Original paper
Abstract
The article analyses the impact of trading costs on the profitability of momentum strategies in the UK and concludes that losers are more expensive to trade than winners. The observed asymmetry in the costs of trading winners and losers crucially relates to the high cost of selling loser stocks with small size and low trading volume. Since transaction costs severely impact net momentum profits, the paper defines a new low-cost relative-strength strategy by shortlisting from all winner and loser stocks those with the lowest total transaction costs. While the study severely questions the profitability of standard momentum strategies, it concludes that there is still room for momentum-based return enhancement, should asset managers decide to adopt low-cost relative-strength strategies.
Keywords:Â Momentum profits, Transaction costs, Low-cost strategy
Trading rules
- Focus on the argest 20% of stocks listed on NYSE, NASDAQ, and AMEX.
- Choose the top 20% of equities with the strongest 12-month momentum.
- Construct a portfolio where all chosen stocks have equal weight.
- Adjust the portfolio composition every month.
Python code
Backtrader
import backtrader as bt
import backtrader.feeds as btfeeds
import datetime
class Momentum(bt.Strategy):
params = (
('momentum_period', 252),
('rebalance_interval', 21),
('num_stocks', 0.2),
)
def __init__(self):
self.add_timer(
bt.timer.SESSION_END, monthdays=[1], monthcarry=True
) # Rebalance monthly
self.inds = {}
for d in self.datas:
self.inds[d] = bt.indicators.ROC(d.close, period=self.params.momentum_period)
def prenext(self):
self.next()
def notify_timer(self, timer, when, *args, **kwargs):
if self._getminperstatus() < 0:
return
stocks = list(self.datas)
stocks.sort(key=lambda d: self.inds[d][0], reverse=True)
selected_stocks = stocks[:int(len(stocks) * self.params.num_stocks)]
for d in self.datas:
target = self.broker.getvalue() * (1 / len(selected_stocks)) if d in selected_stocks else 0
self.order_target_value(d, target)
cerebro = bt.Cerebro()
# Add data for NYSE, NASDAQ, and AMEX stocks# Replace 'NYSE.csv', 'NASDAQ.csv', and 'AMEX.csv' with appropriate files
for market in ['NYSE.csv', 'NASDAQ.csv', 'AMEX.csv']:
data = btfeeds.GenericCSVData(
dataname=market,
fromdate=datetime.datetime(2000, 1, 1),
todate=datetime.datetime(2020, 12, 31),
dtformat=('%Y-%m-%d'),
openinterest=-1,
nullvalue=0.0,
plot=False,
)
cerebro.adddata(data)
cerebro.broker.setcash(1000000.0)
cerebro.broker.setcommission(commission=0.001) # 0.1% commission
cerebro.addstrategy(Momentum)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
results = cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.plot()Make sure to replace ‘NYSE.csv’, ‘NASDAQ.csv’, and ‘AMEX.csv’ with the appropriate files containing the stock data for the mentioned exchanges. This code uses the Backtrader framework to implement the trading strategy as described.