Original paper

SSRN-id2721800.pdf534.5KB

Abstract

Among the various strategies studied in this paper, only momentum investing appears to earn persistently non-zero returns: from 1965 to 2014, the classical momentum strategy based on performance over the previous two to twelve months earned an average return of 1.57% per month (excluding microcap stocks and value-weighted returns). In the most recent ten-year time period, this return has been even larger – 2.27% – which is much larger than in the U.S. However, the profitability net of transaction costs appears weak because the strategy involves trading in disproportionately small stocks with high transaction costs; this has been observed for the loser portfolio in particular. A strategy that concentrates only on the winner portfolio and, thus, avoids potential problems associated with (short) selling the costly loser portfolio appears to earn strong and persistently abnormal profits, even after transaction costs.

Keywords: momentum, stock reversal, contrarian, transaction costs, predictability

Trading rules

Target the top quintile of stocks listed on NYSE, NASDAQ, and AMEX by size.
Select top 20% stocks exhibiting the strongest 12-month momentum.
Create an equally weighted portfolio of selected stocks.
Adjust the portfolio composition each 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.

Trading Strategies Based on Past Returns – Evidence from Germany

Original paper

Abstract

Trading rules

Python code

Backtrader