股票 · 美国 Free

股票市场中现金效应的生产力

学术论文

The Productivity of Cash and the Cross-Section of Expected Stock Returns

论文摘要

我们首先从分析角度表明，股票的预期回报、公司规模以及账面市值比（B/M）与“现金生产力”具有函数依赖关系，现金生产力定义为公司经济租金与其现金持有量的比例。然后，我们通过实证分析表明，现金生产力是一个高度显著且稳健的负向预测因素，能够预测股票回报。我们的研究表明，早期研究中公司规模和账面市值比的预测能力可能源自于它们作为现金生产力的代理变量——这一新的、具有经济理性解释的因素，能够解释股票回报的横截面表现。

论文链接https://www.efmaefm.org/0EFMAMEETINGS/EFMA%20ANNUAL%20MEETINGS/2007-Austria/papers/0690.pdf

策略概要

该策略基于股票的“现金生产力”进行投资，现金生产力的计算公式为：（市场价值 − 总物理资产）/（现金 + 短期投资）。其中，市场价值包括股权市场价值和账面债务价值，而总物理资产则是资产的账面价值。股票每年根据现金生产力排序为十分位，最低生产力十分位的股票做多，最高生产力十分位的股票做空。仓位等权重，且每年重新平衡一次，通常在财务数据发布后的四月下旬进行。该策略利用了公司间现金生产力的低效性。

策略合理性

学术研究表明，预期股票回报代表了公司的股本成本。生产力较低的公司面临较高的资本成本，从而导致较高的股本回报要求。随着现金生产力的增加，股票的风险性降低，导致预期回报下降。此外，投资者通过给予高生产力公司股价溢价来奖励它们，这进一步减少了它们的未来表现。因此，低现金生产力的公司提供更好的潜在回报，而高生产力的公司尽管风险较低，可能由于其溢价估值和风险回报权衡的减弱而表现不佳。这一动态构成了“现金生产力”投资策略的基础。

回测表现

波动率11%

夏普比率1

索提诺比率0.106

胜率58%

完整 Python 代码

from AlgorithmImports import *
from typing import List, Dict
from numpy import isnan
class ProductivityCashEffectStockMarket(QCAlgorithm):
def Initialize(self)  -> None:
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol

self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']
self.fundamental_count:int = 3000
self.fundamental_sorting_key = lambda x: x.MarketCap
self.quantile:int = 10
self.leverage:int = 10
self.min_share_price:int = 5

self.long:List[Symbol] = []
self.short:List[Symbol] = []

self.selection_month:int = 4
self.selection_flag:bool = False
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
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.SetLeverage(self.leverage)
    security.SetFeeModel(CustomFeeModel())
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 not \
    isnan(x.FinancialStatements.BalanceSheet.TotalDebt.TwelveMonths) and x.FinancialStatements.BalanceSheet.TotalDebt.TwelveMonths != 0 and not \
    isnan(x.FinancialStatements.BalanceSheet.TotalEquity.TwelveMonths) and x.FinancialStatements.BalanceSheet.TotalEquity.TwelveMonths != 0 and not \
    isnan(x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths) and x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths != 0 and not \
    isnan(x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.TwelveMonths) and x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.TwelveMonths != 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]]

if len(selected) >= self.quantile:
    # Sorting by productivity of cash.    
    sorted_by_poc:List[Fundamental] = sorted(selected, key = lambda x:((x.FinancialStatements.BalanceSheet.TotalDebt.TwelveMonths + x.FinancialStatements.BalanceSheet.TotalEquity.TwelveMonths - \
                                                                        x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths) / \
                                                                        x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.TwelveMonths), reverse=True)
    quantile = int(len(sorted_by_poc) / self.quantile)
    self.long = [x.Symbol for x in sorted_by_poc[-quantile:]]
    self.short = [x.Symbol for x in sorted_by_poc[:quantile]]
return self.long + self.short

def OnData(self, data: Slice) -> None:
if not self.selection_flag:
    return
self.selection_flag = False
# Trade execution.
targets:List[PortfolioTarget] = []
for i, portfolio in enumerate([self.long, self.short]):
    for symbol in portfolio:
        if symbol in data and data[symbol]:
            targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))

self.SetHoldings(targets, True)
self.long.clear()
self.short.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):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))