Original paper
Abstract
We find large variations in returns from momentum strategies. Momentum strategies did not earn significant returns during the period of 1993-2004 which was due to their poor performance over the period from 2001-2004. We find that the previously documented large firm momentum effect is sensitive to the momentum strategy examined, and is in our sample driven by the abnormal returns of large Nasdaq stocks. We also evaluate momentum strategies that do not adhere to the end of month portfolio formation universally used in the academic literature. To this end we form portfolios one week prior to the end of month and call them 'front-running' momentum portfolios. Consistent with institutional momentum trading affecting end of month returns and volatility, we find that 'front-running' a momentum strategy generates similar, but less volatile returns than the month-end strategy. Pertinently, the returns of a 'front-running' strategy are consistently less volatile than that of an equivalent month-end strategy.
Keywords: Momentum portfolio, front-running, abnormal return, portfolio selection
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.