. Lo,Harry Mamaysky,Jiang Wang. Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. 2000. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w7613.pdf（663KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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