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应计项目效应结合价格动量

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学术论文

Persistence of Earnings Components and Price Momentum

作者Persistence of Earnings Components and Price Momentum [点击查看论文]

机构

The University of Texas Rio Grande Valley
University of Bristol
?University of Texas Rio Grande Valley (UTRGV) (Formerly University of Texas-Pan American)
HKHong Kong Polytechnic University

论文链接https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3207098

策略概要

该策略交易价格高于5美元的纽约证券交易所/美国证券交易所普通股。股票被分为低买卖差价和高买卖差价投资组合，重点关注低买卖差价股票。股票根据过去6个月的回报（动量）和应计项目进行双重排序，分为五分位数。该策略在应计项目最低的动量赢家投资组合中建立多头头寸，在应计项目最高的动量输家投资组合中建立空头头寸。投资组合持有六个月，形成期和持有期之间有一个月的间隔，从而创建重叠投资组合。投资组合等权重，每月重新平衡，利用动量和应计项目的相互作用来获取回报。

II. 策略合理性

该论文将众所周知的动量异常和应计项目异常结合起来，形成一种增强的动量策略。研究结果与应计项目异常的盈利固着解释相符，表明投资者忽视了应计项目与现金流相比的较低持久性，从而加剧了错误定价并增加了动量收益。这支持了动量源于投资者未能准确处理信息准确的观点。

增强型策略在各种市场状况、投资者情绪水平和子周期中始终优于传统动量策略。其表现对一月效应、时间变化和交易成本具有鲁棒性。重要的是，应计项目的增量效应不能完全由现有资产定价模型或常见风险因素解释。

回测表现

波动率10.82%

夏普比率0.59

索提诺比率-0.098

胜率52%

完整 Python 代码

1from numpy import floor, isnan
2from AlgorithmImports import *
3from typing import List, Dict
4import data_tools
5class AccrualsEffectPriceMomentum(QCAlgorithm):
6def Initialize(self):
7self.SetStartDate(2000, 1, 1)
8self.SetCash(100000)
9
10self.exchange_codes:List[str] = ['NYS', 'ASE']	
11self.long:List[Symbol] = []
12self.short:List[Symbol] = []
13
14self.quantile:int = 5
15self.leverage:int = 5
16self.min_share_price:int = 5
17self.months:int = 0
18self.period:int = 6 * 21
19self.holding_period:int = 6
20
21self.data:Dict[Symbol, data_tools.SymbolData] = {}
22self.managed_queue:List[data_tools.RebalanceQueueItem] = []
23
24# Latest accurals data
25self.accural_data:Dict[Symbol, data_tools.AccuralsData] = {}
26
27self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
28
29self.fundamental_count:int = 1000
30self.fundamental_sorting_key = lambda x: x.MarketCap
31self.selection_flag:bool = False
32self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
33self.UniverseSettings.Resolution = Resolution.Daily
34self.AddUniverse(self.FundamentalSelectionFunction)
35self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
36self.settings.daily_precise_end_time = False
37def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
38for security in changes.AddedSecurities:
39    security.SetFeeModel(data_tools.CustomFeeModel())
40    security.SetLeverage(self.leverage)
41
42def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
43# Update the rolling window every day.
44for stock in fundamental:
45    symbol:Symbol = stock.Symbol
46    # Store monthly price.
47    if symbol in self.data:
48        self.data[symbol].update(stock.AdjustedPrice)
49if not self.selection_flag:
50    return Universe.Unchanged
51selected:List[Fundamental] = [
52    x for x in fundamental if x.HasFundamentalData and x.Price > self.min_share_price and x.Market == 'usa' and x.MarketCap != 0
53    and not isnan(x.FinancialStatements.BalanceSheet.CurrentAssets.ThreeMonths) and (x.FinancialStatements.BalanceSheet.CurrentAssets.ThreeMonths > 0) \
54    and not isnan(x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.ThreeMonths) and (x.FinancialStatements.BalanceSheet.CashAndCashEquivalents.ThreeMonths) > 0 \
55    and not isnan(x.FinancialStatements.BalanceSheet.CurrentLiabilities.ThreeMonths) and (x.FinancialStatements.BalanceSheet.CurrentLiabilities.ThreeMonths) > 0 \
56    and not isnan(x.FinancialStatements.BalanceSheet.CurrentDebt.ThreeMonths) and (x.FinancialStatements.BalanceSheet.CurrentDebt.ThreeMonths) > 0 \
57    and not isnan(x.FinancialStatements.BalanceSheet.IncomeTaxPayable.ThreeMonths) and (x.FinancialStatements.BalanceSheet.IncomeTaxPayable.ThreeMonths) > 0 \
58    and not isnan(x.FinancialStatements.IncomeStatement.DepreciationAndAmortization.ThreeMonths) and (x.FinancialStatements.IncomeStatement.DepreciationAndAmortization.ThreeMonths) > 0 \
59    and x.SecurityReference.ExchangeId in self.exchange_codes
60]
61if len(selected) > self.fundamental_count:
62    selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
63# Warmup price rolling windows.
64for stock in selected:
65    symbol:Symbol = stock.Symbol
66    if symbol in self.data:
67        continue
68    self.data[symbol] = data_tools.SymbolData(self.period)
69    history = self.History(symbol, self.period, Resolution.Daily)
70    if history.empty:
71        self.Log(f"Not enough data for {symbol} yet")
72        continue
73    closes = history.loc[symbol].close
74    for time, close in closes.items():
75        self.data[symbol].update(close)
76        
77bs_acc:Dict[Symbol, float] = {}
78momentum:Dict[Symbol, float] = {} 
79current_accurals_data:Dict[Symbol, data_tools.AccuralsData] = {}
80
81for stock in selected:
82    symbol = stock.Symbol
83    
84    if not self.data[symbol].is_ready():
85        continue
86    momentum[symbol] = self.data[symbol].performance()
87    
88    # Accural calc
89    current_accurals_data[symbol] = data_tools.AccuralsData(stock.FinancialStatements.BalanceSheet.CurrentAssets.ThreeMonths, stock.FinancialStatements.BalanceSheet.CashAndCashEquivalents.ThreeMonths,
90                                                stock.FinancialStatements.BalanceSheet.CurrentLiabilities.ThreeMonths, stock.FinancialStatements.BalanceSheet.CurrentDebt.ThreeMonths, stock.FinancialStatements.BalanceSheet.IncomeTaxPayable.ThreeMonths,
91                                                stock.FinancialStatements.IncomeStatement.DepreciationAndAmortization.ThreeMonths, stock.FinancialStatements.BalanceSheet.TotalAssets.ThreeMonths)
92
93    if symbol in self.accural_data:
94        bs_acc[symbol] = self.CalculateAccurals(current_accurals_data[symbol], self.accural_data[symbol])
95
96# Clear old accruals and set new ones 
97self.accural_data.clear()
98for symbol in current_accurals_data:
99    self.accural_data[symbol] = current_accurals_data[symbol]
100
101long:List[Symbol] = []
102short:List[Symbol] = []
103    
104if len(momentum) != 0 and len(bs_acc) != 0:
105    # Momentum sorting
106    sorted_by_mom:List[Tuple[Symbol, float]] = sorted(momentum.items(), key = lambda x: x[1], reverse = True)
107    quintile:int = int(len(sorted_by_mom) / self.quantile)
108    top_by_mom:List[Symbol] = [x[0] for x in sorted_by_mom[:quintile]]
109    low_by_mom:List[Symbol] = [x[0] for x in sorted_by_mom[-quintile:]]
110
111    # Accural sorting
112    sorted_by_acc:List[Tuple[Symbol, float]] = sorted(bs_acc.items(), key = lambda x: x[1], reverse = True)
113    quintile:int = int(len(sorted_by_acc) / self.quantile)
114    top_by_acc:List[Symbol] = [x[0] for x in sorted_by_acc[:quintile]]
115    low_by_acc:List[Symbol] = [x[0] for x in sorted_by_acc[-quintile:]]
116    
117    long = [x for x in top_by_mom if x in low_by_acc]
118    short = [x for x in low_by_mom if x in top_by_acc]
119    
120    if len(long) != 0:
121        long_w:float = self.Portfolio.TotalPortfolioValue / self.holding_period / len(long)
122        # symbol/quantity collection
123        long_symbol_q:List[Tuple[Symbol, int]] = [(x, floor(long_w / self.data[x].LastPrice)) for x in long]
124    else:
125        long_symbol_q = []
126
127    if len(short) != 0:
128        short_w:float = self.Portfolio.TotalPortfolioValue / self.holding_period / len(short)
129        # symbol/quantity collection
130        short_symbol_q:List[Tuple[Symbol, int]] = [(x, floor(short_w / self.data[x].LastPrice)) for x in short]
131    else:
132        short_symbol_q = []
133        
134    self.managed_queue.append(data_tools.RebalanceQueueItem(long_symbol_q, short_symbol_q))
135
136return long + short
137
138def OnData(self, data: Slice) -> None:
139if not self.selection_flag:
140    return
141self.selection_flag = False
142
143remove_item = None
144
145# Rebalance portfolio
146for item in self.managed_queue:
147    if item.holding_period == self.holding_period + 1: # All portfolios are held for six months (month 2 to 7)
148        
149        # Selling long on Liquidate
150        for symbol, quantity in item.long_symbol_q:
151            self.MarketOrder(symbol, -quantity)
152                    
153        # Buying short on Liquidate
154        for symbol, quantity in item.short_symbol_q:
155            self.MarketOrder(symbol, quantity)
156        
157        remove_item = item
158    
159    # Trade execution    
160    if item.holding_period == 1: # All portfolios are held for six months (month 2 to 7)
161        open_long_symbol_q:List[Tuple[Symbol, int]] = []
162        open_short_symbol_q:List[Tuple[Symbol, int]] = []
163        
164        for symbol, quantity in item.long_symbol_q:
165            if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable:
166                self.MarketOrder(symbol, quantity)
167                open_long_symbol_q.append((symbol, quantity))
168                    
169        for symbol, quantity in item.short_symbol_q:
170            if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable:
171                self.MarketOrder(symbol, -quantity)
172                open_short_symbol_q.append((symbol, quantity))
173        
174        # Only opened orders will be closed        
175        item.long_symbol_q = open_long_symbol_q
176        item.short_symbol_q = open_short_symbol_q
177        
178    item.holding_period += 1
179    
180# We need to remove closed part of portfolio after loop. Otherwise it will miss one item in self.managed_queue.
181if remove_item:
182    self.managed_queue.remove(remove_item)
183
184def Selection(self) -> None:
185self.selection_flag = True
186def CalculateAccurals(self, current_accural_data, prev_accural_data):
187delta_assets:float = current_accural_data.CurrentAssets - prev_accural_data.CurrentAssets
188delta_cash:float = current_accural_data.CashAndCashEquivalents - prev_accural_data.CashAndCashEquivalents
189delta_liabilities:float = current_accural_data.CurrentLiabilities - prev_accural_data.CurrentLiabilities
190delta_debt:float = current_accural_data.CurrentDebt - prev_accural_data.CurrentDebt
191delta_tax:float = current_accural_data.IncomeTaxPayable - prev_accural_data.IncomeTaxPayable
192dep:float = current_accural_data.DepreciationAndAmortization
193avg_total:float = (current_accural_data.TotalAssets + prev_accural_data.TotalAssets) / 2
194
195bs_acc:float = ((delta_assets - delta_cash) - (delta_liabilities - delta_debt-delta_tax) - dep) / avg_total
196return bs_acc