债务增长效应结合资产增长效应
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融资资产增长 [点击查看论文]
策略概要
该策略的目标是纽约证券交易所、美国证券交易所和纳斯达克的股票,每年(6月底)按总资产增长对其进行排序,并将其分为十分位数。在资产增长最高的十分位数中,股票按债务增长进一步排名。投资者做空该十分位数中债务增长最高的25%的股票。投资组合采用价值加权,并每年进行再平衡,重点关注资产和债务增长较高的公司,以利用潜在的过度扩张或财务压力,通过系统的空头头寸优化回报。
II. 策略合理性
这种异常现象可以用“粉饰报表”的动机来解释,即管理者优先考虑报告的收益。如果债务成本低于收益价格比,债务融资可以提高每股收益(EPS)。在过去业绩不佳、分析师预期过于乐观或盈利能力下降的管理层压力下,这种担忧变得尤为突出。管理者对短期收益的关注通常会影响融资决策,可能导致旨在满足预期或在短期内提高感知业绩的次优财务策略。
回测表现
波动率17.02%
夏普比率0.43
索提诺比率-0.208
胜率47%
完整 Python 代码
from AlgorithmImports import *
from numpy import isnan
class DebtGrowthEffectCombinedwithAssetGrowthEffect(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.quantile:int = 10
self.sec_quantile:int = 4
self.traded_percentage:float = .5
self.leverage:int = 5
self.min_share_price:float = 5.
self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']
market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.fundamental_count:int = 3000
self.fundamental_sorting_key = lambda x: x.MarketCap
# Last year's debt value.
self.latest_debt:List[Symbol, float] = {}
self.weight:Dict[Symbol, float] = {}
self.selection_flag:bool = False
self.UniverseSettings.Resolution = Resolution.Daily
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.AddUniverse(self.FundamentalSelectionFunction)
self.Schedule.On(self.DateRules.MonthEnd(market), self.TimeRules.AfterMarketOpen(market), self.Selection)
self.settings.daily_precise_end_time = False
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]:
if not self.selection_flag:
return Universe.Unchanged
selected:List[Fundamental] = [
x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and \
x.Price >= self.min_share_price and x.SecurityReference.ExchangeId in self.exchange_codes and \
not isnan(x.OperationRatios.TotalAssetsGrowth.OneYear) and x.OperationRatios.TotalAssetsGrowth.OneYear != 0 and \
not isnan(x.FinancialStatements.BalanceSheet.TotalDebt.TwelveMonths) and x.FinancialStatements.BalanceSheet.TotalDebt.TwelveMonths != 0
]
if len(selected) > self.fundamental_count:
selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
growth_data:Dict[Fundamental, float] = {}
for stock in selected:
symbol:Symbol = stock.Symbol
if symbol not in self.latest_debt:
# Previous year's data.
self.latest_debt[symbol] = None
asset_growth:float = stock.OperationRatios.TotalAssetsGrowth.OneYear
debt:float = stock.FinancialStatements.BalanceSheet.TotalDebt.TwelveMonths
# Previous year's data is ready.
if self.latest_debt[symbol]:
debt_growth:float = (debt - self.latest_debt[symbol]) / self.latest_debt[symbol]
growth_data[stock] = (asset_growth, debt_growth)
self.latest_debt[symbol] = debt
# Remove not updated symbols.
updated_symbols:List[Symbol] = [x.Symbol for x in selected]
not_updated:List[Symbol] = [x for x in self.latest_debt if x not in updated_symbols]
for symbol in not_updated:
del self.latest_debt[symbol]
if len(growth_data) >= self.sec_quantile * self.quantile:
# Sort by asset and debt growth.
sorted_by_asset_growth:List = sorted(growth_data.items(), key = lambda x: x[1][0], reverse = True)
quantile:int = int(len(sorted_by_asset_growth) / self.quantile)
high_by_asset_growth = [x for x in sorted_by_asset_growth[:quantile]]
sorted_by_debt_growth:List = sorted(high_by_asset_growth, key = lambda x: x[1][1], reverse = True)
quantile = int(len(sorted_by_debt_growth) / self.sec_quantile)
short:List[Fundamental] = [x for x in sorted_by_debt_growth[:quantile]]
total_market_cap:float = sum([x[0].MarketCap for x in short])
for stock, _ in short:
self.weight[stock.Symbol] = -(stock.MarketCap / total_market_cap) * self.traded_percentage
return list(self.weight.keys())
def OnData(self, data: Slice) -> None:
if not self.selection_flag:
return
self.selection_flag = False
# trade execution
portfolio:List[PortfolioTarget] = [PortfolioTarget(symbol, w) for symbol, w in self.weight.items() if symbol in data and data[symbol]]
self.SetHoldings(portfolio, True)
self.weight.clear()
def Selection(self) -> None:
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"))