Original paper
Abstract
Existing research has documented cross-sectional seasonality of stock returns—the periodic outperformance of certain stocks during the same calendar months or weekdays. We hypothesize that assets’ different sensitivities to investor mood explain these effects and imply other seasonalities. Consistent with our hypotheses, relative performance across individual stocks or stock portfolios during past high or low mood months and weekdays tends to recur in periods with congruent mood and reverse in periods with noncongruent mood. Furthermore, assets with higher sensitivities to aggregate mood—higher mood betas—subsequently earn higher returns during ascending mood periods and lower returns during descending mood periods.
Keywords:Â Return seasonality, Investor mood, Mood beta, Market efficiency, Anomalies
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.