Original paper
Abstract
Previous studies have focused on which stocks are winners or losers but have paid little attention to the formation process of past returns. This paper develops a model showing that past returns and the formation process of past returns have a joint effect on future expected returns. The empirical evidence shows that the zero-investment portfolio, including stocks with specific patterns of historical prices, improves monthly momentum profit by 59%. Overall, the process of how one stock becomes a winner or loser can further distinguish the best and worst stocks in a group of winners or losers.
Keywords:Â Momentum; Historical Price; Price Movement
Trading rules
- Focus on: Stocks listed on NYSE, AMEX, and NASDAQ
- Compute acceleration metric: Momentum over 6 months minus momentum from the previous 6 months (from 6 months ago)
- Categorize stocks into ten groups with equal distribution in each group
- Adopt a long position in stocks with high acceleration, and a short position in those with low acceleration
- Acceleration effect materializes after 6 months, strongest returns at 12 months.
- Ideal approach: Initiate stock purchases 6 months post-portfolio creation and maintain for an additional 6 months
- To integrate the acceleration and momentum effects:
- Determine acceleration by assessing: (Momentum from month T-7 to T-12) subtracted from (momentum from month T-13 to T-18)
- Organize stocks into ten groups based on their acceleration rate
- At month's end (T), adopt a long position on the highest momentum stocks (from month T-1 to T-6) of the top acceleration group
- Similarly, adopt a short position on the lowest momentum stocks (from month T-1 to T-6) of the lowest acceleration group
- Hold long/short portfolio for six months.
Python code
Backtrader
import backtrader as bt
import pandas as pd
import numpy as np
class AccelerationMomentum(bt.Strategy):
def __init__(self):
self.stock_data = dict()
def prenext(self):
self.next()
def next(self):
# Every 6 months, reevaluate the portfolio
if len(self) % 126 == 0:
self.rebalance_portfolio()
def rebalance_portfolio(self):
stocks_accel = []
for d in self.datas:
# Calculate 6-month momentum
momentum_6m = d.close[0] / d.close[-126]
# Calculate 6-month momentum six months prior
momentum_6m_prior = d.close[-126] / d.close[-252]
# Calculate acceleration ratio
acceleration_ratio = momentum_6m - momentum_6m_prior
stocks_accel.append((d, acceleration_ratio))
# Sort stocks by acceleration ratio and divide into deciles
sorted_stocks = sorted(stocks_accel, key=lambda x: x[1], reverse=True)
deciles = np.array_split(sorted_stocks, 10)
# Get top and bottom deciles
top_decile = deciles[0]
bottom_decile = deciles[-1]
# Sort top decile by momentum (T-1 to T-6)
top_decile_momentum = sorted(top_decile, key=lambda x: x[0].close[0] / x[0].close[-6], reverse=True)
# Sort bottom decile by momentum (T-1 to T-6)
bottom_decile_momentum = sorted(bottom_decile, key=lambda x: x[0].close[0] / x[0].close[-6])
# Go long on top decile stocks
for stock in top_decile_momentum:
self.order_target_percent(stock[0], target=1.0 / len(top_decile_momentum))
# Go short on bottom decile stocks
for stock in bottom_decile_momentum:
self.order_target_percent(stock[0], target=-1.0 / len(bottom_decile_momentum))
if __name__ == '__main__':
cerebro = bt.Cerebro()
# Add all stocks in the investment universe
# (Replace 'stock_data' with actual stock data)
for stock in stock_data:
data = bt.feeds.PandasData(dataname=stock_data[stock])
cerebro.adddata(data)
cerebro.addstrategy(AccelerationMomentum)
cerebro.broker.setcash(100000.0)
cerebro.run()Note: This code is a starting point and should be adapted according to your specific data source and requirements. The ‘stock_data’ variable should be replaced with actual stock data from NYSE, AMEX, and NASDAQ. Additionally, ensure that your stock data has the required history for the calculations.