Original paper

SSRN-id2558783.pdf1211.4KB

Abstract

In this paper, I examine the sources of momentum returns and uncover a list of intriguing features. I find that when the momentum returns are decomposed the contributions of the explained and the unexplained risk factors depend on the level of analysis, the risk factors used, and the lag structure of the risk factors. Further, I find that at the individual stock level, the total contribution of the lagged macroeconomic risk factors is 59 percent per month but that the total contribution of the contemporaneous macroeconomic risk factors at the portfolio level is only 9 percent per month. These new findings add important insights to the existing momentum theories.

Keywords: Decomposition, explained risk factors, unexplained risk factors, portfolio level, individual stock level

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.

Sources of Momentum Returns: A Decomposition of the Explained and the Unexplained Risk Factors

Original paper

Abstract

Trading rules

Python code

Backtrader