The strategy involves sorting equities based on earnings surprises, buying stocks with the highest surprises and shorting those with the lowest, both announced on non-macro days. The portfolio is rebalanced monthly.
ASSET CLASS: stocks | REGION: United States | FREQUENCY:
Monthly | MARKET: equities | KEYWORD: Impact, Macro, News, PEAD, Strategy
I. STRATEGY IN A NUTSHELL
The strategy invests in U.S. equities from NYSE, AMEX, and NASDAQ, sorting firms into deciles based on earnings surprises (actual earnings minus analysts’ median forecast, divided by stock price). Each month, the investor goes long on stocks with the highest earnings surprises and shorts stocks with the lowest, focusing only on announcements made on non-macro days. The portfolio is equally weighted and rebalanced monthly to maintain exposure.
II. ECONOMIC RATIONALE
The strategy leverages investor attention dynamics. Sheng (2017) suggests that investors first focus on non-market activities and then allocate attention between macro and micro news. Macro-news days grab more attention, increasing trading volumes and market focus on earnings announcements. By concentrating on non-macro days, the strategy exploits the predictable patterns in trading behavior when attention is relatively lower, capturing the price movements driven by under- or over-reaction to earnings surprises.
III. SOURCE PAPER
Macro News, Micro News, and Stock Prices [Click to Open PDF]
Sheng, University of California, Irvine – Paul Merage Scho
<Abstract>
We study how the arrival of macro-news affects the stock market’s ability to incorporate the information in firm-level earnings announcements. Existing theories suggest that macro and firm-level earnings news are attention substitutes; macro-news announcements crowd out firm-level attention, causing less efficient processing of firm-level earnings announcements. We find the opposite: the sensitivity of announcement returns to earnings news is 17% stronger, and post-earnings announcement drift 71% weaker, on macro-news days. This suggests a complementary relationship between macro and micro news that is consistent with either investor attention or information transmission channels.
IV. BACKTEST PERFORMANCE
| Annualised Return | 12.28% |
| Volatility | N/A |
| Beta | -0.014 |
| Sharpe Ratio | N/A |
| Sortino Ratio | -0.235 |
| Maximum Drawdown | N/A |
| Win Rate | 51% |
V. FULL PYTHON CODE
from AlgorithmImports import *
import numpy as np
from collections import deque
from typing import List, Dict, Deque
from numpy import isnan
#endregion
class ImpactofMacroNewsonPEADStrategy(QCAlgorithm):
def Initialize(self) -> None:
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.exchange_codes: List[str] = ['NYS', 'NAS', 'ASE']
self.period: int = 13
self.quantile: int = 5
self.leverage: int = 5
self.threshold: int = 3
self.min_share_price: int = 5
# EPS quarterly data.
self.eps_data: Dict[Symbol, Deque[List[float]]] = {}
self.long: List[Symbol] = []
self.short: List[Symbol] = []
symbol: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
# Import macro dates.
csv_string_file: str = self.Download('data.quantpedia.com/backtesting_data/economic/economic_announcements.csv')
dates: List[str] = csv_string_file.split('\r\n')
self.macro_dates: List[datetime.date] = [datetime.strptime(x, "%Y-%m-%d").date() for x in dates]
self.fundamental_count: int = 3000
self.fundamental_sorting_key = lambda x: x.MarketCap
self.selection_flag: int = False
self.settings.daily_precise_end_time = False
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Schedule.On(self.DateRules.MonthStart(symbol), self.TimeRules.AfterMarketOpen(symbol), self.Selection)
def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
if not self.selection_flag:
return Universe.Unchanged
selected: List[Fundamental] = [
x for x in fundamental
if x.HasFundamentalData
and x.Market == 'usa'
and x.Price > self.min_share_price
and x.SecurityReference.ExchangeId in self.exchange_codes
and not isnan(x.EarningReports.BasicEPS.ThreeMonths) and (x.EarningReports.BasicEPS.ThreeMonths != 0)
]
if len(selected) > self.fundamental_count:
selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
# Stocks with last month's earnings.
last_month_date: datetime.date = self.Time - timedelta(self.Time.day)
filtered_fundamental: List[Fundamental] = [x for x in selected if (x.EarningReports.FileDate.ThreeMonths.year == last_month_date.year and x.EarningReports.FileDate.ThreeMonths.month == last_month_date.month)]
# earnings surprises data for stocks
earnings_surprises: Dict[Symbol, List[float, datetime.date]] = {}
for stock in filtered_fundamental:
symbol: Symbol = stock.Symbol
# Store eps data.
if symbol not in self.eps_data:
self.eps_data[symbol] = deque(maxlen = self.period)
self.eps_data[symbol].append([stock.EarningReports.FileDate.ThreeMonths.date(), stock.EarningReports.BasicEPS.ThreeMonths])
if len(self.eps_data[symbol]) == self.eps_data[symbol].maxlen:
year_range: range = range(self.Time.year - 3, self.Time.year)
month_range: List[datetime.date] = [last_month_date.month - 1, last_month_date.month, last_month_date.month + 1]
# Earnings 3 years back.
seasonal_eps_data: List[List[datetime.date, float]] = [
x for x in self.eps_data[symbol]
if x[0].month in month_range
and x[0].year in year_range]
if len(seasonal_eps_data) != self.threshold:
continue
recent_eps_data: List[datetime.date, float] = self.eps_data[symbol][-1]
# Make sure we have a consecutive seasonal data. Same months with one year difference.
year_diff: np.ndarray = np.diff([x[0].year for x in seasonal_eps_data])
if all(x == 1 for x in year_diff):
seasonal_eps: List[float] = [x[1] for x in seasonal_eps_data]
diff_values: np.ndarray = np.diff(seasonal_eps)
drift: float = np.average(diff_values)
# earnings surprise calculation
last_earnings: float = seasonal_eps[-1]
expected_earnings: float = last_earnings + drift
actual_earnings: float = recent_eps_data[1]
# Store sue value with earnigns date
earnings_surprise: float = actual_earnings - expected_earnings
earnings_surprises[symbol] = [earnings_surprise, stock.EarningReports.FileDate.ThreeMonths.date()]
# wait until earnings suprises are ready
if len(earnings_surprises) < self.quantile:
return Universe.Unchanged
if self.Time.date() > self.macro_dates[-1]:
return Universe.Unchanged
# sort by earnings suprises.
quantile: int = int(len(earnings_surprises) / self.quantile)
sorted_by_earnings_surprise: List[Symbol] = [x[0] for x in sorted(earnings_surprises.items(), key=lambda item: item[1][0])]
# select top quintile and bottom quintile based on earnings suprise sort
top_quintile: List[Symbol] = sorted_by_earnings_surprise[-quantile:]
bottom_quintile: List[Symbol] = sorted_by_earnings_surprise[:quantile]
# long stocks, which are in top quintile by earnings suprise sort and have non-macro date
self.long = [x for x in top_quintile if earnings_surprises[x][1] not in self.macro_dates]
# short stocks, which are in bottom quintile by earnings suprise sort and have non-macro date
self.short = [x for x in bottom_quintile if earnings_surprises[x][1] not in self.macro_dates]
return self.long + self.short
def OnData(self, data: Slice) -> None:
if not self.selection_flag:
return
self.selection_flag = False
# Trade execution.
targets: List[PortfolioTarget] = []
for i, portfolio in enumerate([self.long, self.short]):
for symbol in portfolio:
if symbol in data and data[symbol]:
targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
self.SetHoldings(targets, True)
self.long.clear()
self.short.clear()
def Selection(self) -> None:
self.selection_flag = True
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters: OrderFeeParameters) -> OrderFee:
fee: float = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))VI. Backtest Performance
