Original paper
Abstract
We use seasonality in stock trading activity associated with summer vacation as a source of exogenous variation to study the relationship between trading volume and expected return. Using data from 51 stock markets, we first confirm a widely held belief that stock turnover is significantly lower during the summer because market participants are on vacation. Interestingly, we find that mean stock return is also lower during the summer for countries with significant declines in trading activity. This relationship is not due to time-varying volatility. Moreover, both large and small investors trade less and the price of trading (bid-ask spread) is higher during the summer. These findings suggest that heterogeneous agent models are essential for a complete understanding of asset prices.
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, funnel investments into stocks from the northern hemisphere during colder months and those from the southern hemisphere in warmer months.
- 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)