Equity · United States · stocks · Yearly · United States Free

Employee Satisfaction Factor

Academic paper

Employee Satisfaction and Long-run Stock Returns, 1984-2020

AuthorsHamid Boustanifar; Young Dae Kang

Institute

Ecole des Hautes Etudes Commerciales du Nord
?EDHEC Business School
KRBank of Korea
?The Bank of Korea

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3933687

Strategy in a nutshell

Universe: Firms on Fortune’s “100 Best Companies to Work For in America” list. Equal-weighted, long-only portfolio formed on February 1 each year, rebalanced annually after the new ranking is published.

Economic rationale

Companies with strong employee satisfaction and social responsibility are often undervalued by investors but deliver higher productivity and resilience. The strategy’s alpha remains significant across multiple factor models, confirming a persistent return premium tied to workplace quality.

Backtest performance

Annualised return16.07%

Volatility17.49%

Beta0.947

Sharpe ratio0.92

Sortino ratio0.359

Win rate79%

Full Python code

1from AlgorithmImports import *
2#endregion
3
4class EmployeeSatisfactionFactor(QCAlgorithm):
5
6def Initialize(self):
7self.SetStartDate(2000, 1, 1)
8self.SetCash(100_000)
9
10self.tickers_by_year: Dict[int, List[str]] = {}
11
12# download companies employees satisfaction ratings for each year
13csv_string_file: str = self.Download('data.quantpedia.com/backtesting_data/index/employee_satisfaction_top_100.csv')
14lines: List[str] = csv_string_file.split('\r\n')
15
16for line in lines:
17    line_split: List[str] = line.split(';')
18    date: datetime.date = datetime.strptime(line_split[0], "%d.%m.%Y").date()
19    year: int = date.year
20    
21    # store list of tickers by year
22    self.tickers_by_year[date.year] = line_split[1:]
23
24self.leverage: int = 5
25
26self.current_year: int = -1
27self.selection_flag: bool = False
28self.UniverseSettings.Resolution = Resolution.Daily
29self.AddUniverse(self.FundamentalSelectionFunction)
30self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
31
32self.long: List[Symbol] = []
33market: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
34self.Schedule.On(self.DateRules.MonthStart(market), self.TimeRules.BeforeMarketClose(market), self.Selection)
35
36def Selection(self) -> None:
37# beginning of February rebalance
38if self.Time.month == 2:
39    self.selection_flag = True
40
41def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
42for security in changes.AddedSecurities:
43    security.SetFeeModel(CustomFeeModel())
44    security.SetLeverage(self.leverage)
45
46def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
47self.current_year = self.Time.year
48
49# rebalance yearly
50if not self.selection_flag or self.current_year - 1 not in self.tickers_by_year:
51    return Universe.Unchanged
52    
53current_top_companies: List[str] = self.tickers_by_year[self.current_year - 1]
54
55# filter stock symbols from top companies in previous year
56self.long = [x.Symbol for x in fundamental if x.Symbol.Value in current_top_companies]
57
58return self.long
59
60def OnData(self, slice) -> None:
61# rebalance yearly
62if self.selection_flag == False:
63    return
64self.selection_flag = False
65
66# trade execution
67portfolio: List[PortfolioTarget] = [PortfolioTarget(symbol, 1. / len(self.long)) for symbol in self.long if symbol in slice and slice[symbol]]
68self.SetHoldings(portfolio, True)
69
70self.long.clear()
71
72# Custom fee model
73class CustomFeeModel(FeeModel):
74def GetOrderFee(self, parameters):
75fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
76return OrderFee(CashAmount(fee, "USD"))