Original paper
Abstract
As a visual mode of analysis is more intuitive to human cognition than algebraic numbers, we propose that the visual pattern of historical prices is a salient signal that attracts attention; thereby inducing overreaction. We construct a long-short portfolio, including the stocks that are more likely to grab attention, and create an illusion through their discernible visual patterns of historical prices. The newly-developed portfolio commands an annual risk-adjusted return of 23.1% and dominates momentum investing. The outperformance holds under various specifications and asset pricing models. We provide support that momentum is induced by visually psychological biases.
Keywords:Â Momentum; Technical Analysis; Limited Attention; Visual; Illusion
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.