Original paper
Abstract
This paper studies the performance of international stock strategies based on historical returns. Stocks that outperform the local market in a particular month continue to outperform the local market in future years in that same calendar month. This effect lasts for 10 years and the same pattern appears in Canada, Japan, and twelve European countries. This return pattern is independent of country, currency effects, and market capitalization. These strategies are not highly correlated across countries; this indicates they do not reflect pervasive international risk. Instead this common seasonal structure in international stocks suggests countries share similar segmented return mechanisms.
Trading rules
- Choose the top 30% of companies by market capitalization from NYSE and AMEX.
- Organize firms into 10 equal-sized portfolios every month, determined by their performance from the previous year.
- Take long positions in shares from the highest performance decile and short positions in those from the lowest decile.
- Ensure an equal-weighting across the portfolio.
- Adjust the portfolio composition on a monthly basis.
Python code
Backtrader
import backtrader as bt
import numpy as np
class SeasonalityStrategy(bt.Strategy):
params = (
('top_percent', 0.3),
('num_portfolios', 10),
('lookback_period', 12),
('rebalance_days', 21)
)
def __init__(self):
self.add_timer(bt.timer.SESSION_END, monthdays=[1], monthcarry=True)
self.inds = {}
for d in self.datas:
self.inds[d] = {}
self.inds[d]['market_cap'] = d.close * d.volume
self.inds[d]['past_performance'] = bt.ind.PctChange(d.close, period=self.p.lookback_period)
def notify_timer(self, timer, when, *args, **kwargs):
if self._last_month != self.data.datetime.date(0).month:
self._last_month = self.data.datetime.date(0).month
self.rebalance_portfolio()
def rebalance_portfolio(self):
market_caps = [self.inds[d]['market_cap'][0] for d in self.datas]
past_performances = [self.inds[d]['past_performance'][0] for d in self.datas]
sorted_indices = np.argsort(market_caps)[::-1]
top_percentile = int(len(sorted_indices) * self.p.top_percent)
top_firms = sorted_indices[:top_percentile]
selected_stocks = [self.datas[i] for i in top_firms]
selected_performances = [past_performances[i] for i in top_firms]
sorted_stocks = np.argsort(selected_performances)
num_in_decile = int(len(sorted_stocks) / self.p.num_portfolios)
long_decile = sorted_stocks[-num_in_decile:]
short_decile = sorted_stocks[:num_in_decile]
long_stocks = [selected_stocks[i] for i in long_decile]
short_stocks = [selected_stocks[i] for i in short_decile]
for d in self.datas:
current_position = self.getposition(d).size
if d in long_stocks and current_position <= 0:
self.order_target_percent(d, target=1.0 / len(long_stocks))
elif d in short_stocks and current_position >= 0:
self.order_target_percent(d, target=-1.0 / len(short_stocks))
elif current_position != 0:
self.close(d)
cerebro = bt.Cerebro()
# Add data feeds, broker, etc.
cerebro.addstrategy(SeasonalityStrategy)
results = cerebro.run()This is a basic implementation of the trading rules in Backtrader. You’ll need to add your own data feeds, broker settings, and any other desired configurations to complete the implementation.