股票中的盈利加速效应
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Earnings Acceleration and Stock Returns
Earnings Acceleration and Stock Returns [点击查看论文]
- Tulane University
- ?Tulane University - A.B. Freeman School of Business
- ?Tulane University - Accounting & Taxation
策略概要
该策略针对CRSP中的纽约证券交易所、美国证券交易所和纳斯达克股票，不包括金融（SIC 6000–6999）和公用事业（SIC 4900–4949）公司。盈利加速计算为连续两个四季度期间每股收益（EPS）增长率的变化，并按股票价格缩放。股票根据盈利加速分为十分位数，做多最高十分位数，做空最低十分位数。持有期从季度t的盈利公告后两天开始，到该月的第30天结束。投资组合按价值加权，并每日重新平衡，以适应不同的盈利公告日期。
II. 策略合理性
盈利加速策略产生了异常回报，因为投资者未能充分理解当前盈利加速对未来盈利增长的影响。测试证实，正的策略回报与未来盈利增长之间存在很强的关联。与其他在2003年后重要性下降的异常现象不同，该策略继续表现稳健，在176个季度内，超额回报保持显著且稳定。该策略还满足了解释横截面回报的建议t统计阈值（>3.0），并展示了与价值加权投资组合一致的结果。这些发现突显了该策略在不同市场条件和时间段内的经济意义、持久性和实际可行性。
回测表现
波动率13.81%
夏普比率1.44
索提诺比率-0.405
胜率50%
完整 Python 代码
from AlgorithmImports import *
from typing import Dict, List
from numpy import isnan
class EarningsAccelerationEffectinStocks(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2004, 1, 1)
self.SetCash(100000)
self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']
self.leverage:int = 5
self.quantile:int = 10
self.min_share_price:int = 5
self.quarters_count:int = 6 # Number of quarters to calculate the earning acceleration indicator
self.fundamental_count:int = 1000
self.fundamental_sorting_key = lambda x: x.DollarVolume
self.weight:Dict[Symbol, float] = {}
self.eps_by_symbol:Dict[Symbol, RollingWindow] = {} # Contains RollingWindow objects for every stock
self.last_fundamental:List[Symbol] = []
self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.selection_flag:bool = False
self.settings.daily_precise_end_time = False
self.settings.minimum_order_margin_portfolio_percentage = 0.
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), 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]:
'''Drop securities which have no fundamental data or have too low prices.
Select those with the highest dollar volume'''
if not self.selection_flag:
return Universe.Unchanged
selected:List[Fundamental] = [
x for x in fundamental if x.HasFundamentalData and x.Price > self.min_share_price and x.MarketCap != 0 \
and not isnan(x.EarningReports.BasicEPS.ThreeMonths) and x.EarningReports.BasicEPS.ThreeMonths > 0 \
and x.SecurityReference.ExchangeId in self.exchange_codes
]
if len(selected) > self.fundamental_count:
selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
ea_by_stock:Dict[Symbol, float] = {}
for stock in selected:
symbol:Symbol = stock.Symbol
# If stock wasn't in last_fundamental we need to reintialize it to make sure data are consecutive
if symbol not in self.eps_by_symbol or symbol not in self.last_fundamental:
self.eps_by_symbol[symbol] = RollingWindow[float](self.quarters_count)
# update rolling window for every stock
self.eps_by_symbol[symbol].Add(stock.EarningReports.BasicEPS.ThreeMonths)
if self.eps_by_symbol[symbol].IsReady:
rw:List[float] = [x for x in self.eps_by_symbol[symbol]]
eps_fraction1:float = (rw[0] - rw[4]) / rw[1]
eps_fraction2:float = (rw[1] - rw[5]) / rw[2]
ea_by_stock[stock] = eps_fraction1 - eps_fraction2 # That's the earnings acceleration we want
sorted_by_ea:List[Fundamental] = [x[0] for x in sorted(ea_by_stock.items(), key = lambda item: item[1], reverse = True)]
# Create long and short quantile
quantile:int = int(len(sorted_by_ea) / self.quantile)
long:List[Fundamental] = sorted_by_ea[:quantile]
short:List[Fundamental] = sorted_by_ea[-quantile:]
# Calculate weight for each stock in portfolio
for i, portfolio in enumerate([long, short]):
mc_sum:float = sum(list(map(lambda stock: stock.MarketCap, portfolio)))
for stock in portfolio:
self.weight[stock.Symbol] = ((-1) ** i) * stock.MarketCap / mc_sum
# Change last fundamental to make sure data are consecutive
self.last_fundamental = [x.Symbol for x in fundamental]
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"))