Global Low-CAPE Value Strategy with Annual Rebalance
Log in to collectAcademic paper
Strategy in a nutshell
The investment universe consists of 32 countries with easily accessible equity markets (via ETFs, for example). At the end of every year, the investor calculates Shiller’s “CAPE” Cyclically Adjusted PE) ratio, for each country in his investment universe. CAPE is the ratio of the real price of the equity market (adjusted for inflation) to the 10-year average of the country’s equity index (again adjusted for inflation). The whole methodology is explained well on Shiller’s home page (http://www.econ.yale.edu/~shiller/data.htm) or http://turnkeyanalyst.com/2011/10/the-shiller-pe-ratio/). The investor then invests in the cheapest 33% of countries from his sample if those countries have a CAPE below 15. The portfolio is equally weighted (the investor holds 0% cash instead of countries with a CAPE higher than 15) and rebalanced yearly.
Economic rationale
The anomaly has its source in investor psychology. Academic research postulates that investors overreact to news and events; “winners”, i.e. favourite countries, tend to be overvalued while “losers”, i.e. neglected countries, are undervalued. The contrarian investor can, therefore, exploit this generic investor mentality to capitalize on the inefficiency of the market to reap financial gains when stock prices revert to their intrinsic values.
Backtest performance
Full Python code
from AlgoLib import *
#endregion
class ValueFactorCAPEEffectwithinCountries(XXX):
def Initialize(self):
self.SetStartDate(2008, 1, 1)
self.SetCash(100000)
self.symbols = {
"Australia" : "EWA", # iShares MSCI Australia Index ETF
"Brazil" : "EWZ", # iShares MSCI Brazil Index ETF
"Canada" : "EWC", # iShares MSCI Canada Index ETF
"Switzerland" : "EWL", # iShares MSCI Switzerland Index ETF
"China" : "FXI", # iShares China Large-Cap ETF
"France" : "EWQ", # iShares MSCI France Index ETF
"Germany" : "EWG", # iShares MSCI Germany ETF
"Hong Kong" : "EWH", # iShares MSCI Hong Kong Index ETF
"Italy" : "EWI", # iShares MSCI Italy Index ETF
"Japan" : "EWJ", # iShares MSCI Japan Index ETF
"Korea" : "EWY", # iShares MSCI South Korea ETF
"Mexico" : "EWW", # iShares MSCI Mexico Inv. Mt. Idx
"Netherlands" : "EWN", # iShares MSCI Netherlands Index ETF
"South Africa" : "EZA", # iShares MSCI South Africe Index ETF
"Singapore" : "EWS", # iShares MSCI Singapore Index ETF
"Spain" : "EWP", # iShares MSCI Spain Index ETF
"Sweden" : "EWD", # iShares MSCI Sweden Index ETF
"Taiwan" : "EWT", # iShares MSCI Taiwan Index ETF
"UK" : "EWU", # iShares MSCI United Kingdom Index ETF
"USA" : "SPY", # SPDR S&P 500 ETF
"Russia" : "ERUS", # iShares MSCI Russia ETF
"Israel" : "EIS", # iShares MSCI Israel ETF
"India" : "INDA", # iShares MSCI India ETF
"Poland" : "EPOL", # iShares MSCI Poland ETF
"Turkey" : "TUR" # iShares MSCI Turkey ETF
}
self.quantile:int = 3
self.max_missing_days:int = 31
self.leverage:int = 2
for country, etf_symbol in self.symbols.items():
data = self.AddEquity(etf_symbol, Resolution.Daily)
data.SetLeverage(self.leverage)
data.SetFeeModel(CustomFeeModel())
# CAPE data import.
self.cape_data = self.AddData(CAPE, 'CAPE', Resolution.Daily).Symbol
self.recent_month:int = -1
def OnData(self, data:Slice) -> None:
if self.Time.month == self.recent_month:
return
self.recent_month = self.Time.month
if self.recent_month != 12:
return
price = {}
for country, etf_symbol in self.symbols.items():
if etf_symbol in data and data[etf_symbol]:
# cape data is still comming in
if self.Securities[self.cape_data].GetLastData() and (self.Time.date() - self.Securities[self.cape_data].GetLastData().Time.date()).days <= self.max_missing_days:
country_cape = self.Securities['CAPE'].GetLastData().GetProperty(country)
if country_cape < 15. and country_cape != 0.:
price[etf_symbol] = data[etf_symbol].Value
long = []
# Cape and price sorting.
if len(price) >= self.quantile:
sorted_by_price = sorted(price.items(), key = lambda x: x[1], reverse = True)
tercile = int(len(sorted_by_price) / self.quantile)
long = [x[0] for x in sorted_by_price[-tercile:]]
# Trade execution.
invested = [x.Key for x in self.Portfolio if x.Value.Invested]
for symbol in invested:
if symbol not in long:
self.Liquidate(symbol)
for symbol in long:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, 1 / len(long))
# NOTE: IMPORTANT: Data order must be ascending (datewise)
# Data source: https://indices.barclays/IM/21/en/indices/static/historic-cape.app
class CAPE(PythonData):
def GetSource(self, config, date, isLiveMode):
return SubscriptionDataSource("data.quantpedia.com/backtesting_data/economic/cape_by_country.csv", SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
_header_columns:List[str] = []
def Reader(self, config, line, date, isLiveMode):
data = CAPE()
data.Symbol = config.Symbol
if not line[0].isdigit():
CAPE._header_columns = line.split(',')[1:]
return None
split = line.split(',')
data.Time = datetime.strptime(split[0], "%Y-%m-%d") + timedelta(days=1)
for i, col in enumerate(CAPE._header_columns):
if split[i+1] != '':
data[col] = float(split[i+1])
else:
data[col] = 0.
data.Value = float(split[1])
return data
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))