Original paper
Abstract
We show that asset prices behave very differently on days when important macroeconomic news is scheduled for announcement relative to other trading days. In addition to significantly higher average returns for risky assets on announcement days, return patterns are also much easier to reconcile with standard asset pricing theories, both cross-sectionally and across time. On such days, stock market beta is strongly related to average returns. This positive relation holds for individual stocks, for various test portfolios, and even for bonds and currencies, suggesting that beta is after all an important measure of systematic risk. Furthermore, a robust risk-return trade-off exists on announcement days. Expected variance is positively related to future aggregated quarterly announcement day returns, in contrast to market or aggregated non-announcement day returns where there is no evidence of predictability. We explore the implications of our findings in the context of various asset pricing models.
Trading rules
- Employ CFDs, ETFs, or futures for creating long positions in the equity market during key economic release days.
- Concentrate on days presenting CPI, PPI, jobs data, or FOMC outcomes.
- Stay in a cash position on non-announcement days.
Python code
Backtrader
import backtrader as bt
import datetime
class EconomicAnnouncementStrategy(bt.Strategy):
def __init__(self):
self.in_announcement = False
def next(self):
announcement_days = self.get_announcement_days()
if self.datetime.date(0) in announcement_days:
self.in_announcement = True
self.order_target_percent(target=1.0) # Long exposure to equity market
elif self.in_announcement:
self.in_announcement = False
self.order_target_percent(target=0.0) # Move to cash
def get_announcement_days(self):
# Replace this function with actual dates of CPI, PPI, employment data, and FOMC decisions
return [
datetime.date(2023, 4, 20),
datetime.date(2023, 5, 15),
datetime.date(2023, 6, 19),
# ...
]
if __name__ == '__main__':
cerebro = bt.Cerebro()
data = bt.feeds.YahooFinanceData(
dataname='SPY', fromdate=datetime.datetime(2023, 1, 1),
todate=datetime.datetime(2023, 12, 31))
cerebro.adddata(data)
cerebro.addstrategy(EconomicAnnouncementStrategy)
cerebro.run()