Original paper
Abstract
We find strong evidence linking the momentum pattern in equity returns with a prominent measure of macroeconomic conditions, specifically the funding environment. We show that the size and consistency of the momentum premium varies systematically across funding states. Furthermore, we find evidence that the relationship between momentum returns and firm characteristics (documented in previous research) is conditional on the funding environment. After controlling for the funding state, we find that the importance of market states and return dispersion disappears. Additionally, funding conditions appear to contain incremental information about the momentum premium even after adjusting for the influence of market states and return dispersion. Overall our results are consistent with the conjecture that transitions in the funding environment encourage investors to revise their portfolio allocations; this reallocation produces inter-temporal variation in the momentum return pattern.
Keywords:Â Momentum, Funding Conditions, Asset Prices
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.