管理层多元化战略
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Diversity Investing [点击查看论文]
- Centre for Economic Policy Research
- ITBocconi University
- ?Bocconi University - Department of Finance
- ?Centre for Economic Policy Research (CEPR)
- ?Nova School of Business and Economics
- DEUniversity of Mannheim
- BEEuropean Corporate Governance Institute
- ?European Corporate Governance Institute (ECGI)
- ?University of Mannheim - Business School
策略概要
投资范围包括具有正账面价值和至少12个月过往回报的纽约证券交易所、美国证券交易所和纳斯达克股票。公司的传记数据来自EDGAR数据库的年度报告。多元化是根据高层管理人员的构成来衡量的，如果公司只有一个高层管理人员，则多元化得分为0。投资者做多管理团队最多元化的前五分之一公司，做空管理团队最同质化的后五分之一公司。投资组合每年重新平衡并持有，股票等权重。
II. 策略合理性
作者提出了两种对这种异常现象的解释：质量溢价和错误定价。他们发现，已知的质量因素占该策略阿尔法的比例高达25%。他们还认为，管理层多元化可能是一种尚未被发现的质量因素。错误定价效应与分析师覆盖率有关，分析师关注度越低，股票错误定价的可能性就越高。
回测表现
波动率7.38%
夏普比率0.89
索提诺比率0.236
胜率77%
完整 Python 代码
from AlgorithmImports import *
from io import StringIO
from pandas.core.frame import DataFrame
from typing import List, Dict
import pandas as pd
# endregion
class ManagementDiversityStrategy(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.market: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.leverage: int = 3
self.selection_month: int = 6
self.short_allocation: float = -.0
self.weights: Dict[Symbol, float] = {}
# source: https://www.fair360.com/top-50-list/2023/
top_diversity_firms: str = self.Download('data.quantpedia.com/backtesting_data/economic/top50_diversity_firms.csv')
self.top_diversity_firms_df: DataFrame = pd.read_csv(StringIO(top_diversity_firms), delimiter=';')
self.selection_flag: bool = 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(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 FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
# selection in the beginning of June
if not self.selection_flag:
return Universe.Unchanged
selected: Dict[str, Fundamental] = {
x.Symbol.Value: x for x in fundamental if x.HasFundamentalData and x.MarketCap != 0
}
long: List[Fundamental] = []
if str(self.Time.year) in list(self.top_diversity_firms_df.columns):
long = [selected[x] for x in self.top_diversity_firms_df[str(self.Time.year)].values if x in selected]
else:
self.Liquidate()
if len(long) != 0:
# calculate weights based on values
self.weights[self.market] = self.short_allocation
sum_long: float = sum([x.MarketCap for x in long])
for stock in long:
self.weights[stock.Symbol] = stock.MarketCap / sum_long
return list(self.weights.keys())
def OnData(self, data: Slice) -> None:
# yearly rebalance
if not self.selection_flag:
return
self.selection_flag = False
targets: List[PortfolioTarget] = [PortfolioTarget(symbol, weight) for symbol, weight in self.weights.items() if symbol in data and data[symbol]]
self.SetHoldings(targets, True)
self.weights.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: OrderFeeParameters) -> OrderFee:
fee: float = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))