Original paper

SSRN-id2642185.pdf298.5KB

Abstract

We study the link between the profitability of momentum strategies and firm size, drawing on an extensive dataset covering 14 stock markets across the globe. International momentum profitability is markedly higher in medium-size than in big stocks. Momentum premia are considerably diminished by trading costs, when taking into account the actual portfolio turnover incurred when implementing this strategy. Thus, while small stocks exhibit momentum, exploiting this feature is hardly feasible. By contrast, momentum premia especially for medium-sized stocks still remain economically and statistically significant in most equity markets worldwide after adjusting for trading costs.

Keywords: Size; momentum; international equity markets; asset pricing anomalies; trading costs

Trading rules

Focus on the largest 20% of stocks on NYSE, NASDAQ, and AMEX
Select top 20% stocks with best 12-month momentum (performance)
Create an equally weighted portfolio of selected stocks
Rebalance portfolio monthly

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.

Size and Momentum Profitability in International Stock Markets

Original paper

Abstract

Trading rules

Python code

Backtrader