MA trading rules, herding behaviors, and stock market overreaction

Yensen Ni, Yi-Ching Liao and Paoyu Huang

International Review of Economics & Finance, 2015, vol. 39, issue C, 253-265

Abstract: We determine whether investors profit from employing moving average trading rules that consider either “wide” or “in-depth” concerns. Our remarkable findings are as follows: First, investors benefit from purchasing the constituent stocks of SSE50 as dead crosses emerge. These stocks may be the result of the herding behaviors of individual investors who account for over 80% of investors in China's stock markets. Second, negative weekly returns increase in trading the constituent stocks of DJ30 and FTSE100 because returns increase considerably on golden-cross days as a result of stock price overreaction. These results remain robust by concerning investors' risk aversion, and even high risk aversion as investors suffer losses. In addition, our findings imply that stock market overreaction and herding behaviors are incorporated into technical analysis.

Keywords: Moving average; Herding behavior; Overreaction (search for similar items in EconPapers)
JEL-codes: G02 G11 G14 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1059056015000842
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:39:y:2015:i:c:p:253-265

DOI: 10.1016/j.iref.2015.04.009

Access Statistics for this article

International Review of Economics & Finance is currently edited by H. Beladi and C. Chen

More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().