“长期继任投资组合”包括已宣布首席执行官离职但尚未任命新首席执行官的公司。该投资组合采用等权重，每月进行再平衡，并针对卡哈特四因子（市场、价值、规模、动量）进行对冲。”
资产类别: 股票 | 地区: 美国 | 周期: 每月 | 市场: 股票 | 关键词: CEO
I. 策略概要
该策略的投资范围是标普1500指数中的公司。“长期继任投资组合”专门针对处于“跛脚鸭”状态的首席执行官所在的公司，即当前首席执行官已宣布离职，但新任首席执行官尚未确定。这些公司会在现任首席执行官离职声明发布后的下个月初被纳入投资组合，并一直保留到新任首席执行官在月底公布为止。该投资组合采用等权重，每月进行再平衡，并针对卡哈特的四因子（市场、价值、规模和动量）进行对冲。
II. 策略合理性
该论文指出了在“跛脚鸭”首席执行官（CEO）时期出现正超额回报的两个原因。首先，投资者对这段时期缺乏关于继任者的消息反应迟钝，导致了这种异常现象。风险率测试表明，这种反应迟钝解释了部分正超额回报。其次，经济机制涉及在现任CEO离职公告后，内部锦标赛会选择继任者。这种竞争有利于公司价值，但其影响会逐渐反映到股价中。具有高内部锦标赛竞争的公司显示出显著的月度阿尔法，达到1.5%。此外，由内部晋升的新CEO领导的公司表现更佳，月度超额回报超过2%。进一步的测试表明，CEO离职动机、临时CEO或董事会绩效等因素并不能解释这些异常回报，这支持了内部锦标赛竞争是主要驱动因素的观点。
III. 来源论文
Lame-Duck CEOs [点击查看论文]
- Gabarro, Marc (加巴罗·马克) 和 Gryglewicz, Sebastian (格里格莱维奇·塞巴斯蒂安) 和 Xia, Shuo (夏硕). 伊拉斯姆斯大学；鹿特丹伊拉斯姆斯大学 (EUR) – 伊拉斯姆斯经济学院 (ESE)；哈勒经济研究所；莱比锡大学 – 经济与管理科学学院
<摘要>
金融当局和投资者对旷日持久的首席执行官（CEO）继任表示担忧。我们发现大约三分之一的CEO继任是旷日持久的，在此期间，“跛脚鸭”CEO会继续管理公司约六个月，然后才宣布继任者。尽管市场对旷日持久的继任公告反应负面，但由“跛脚鸭”CEO管理的公司在多项指标上表现良好：它们产生了每年9.6%的四因子阿尔法，并在盈利公告前后表现出正异常回报。通过测试不同的机制，我们发现当内部候选人之间的竞争更加激烈时，结果会更强劲。我们的研究结果表明，市场对由“跛脚鸭”CEO管理的公司价值存在错误定价，但旷日持久的继任对公司价值并无损害。
IV. 回测表现
| 年化回报 | 11.35% |
| 波动率 | 14.71% |
| β值 | -0.033 |
| 夏普比率 | 0.5 |
| 索提诺比率 | -0.009 |
| 最大回撤 | N/A |
| 胜率 | 51% |
V. 完整的 Python 代码
from AlgorithmImports import *
from scipy import stats
from typing import List, Dict
from pandas.core.frame import dataframe
from pandas.core.series import Series
# endregion
class LameDuckCEOs(QCAlgorithm):
def Initialize(self) -> None:
self.SetStartDate(2000, 1, 1)
self.SetCash(100_000)
# Source: https://zenodo.org/record/4543893#.YwNo1xxBw2w
self.ceo_departure_dates: Symbol = self.AddData(CEODepartureDates, "CEODepartureDates", Resolution.Daily).Symbol
self.departure_company_tickers: List[str] = [] # recent month CEO departures
self.departure_company_ticker_universe: set = set() # ticker universe from the whole dataset
self.selected_universe: List[Symbol] = [] # currently monthly selected stock universe
self.price_data: Dict[Symbol, RollingWindow] = {} # daily price data
self.period: int = 12 * 21
self.leverage: int = 10
self.min_share_price: int = 5
self.market: Symbol = self.AddEquity("SPY", Resolution.Daily, leverage=self.leverage).Symbol
self.price_data[self.market] = RollingWindow[float](self.period)
self.selection_flag: bool = False
self.UniverseSettings.Leverage = self.leverage
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.settings.daily_precise_end_time = False
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
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())
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
# update the rolling window every day
for stock in fundamental:
symbol = stock.Symbol
# Store monthly price.
if symbol in self.price_data:
self.price_data[symbol].Add(stock.AdjustedPrice)
if not self.selection_flag:
return Universe.Unchanged
selected: List[Symbol] = [
x.Symbol for x in fundamental
if x.Market == 'usa'
and x.Price > self.min_share_price
and x.Symbol.Value in self.departure_company_tickers
]
for symbol in selected:
if symbol in self.price_data:
continue
self.price_data[symbol] = RollingWindow[float](self.period)
history: dataframe = self.History(symbol, self.period, Resolution.Daily)
if history.empty:
self.Log(f"Not enough data for {symbol} yet.")
continue
closes: Series = history.loc[symbol].close
for time, close in closes.items():
self.price_data[symbol].Add(close)
if self.price_data[self.market].IsReady:
self.selected_universe = [x for x in selected if self.price_data[x].IsReady]
return self.selected_universe
def OnData(self, data: Slice) -> None:
custom_data_last_update_date: Dict[Symbol, datetime.date] = CEODepartureDates.get_last_update_date()
if self.Securities[self.ceo_departure_dates].GetLastData() and self.Time.date() > custom_data_last_update_date[self.ceo_departure_dates]:
self.Liquidate()
return Universe.Unchanged
# store new ceo departure data
if data.ContainsKey(self.ceo_departure_dates):
# store whole ticker universe
if len(self.departure_company_ticker_universe) == 0:
self.departure_company_ticker_universe = CEODepartureDates._ticker_universe
departure_tickers:str = data[self.ceo_departure_dates].GetProperty('stocks')
for t in departure_tickers:
self.departure_company_tickers.append(t)
# monthly rebalance
if not self.selection_flag:
return
self.selection_flag = False
# select long leg
long: List[Symbol] = []
for symbol in self.selected_universe:
if symbol.Value in self.departure_company_tickers:
long.append(symbol)
# reset ceo departure for the recent month
self.departure_company_tickers.clear()
if len(long) == 0:
if self.Portfolio.Invested:
self.Liquidate()
return
# order execution
total_beta: float = 0.
market_prices: np.ndarray = np.array(list(self.price_data[self.market]))
market_perf_data: np.ndarray = market_prices[:-1] / market_prices[1:] - 1
targets: List[PortfolioTarget] = []
for symbol in long:
# calculate beta to market for each stock
stocks_prices: np.ndarray = np.array(list(self.price_data[symbol]))
stock_perf_data: np.ndarray = stocks_prices[:-1] / stocks_prices[1:] - 1
slope, intercept, r_value, p_value, std_err = stats.linregress(market_perf_data, stock_perf_data)
total_beta += slope
if data.contains_key(symbol) and data[symbol]:
targets.append(PortfolioTarget(symbol, 1 / len(long)))
self.SetHoldings(targets, True)
avg_beta_to_market: float = total_beta / len(long)
# market hedge
self.SetHoldings(self.market, -avg_beta_to_market)
def Selection(self) -> None:
if len(self.departure_company_ticker_universe) != 0:
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"))
# CEO departure dates.
# SOURCE: https://zenodo.org/record/4543893#.YwNo1xxBw2w
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class CEODepartureDates(PythonData):
_ticker_universe:Set[str] = set()
_last_update_date:Dict[Symbol, datetime.date] = {}
def GetSource(self, config:SubscriptionDataConfig, date:datetime, isLiveMode:bool) -> SubscriptionDataSource:
return SubscriptionDataSource("data.quantpedia.com/backtesting_data/economic/ceo_departure_dates.json", SubscriptionTransportMedium.RemoteFile, FileFormat.UnfoldingCollection)
@staticmethod
def get_last_update_date() -> Dict[Symbol, datetime.date]:
return CEODepartureDates._last_update_date
@staticmethod
def get_ticker_universe() -> list:
return list(CEODepartureDates._ticker_universe)
def Reader(self, config:SubscriptionDataConfig, line:str, date:datetime, isLiveMode:bool) -> BaseData:
objects:list[CEODepartureDates] = []
data:list[dict] = json.loads(line)
end_time:datetime.date|None = None
for index, sample in enumerate(data):
custom_data:CEODepartureDates = CEODepartureDates()
custom_data.Symbol = config.Symbol
departure_date:datetime.date = datetime.strptime(sample['departure_date'], '%Y-%m-%d')
custom_data.Time = departure_date
custom_data.EndTime = custom_data.Time + timedelta(days=1)
custom_data['stocks'] = sample['stocks']
custom_data.Value = 1
end_time = custom_data.EndTime
# store last date of the symbol
if config.Symbol not in CEODepartureDates._last_update_date:
CEODepartureDates._last_update_date[config.Symbol] = datetime(1,1,1).date()
if custom_data.Time.date() > CEODepartureDates._last_update_date[config.Symbol]:
CEODepartureDates._last_update_date[config.Symbol] = custom_data.Time.date()
for ticker in sample['stocks']:
CEODepartureDates._ticker_universe.add(ticker)
objects.append(custom_data)
return BaseDataCollection(end_time, config.Symbol, objects)