The Halloween Indicator, 'Sell in May and Go Away': Everywhere and All the Time

78 Pages Posted: 2 Oct 2012 Last revised: 25 Oct 2018

See all articles by Ben Jacobsen

Ben Jacobsen

Tilburg University - TIAS School for Business and Society; Massey University

Cherry Yi Zhang

Nottingham University Business School China; Massey University - School of Economics and Finance

Date Written: October 1, 2018

Abstract

To answer the sceptics, we use all historical data (62962 observations) on all stock market indices worldwide to verify the robustness of the so-called Halloween Indicator or Sell in May effect. The effect seems remarkably robust with returns on average 4% higher during November-April period than during May-October. A new test for the effect offers some additional insights. Worldwide excess returns during summer seem negative (around -1%) and often significantly so suggesting a flat or negative risk return relation. Only for Mauritius do we find a significantly positive risk return relation during May-October. Our dataset also allows for a new (upper bound) estimate for the equity premium of around 4%.

Keywords: seasonal anomalies, sell in May, Halloween indicator, long time series data

JEL Classification: G10, G14

Suggested Citation

Jacobsen, Ben and Zhang, Cherry Yi, The Halloween Indicator, 'Sell in May and Go Away': Everywhere and All the Time (October 1, 2018). Available at SSRN: https://ssrn.com/abstract=2154873 or http://dx.doi.org/10.2139/ssrn.2154873

Ben Jacobsen

Tilburg University - TIAS School for Business and Society ( email )

Warandelaan 2
TIAS Building
Tilburg, Noord Brabant 5037 AB
Netherlands

Massey University ( email )

Auckland
New Zealand

Cherry Yi Zhang (Contact Author)

Nottingham University Business School China ( email )

199 Taikang East Rd.
Ningbo, 315100
China

Massey University - School of Economics and Finance ( email )

New Zealand

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
7,177
Abstract Views
38,268
Rank
1,945
PlumX Metrics