Original paper

SSRN-id3240609.pdf692.0KB

Abstract

We demonstrate that firm-specific momentum profits are predictable across a wide range of international equity markets when combining information given in a multitude of stock characteristics. This predictor is comparatively simple to compute and can yield significant positive out-of-sample long-short portfolio returns. Cross-country analyses reveal that both, ordinary and composite-enhanced momentum returns tend to be higher within countries that exhibit less trading frictions and markets that exhibit less information opaqueness. Simultaneously, we find composite-enhanced momentum returns to be higher in highly individualistic countries that simultaneously exhibit smaller degrees of power distance. Overall, we interpret reported findings as empirical support for overreaction-based explanations of (composite-enhanced) momentum.

Keywords: momentum profits, stock characteristics, market efficiency, international stock markets

Trading rules

Focus on the largest 20% of stocks on NYSE, NASDAQ, and AMEX.
Choose the top quintile of stocks based on their 12-month momentum performance.
Construct a portfolio with equal weighting from the selected stocks.
Adjust the portfolio composition every month.
Benchmark: an equally weighted portfolio of largest 20% stocks on NYSE, NASDAQ, and AMEX.

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.

Cross-Country Composite Momentum

Original paper

Abstract

Trading rules

Python code

Backtrader