Original paper
Abstract
Trend extrapolation in financial markets has been well documented, however it is contentious as to which trends will be extrapolated or mean reverted. We examine whether investors are more likely to extrapolate trends that they perceive to be salient by examining an investment strategy that considers both the magnitude and the strength of the trend. Consistent with behavioral models of momentum, our investment strategy based on trend salience significantly outperforms traditional momentum strategies and is not explained by the Carhart [1997] four-factor model. The relative performance of the trend salience signal is robust across different investment horizons and size-sorted portfolios, although is time-varying; the strategy does not outperform momentum in "down" markets where volatility is high and salient trends are more difficult to identify.
Keywords:Â Momentum, trend salience, extrapolation, market states
Trading rules
- Investment universe: All stocks on NYSE, AMEX, and NASDAQ
- Calculate acceleration ratio: 6-month momentum - (6-month momentum six months prior)
- Organize stocks into ten tiers, maintaining an equal weighting within each tier.
- Establish a long position in stocks with rising acceleration and a short position in those with declining acceleration.
- Acceleration effect materializes after 6 months, strongest returns at 12 months
- Ideal approach: Delay investments by 6 months post portfolio setup, then maintain positions for another 6 months.
- Combine acceleration effect with momentum effect:
- Compute acceleration as: (momentum T-7 to T-12) - (momentum T-13 to T-18)
- Sort stocks into deciles based on acceleration
- At end of month T, go long on strongest momentum stocks (T-1 to T-6) from top acceleration portfolio
- Short weakest momentum stocks (T-1 to T-6) from bottom acceleration portfolio
- 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.