Technical analysis: An asset allocation perspective on the use of moving averages

• 成果类型: Article
• 署名作者: Zhu, Yingzi; Zhou, Guofu
• 署名单位: Washington University (WUSTL); Tsinghua University; Tsinghua University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2008.07.002
• 发表日期: 2009
• 页码: 519-544
• 关键词: technical analysis; Trading rules; asset allocation; predictability; learning

摘要

In this paper, we analyze the usefulness of technical analysis, specifically the widely employed moving average trading rule from an asset allocation perspective. We show that, when stock returns are predictable, technical analysis adds value to commonly used allocation rules that invest fixed proportions of wealth in stocks. When uncertainty exists about predictability, which is likely in practice, the fixed allocation rules combined with technical analysis can outperform the prior-dependent optimal learning rule when the prior is not too informative. Moreover, the technical trading rules are robust to model specification, and they tend to substantially outperform the model-based optimal trading strategies when the model governing the stock price is uncertain. (C) 2009 Elsevier B.V. All rights reserved.