Shorting Companies With the Most Overpaid CEOs
Log in to collectStrategy in a nutshell
Yearly long-short strategy on NYSE stocks: short the top 100 firms with overpaid CEOs (from rankings like “As You Sow”) and hedge with a long position in the S&P500. Equal-weighted; rebalanced annually.
Economic rationale
Stocks of companies with overpaid CEOs tend to underperform due to poor governance and potential morale/productivity drops. Exploiting this empirical pattern provides a rationale for shorting these firms while hedging market exposure.
Backtest performance
Annualised return5.41%
Volatility8.55%
Beta0.047
Sharpe ratio0.63
Win rate24%
Full Python code
from AlgorithmImports import *
from io import StringIO
from pandas.core.frame import DataFrame
from typing import List, Dict
import pandas as pd
# endregion
class ShortingCompaniesWiththeMostOverpaidCEOs(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2015, 1, 1)
self.SetCash(100000)
self.market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.leverage:int = 3
self.selection_month:int = 3
self.short:List[Symbol] = []
# source: https://www.asyousow.org/reports/the-100-most-overpaid-ceos-2022
overpaid_CEO:str = self.Download('data.quantpedia.com/backtesting_data/economic/overpaid_CEO.csv')
self.overpaid_CEO_df:DataFrame = pd.read_csv(StringIO(overpaid_CEO), delimiter=';')
self.selection_flag:bool = False
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.Schedule.On(self.DateRules.MonthStart(self.market), self.TimeRules.AfterMarketOpen(self.market), self.Selection)
def OnSecuritiesChanged(self, changes:SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def CoarseSelectionFunction(self, coarse:List[CoarseFundamental]) -> List[Symbol]:
# selection on start of March
if not self.selection_flag:
return Universe.Unchanged
selected:List[Symbol] = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa']
return selected
def FineSelectionFunction(self, fine:List[FineFundamental]) -> List[Symbol]:
fine:List[FineFundamental] = [x for x in fine if x.MarketCap != 0 and \
(x.SecurityReference.ExchangeId == 'NYS')]
fine:Dict[str, Symbol] = {x.Symbol.Value: x.Symbol for x in fine}
if str(self.Time.year) in list(self.overpaid_CEO_df.columns):
self.short = [fine[x] for x in self.overpaid_CEO_df[str(self.Time.year)].values if x in fine]
else:
self.Liquidate()
return self.short
def OnData(self, data: Slice) -> None:
# yearly rebalance
if not self.selection_flag:
return
self.selection_flag = False
invested:List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
for price_symbol in invested:
if price_symbol not in self.short + [self.market]:
self.Liquidate(price_symbol)
# trade execution
if len(self.short) != 0:
if self.market in data and data[self.market]:
self.SetHoldings(self.market, 1)
for symbol in self.short:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, -1 / len(self.short))
self.short.clear()
def Selection(self) -> None:
if self.Time.month == self.selection_month:
self.selection_flag = True
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))