Original paper
Abstract
While there is a large literature documenting the profitability of momentum strategies, their implementation is afflicted with many difficulties. Most importantly, high turnover and costs to hold short positions, especially in small-cap stocks, result in high transaction costs. We restrict our investment universe to large-capitalized stocks included in the S&P 100 index. Moreover, we implement simple investment strategies that invest long in single stocks and short in the stock index. Such simple and cost-saving momentum strategies generate economically high and statistically significant abnormal returns. These results are robust to various risk-adjustments including the CAPM, the Fama French (1993) three-factor model, and a conditional version of the Fama and French (1993) three-factor model.
Keywords:Â Momentum strategies, Large-cap stocks, Stock market predictability
Trading rules
- Investment scope: Companies publicly traded in the UK (can be adjusted for different markets)
- Omit stocks in the bottom 25% by market capitalization due to liquidity concerns
- Order stocks by their performance over the last year
- Invest in the top 10 best-performing stocks and bet against the 10 lowest-performing ones
- Allocate funds evenly across portfolio assets
- Adjust the portfolio once a year
Python code
Backtrader
import backtrader as bt
import datetime
class MomentumStrategy(bt.Strategy):
params = (
('momentum_period', 12),
('num_stocks_long', 10),
('num_stocks_short', 10),
('rebalance_months', [1]),
)
def __init__(self):
self.rank = dict()
self.rebalance = False
def next(self):
if self.datetime.date().month not in self.params.rebalance_months:
return
if self.rebalance:
self.rebalance_portfolio()
else:
self.rank_stocks()
self.rebalance = True
def rank_stocks(self):
for d in self.getdatanames():
data = self.getdatabyname(d)
self.rank[data] = data.close[-1] / data.close[-self.params.momentum_period * 21] - 1
self.rank = dict(sorted(self.rank.items(), key=lambda x: x[1], reverse=True))
def rebalance_portfolio(self):
long_stocks = list(self.rank.keys())[:self.params.num_stocks_long]
short_stocks = list(self.rank.keys())[-self.params.num_stocks_short:]
for d in self.getdatanames():
data = self.getdatabyname(d)
if data in long_stocks:
self.order_target_percent(data, target=1.0 / self.params.num_stocks_long)
elif data in short_stocks:
self.order_target_percent(data, target=-1.0 / self.params.num_stocks_short)
else:
self.order_target_percent(data, target=0)
cerebro = bt.Cerebro()
cerebro.addstrategy(MomentumStrategy)
initial_cash = 10000
for data in your_data_feed_list: # Replace with your list of data feeds for UK listed companies
if data.market_cap_rank() > data.market_cap_rank().quantile(0.25): # Exclude lowest 25% market cap stocks
cerebro.adddata(data)
cerebro.broker.setcash(initial_cash)
cerebro.run()