Original paper
Abstract
We hypothesize that post-earnings-announcement drift (PEAD) is caused by underreaction of long-term investors since they do not pay much attention to short-term events. Consistent with the hypothesis, empirical observations show that stocks mostly held by long-term investors exhibit strong PEAD, while stocks mostly held by short-term investors does not. The results are still robust even after transaction costs, investor recognition, temporal inattention, and reversal in earnings surprises are controlled for.
Keywords: Post-Earnings-Announcement Drift, Investment Horizon, Portfolio Turnover Level
Trading rules
- Target stocks: Consider all stocks from NYSE, AMEX, and NASDAQ, but omit financial and utility companies as well as those with a share price below $5.
- Indicator 1: EAR (Earnings Announcement Return) – Determine the unusual return over a span of three days centered on the announcement date, surpassing a similar risk portfolio's return.
- Indicator 2: SUE (Standardized Unexpected Earnings) – Identify by dividing the earnings surprise by the fluctuation in past earnings surprises.
- Sorting: Based on prior quarter's EAR and SUE, categorize stocks into five groups to avoid forward-looking bias.
- Portfolio weighting: Equal weight for each stock within quintiles
- Trading strategy: Initiate a long position on the top intersection of SUE and EAR groups, and a short position on the lowest intersection of these two groups.
- Holding period: 1 quarter (60 working days) from the second day after the earnings announcement
- Rebalancing: Quarterly
Python code
Backtrader
import backtrader as bt
class PEADStrategy(bt.Strategy):
def __init__(self):
self.ear = {} # Earnings Announcement Return
self.sue = {} # Standardized Unexpected Earnings
def next(self):
# Check if it's the second day after the earnings announcement
if not self.is_second_day_after_announcement():
return
# Calculate factors EAR and SUE for each stock
self.calculate_factors()
# Sort stocks into quintiles based on EAR and SUE
long_stocks, short_stocks = self.sort_quintiles()
# Calculate equal weight for each stock within quintiles
long_weight = 1.0 / len(long_stocks)
short_weight = -1.0 / len(short_stocks)
# Enter long and short positions
for stock in long_stocks:
self.order_target_percent(stock, target=long_weight)
for stock in short_stocks:
self.order_target_percent(stock, target=short_weight)
# Hold positions for 1 quarter (60 working days)
self.hold_positions_for_n_days(60)
def is_second_day_after_announcement(self):
# Implement logic to determine if it's the second day after the earnings announcement
pass
def calculate_factors(self):
# Implement logic to calculate EAR and SUE for each stock in the investment universe
pass
def sort_quintiles(self):
# Implement logic to sort stocks into quintiles based on EAR and SUE
pass
def hold_positions_for_n_days(self, n):
# Implement logic to hold positions for n days
pass
if __name__ == '__main__':
cerebro = bt.Cerebro()
# Add data feeds for NYSE, AMEX, and NASDAQ stocks (excluding financial and utility firms and stocks below $5)
cerebro.addstrategy(PEADStrategy)
cerebro.run()Please note that this is just a skeleton code for the PEAD strategy. You need to implement the logic for each function based on your data source and the desired calculations.