Original paper
Abstract
We report strong evidence that changes of momentum, i.e. "acceleration", defined as the first difference of successive returns, provide better performance and higher explanatory power than momentum. The corresponding Γ-factor explains the momentum-sorted portfolios entirely but not the reverse. Thus, momentum can be considered an imperfect proxy for acceleration, and its success can be attributed to its correlation to the predominant Γ-factor. Γ-strategies based on the "acceleration" effect are on average profitable and beat momentum-based strategies in two out of three cases, for a large panel of parameterizations. The "acceleration" effect and the Γ-factor profit from transient non-sustainable accelerating (upward or downward) log-prices associated with positive feedback mechanisms.
Keywords:Â Asset pricing, momentum, positive feedbacks, acceleration, investment strategies
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.