Original paper
Abstract
In this paper, we conduct a comprehensive investigation of the Halloween effect evolution in the US stock market over its entire history. We employ various statistical techniques (average analysis, Student’s t-test, ANOVA, and the Mann-Whitney test) and the trading simulation approach to analyse the evolution of the Halloween effect. The results suggest that in the US stock market the Halloween effect became more persistent since the middle of the 20th century. Despite the decline in its prevalence since that time, nowadays it is still present in the US stock market and provides opportunities to build a trading strategy which can beat the market. These results are well in line with other developed stock markets. Therefore, in the main, our results are inconsistent with the Efficient Market Hypothesis.
Keywords:Â Calendar Anomalies, Halloween Effect, Stock Market, Efficient Market Hypothesis
Trading rules
- Allocate funds to worldwide stock markets between November and April.
- Hold cash or diversify into other assets from May through October.
- If desired, invest in northern hemisphere stocks during winter and southern hemisphere stocks during summer.
- As another strategy, take a long position in cyclical businesses during winter, and bet against defensive companies, reversing the position when summer approaches.
Python code
Backtrader
import backtrader as bt
class SeasonalStrategy(bt.Strategy):
params = (
('northern_hemisphere', None),
('southern_hemisphere', None),
('cyclical_companies', None),
('defensive_stocks', None),
)
def __init__(self):
self.month = self.datas[0].datetime.date(0).month
def next(self):
self.month = self.datas[0].datetime.date(0).month
if 11 <= self.month <= 4: # November to April
if self.params.northern_hemisphere and self.params.southern_hemisphere:
self.order_target_percent(self.params.northern_hemisphere, target=0.5)
self.order_target_percent(self.params.southern_hemisphere, target=0.0)
elif self.params.cyclical_companies and self.params.defensive_stocks:
self.order_target_percent(self.params.cyclical_companies, target=1.0)
self.order_target_percent(self.params.defensive_stocks, target=-1.0)
else:
self.order_target_percent(self.data, target=1.0)
else: # May to October
if self.params.northern_hemisphere and self.params.southern_hemisphere:
self.order_target_percent(self.params.northern_hemisphere, target=0.0)
self.order_target_percent(self.params.southern_hemisphere, target=0.5)
elif self.params.cyclical_companies and self.params.defensive_stocks:
self.order_target_percent(self.params.cyclical_companies, target=-1.0)
self.order_target_percent(self.params.defensive_stocks, target=1.0)
else:
self.order_target_percent(self.data, target=0.0)