# Original paper

**Abstract**

We show that the post earnings announcement drift (PEAD) is stronger for conglomerates than single-segment firms. Conglomerates, on average, are larger than single segment firms, so it is unlikely that limits-to-arbitrage drive the difference in PEAD. Rather, we hypothesize that market participants find it more costly and difficult to understand firm-specific earnings information regarding conglomerates as they have more complicated business models than single-segment firms. This in turn slows information processing about them. In support of our hypothesis, we find that, compared to single-segment firms with similar firm characteristics, conglomerates have relatively low institutional ownership and short interest, are covered by fewer analysts, these analysts have less industry expertise and make larger forecast errors. Finally, we find that an increase in organizational complexity leads to larger PEAD and document that more complicated conglomerates have even greater PEAD. Our results are robust to a long list of alternative explanations of PEAD as well as alternative measures of firm complexity.

**Keywords:**Â organizational complexity, post-earnings-announcement drift, conglomerates, complicated firms

# Trading rules

- Investment universe: All NYSE, AMEX, and NASDAQ stocks (excluding financial and utility firms and stocks below $5)
- Factor 1: EAR (Earnings Announcement Return), calculated as the abnormal return during a 3-day window around the announcement date, in excess of a similar risk portfolio
- Factor 2: SUE (Standardized Unexpected Earnings), calculated as the earnings surprise divided by the standard deviation of earnings surprises
- Sorting: Stocks sorted into quintiles based on EAR and SUE, using previous quarter data to prevent look-ahead bias
- Portfolio weighting: Equal weight for each stock within quintiles
- Long/short positions: Go long on top SUE and EAR quintile intersection, short on bottom SUE and EAR quintile intersection
- 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.