Backtest Period

Markets Traded

Options

Maximum Drawdown

Period of Rebalancing

Daily

Return (Annual)

Sharpe Ratio

Standard Deviation (Annual)

# Original paper

SSRN-id1512046.pdf165.5KB

# Trading rules

- Investment universe: NYSE, Amex, and Nasdaq firms with liquid options
- Calculate volatility spread: End of the day before earnings announcement
- Volatility spread measure: Weighted difference between implied volatility of matched put and call options, considering average open interest
- Eliminate options: Remove options with non-positive open interest and missing trading volume
- Monthly backtest: Sort stocks into quintiles based on put-call volatility spread
- Real trading consideration: Compare stockâ€™s volatility spread to last monthâ€™s reporting stocks to determine quintile placement
- Option liquidity: Divide option pairs on the day before earnings announcement into three groups based on average bid-ask spread
- Trading strategy: Go long on stocks with highest implied volatility spreads (measured by low bid-ask spread options) and short on stocks with lowest implied volatility spreads on the pre-announcement day
- Holding period: 2 days (announcement day and 1 day after)
- Portfolio: Equally weighted and daily rebalanced, utilized at 50% for manageable volatility

# Python code

## Backtrader

```
import backtrader as bt import pandas as pd
class EarningsAnnouncementStrategy(bt.Strategy):
def __init__(self):
self.earnings_announcements = self.get_earnings_announcements()
def get_earnings_announcements(self):
# Load earnings announcement data here, assuming DataFrame format
return pd.DataFrame()
def get_volatility_spread(self, stock):
# Calculate and return the weighted difference between implied volatility of matched put and call options
pass
def prenext(self):
self.next()
def next(self):
date = self.data.datetime.date(0)
pre_announcement_day = self.earnings_announcements[self.earnings_announcements['date'] == date]
if not pre_announcement_day.empty:
stocks = pre_announcement_day['symbol'].tolist()
volatility_spreads = [self.get_volatility_spread(stock) for stock in stocks]
quintiles = pd.qcut(volatility_spreads, 5, labels=False)
long_stocks = [stocks[i] for i in range(len(stocks)) if quintiles[i] == 4]
short_stocks = [stocks[i] for i in range(len(stocks)) if quintiles[i] == 0]
# Rebalance portfolio
self.rebalance_portfolio(long_stocks, short_stocks)
def rebalance_portfolio(self, long_stocks, short_stocks):
# Get target position size for each stock
position_size = 1.0 / (len(long_stocks) + len(short_stocks))
# Close positions not in the new target stocks
for stock in self.getdatanames():
if stock not in long_stocks + short_stocks:
self.order_target_percent(stock, target=0.0)
# Open or adjust positions for target stocks
for stock in long_stocks:
self.order_target_percent(stock, target=position_size)
for stock in short_stocks:
self.order_target_percent(stock, target=-position_size)
if __name__ == '__main__':
cerebro = bt.Cerebro()
cerebro.addstrategy(EarningsAnnouncementStrategy)
# Add data feeds for stocks in the investment universe
# ...
cerebro.broker.setcash(100000)
cerebro.run()
print(f"Final Portfolio Value: {cerebro.broker.getvalue():.2f}")
```