Equity · Global · CFDs, ETFs, futures · Daily · Global Free

Scheduled Economic Announcements Effect in Stocks

Academic paper

How Much Do Investors Care About Macroeconomic Risk? Evidence from Scheduled Economic Announcements

AuthorsPavel G. Savor; Mungo Ivor Wilson

Institute

DePaul University
?DePaul University - Kellstadt Graduate School of Business
?affiliation not provided to SSRN
University of Oxford
?University of Oxford - Said Business School

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=1312091

Strategy in a nutshell

The strategy invests in equities via CFDs, ETFs, or futures only on major economic announcement days (CPI, PPI, jobs data, FOMC decisions). On other days, positions are closed and capital stays in cash, targeting gains from heightened market activity while minimizing exposure to low-information periods.

Economic rationale

Research shows markets demand a risk premium on announcement days due to heightened uncertainty. As risk-averse investors require higher compensation, stock returns rise predictably around these events, making such days attractive for targeted equity exposure.

Backtest performance

Annualised return7.2%

Beta0.093

Sortino ratio-0.104

Win rate54%

Full Python code

1from AlgorithmImports import *
2from pandas.tseries.offsets import BDay
3#endregion
4class ScheduledAnnouncementsAnomaly(QCAlgorithm):
5def initialize(self):
6self.set_start_date(2000, 1, 1)
7self.set_cash(100_000)
8
9self.symbol: Symbol = self.add_equity("SPY", Resolution.MINUTE).symbol
10csv_string_file: str = self.download('data.quantpedia.com/backtesting_data/economic/economic_announcements.csv')
11dates: List[str] = csv_string_file.split('\r\n')
12self.announcement_dates_t_minus_one: List[datetime.date] = [(datetime.strptime(x, "%Y-%m-%d") - BDay(1)).date() for x in dates]
13def on_data(self, data: Slice) -> None:
14if self.time.hour == 15 and self.time.minute == 44:
15    if self.time.date() in self.announcement_dates_t_minus_one:
16        if not self.portfolio[self.symbol].is_long:
17            self.market_on_close_order(self.symbol, self.calculate_order_quantity(self.symbol, 1.))
18    else:
19        if self.portfolio[self.symbol].is_long:
20            self.market_on_close_order(self.symbol, -self.portfolio[self.symbol].quantity)