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Equity · Global · ETFs, funds · Monthly · Global Free

Why Do US Stocks Outperform EM and EAFE Regions?

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Academic paper

Why Do US Stocks Outperform EM and EAFE Regions?

AuthorsCyril Dujava; Radovan Vojtko

Institute
  • ?Quantpedia
  • ?Quantpedia.com

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=4694624

Strategy in a nutshell

This strategy applies a simple trend-following rule on the spread between U.S. equities (SPY) and emerging markets (EEM). If the spread shows positive performance over the past 12 months, go long SPY and short EEM; otherwise, reverse the position. Positions are rebalanced yearly.

Economic rationale

The strategy leverages long-term outperformance of U.S. equities versus global markets, combined with momentum effects and U.S. dollar trends that influence commodities and emerging market stocks, reinforcing spread-based trading opportunities.

Backtest performance

Annualised return7.14%
Volatility19.5%
Beta0.132
Sharpe ratio0.37
Sortino ratio0.308
Maximum drawdown-62.28%
Win rate67%

Full Python code

from AlgorithmImports import *
import data_tools
from pandas.core.frame import DataFrame
# endregion

class WhyDoUSStocksOutperformEMandEAFERegions(QCAlgorithm):

def Initialize(self) -> None:
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)

self.period: int = 365
self.leverage: int = 3

self.spread_assets: List[Symbol] = [
    self.AddEquity('SPY', Resolution.Daily).Symbol,
    self.AddEquity('EEM', Resolution.Daily).Symbol
]

for symbol in self.spread_assets:
    self.Securities[symbol].SetLeverage(self.leverage)

self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.current_month: int = -1

def OnData(self, data: Slice) -> None:
# monthly rebalance
if self.Time.month == self.current_month:
    return
self.current_month = self.Time.month

trade_direction: int = 0

returns_df: DataFrame = self.History(self.spread_assets, timedelta(days=self.period), Resolution.Daily)['close'].unstack(level=0).pct_change().iloc[1:].dropna(axis=1)
if returns_df.shape[1] == 2:
    spread: pd.Series = returns_df[self.spread_assets[0]] - returns_df[self.spread_assets[1]]
    spread_equity: float = (1 + spread).cumprod()
    trade_direction: int = 1 if spread_equity[-1] > spread_equity[0] else -1

# order execution
portfolio: List[PortfolioTarget] = [PortfolioTarget(symbol, ((-1)**i) * trade_direction) for i, symbol in enumerate(self.spread_assets) if symbol in data and data[symbol]]
self.SetHoldings(portfolio, True)