Anonparametric Kernel Regression Method for the Recognition of Visual Technical Patterns in China’s Stock Market
This paper investigates the informativeness of some popular visual technical patterns among technical practitioners in China’s stock market,which are quantitatively defined and automatically recognized using a nonparametric kernel regression method. By comparing the unconditional empirical distribution of daily returns to the distribution of the returns that follw these technical patterns,we find that most of the patterns can provide incremental information that may be used to forecast further prices changes. However,only two of eight technical patterns can not generate significant excess trading profits after risk adjustment.
technical patterns kernel regression pattern recognition excess return
Zhigang Wang Yong Zeng Ping Li
School of Management and Economics,University of Electronic Science and Technology of China,Chengdu,610054,China
国际会议
香港
英文
296-300
2010-08-17（万方平台首次上网日期，不代表论文的发表时间）