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内部交易与动量结合

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学术论文

Momentum and Insider Trading

作者动量与内部交易 [点击查看论文]

机构

California State University, Chico

论文链接http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2293221

策略概要

该策略针对纽约证券交易所 (NYSE)、美国证券交易所 (AMEX) 和纳斯达克 (NASDAQ) 的股票，但排除股价低于 5 美元、市值处于纽约证券交易所最低十分位、或账面权益缺失/非正值的股票。每月，投资者计算净内部需求 (NID)，即过去六个月的内部购买量减去销售量，并归一化为流通股数。股票根据六个月收益率分为“赢家”（前十分位）和“输家”（后十分位）投资组合，并进一步按内部交易活动分为“买入”（NID 为正）、“卖出”（NID 为非正值）和“沉默”（无交易活动）类别。该策略做多具有正 NID 的赢家股票，并做空处于沉默投资组合中的输家股票。所有头寸采用等权重配置，持有期为 12 个月，并按 1/12 比例进行每月再平衡。

II. 策略合理性

学术研究表明，只有当内部交易与过去的回报一致时，短期动量才会持续——积极的内部活动支持赢家，而消极的活动证实输家。这种动量源于投资者对内部交易信息反应不足。然而，由于潜在的监管和诉讼风险，内部人士很谨慎，尤其是对于销售而言，这造成了他们行为的不对称性。当掌握负面私人信息并预期价格大幅下跌时，内部人士通常会避免出售，以避免法律审查。相反，当他们掌握有关公司的正面信息时，他们更有可能进行交易，从而加强了观察到的内部活动与动量驱动的股票表现之间的联系。

回测表现

胜率28%

完整 Python 代码

1from AlgorithmImports import *
2import pandas as pd
3from io import StringIO
4from numpy import floor
5#endregion
6class MomentumCombinedInsiderTrading(QCAlgorithm):
7def Initialize(self):
8self.SetStartDate(2010, 1, 1)
9self.SetCash(100000)
10# NOTE: We use only s&p 100 stocks so it's possible to fetch short interest data from quandl.
11self.symbols = [
12    'AAPL','MSFT','AMZN','FB','BRKB','GOOGL','GOOG','JPM','JNJ','V','PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ','MRK',
13    'PFE','CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA','ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN','PM','PYPL',
14    'TMO','COST','ABBV','ACN','HON','NKE','UNP','UTX','NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT','GE','MMM','DHR',
15    'QCOM','CVS','MO','LOW','FIS','AXP','BKNG','UPS','GILD','CHTR','CAT','MDLZ','GS','USB','CI','ANTM','BDX','TJX','ADP','TFC','CME',
16    'SPGI','COP','INTU','ISRG','CB','SO','D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK'
17    ]
18    
19self.period = 6 * 21
20
21# Trenching
22self.holding_period = 12
23self.managed_queue = []
24
25# Dataframe with insider trades for every stock.
26self.insiders_trading = {}
27
28# Create custom universe.
29self.selection_flag = False
30
31self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
32self.shares_outstanding = {}
33for symbol in self.symbols:
34    # Import insiders trading data.
35    csv_string_file = self.Download(f'data.quantpedia.com/backtesting_data/economic/insiders_trading/{symbol}.csv')
36    if csv_string_file == "": continue
37    parser = lambda x: pd.datetime.strptime(x, "%Y-%m-%d")
38    self.insiders_trading[symbol] = pd.read_csv(StringIO(csv_string_file), sep=';', parse_dates=['Tran.Date'], date_parser=parser)
39self.UniverseSettings.Resolution = Resolution.Daily
40self.AddUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction))
41self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
42def OnSecuritiesChanged(self, changes):
43for security in changes.AddedSecurities:
44    security.SetFeeModel(CustomFeeModel())
45    security.SetLeverage(10)
46    
47def CoarseSelectionFunction(self, coarse):
48if not self.selection_flag:
49    return Universe.Unchanged
50
51return [Symbol.Create(x, SecurityType.Equity, Market.USA) for x in self.symbols]
52def FineSelectionFunction(self, fine):
53fine = [x for x in fine if x.EarningReports.BasicAverageShares.ThreeMonths > 0 and x.Symbol.Value in self.insiders_trading]
54symbols = [x.Symbol for x in fine]
55
56history = self.History(symbols, self.period, Resolution.Daily)
57if history.empty:
58    self.Log(f'Empty history request for {len(symbols)} symbols')
59    return Universe.Unchanged
60history = history.close.unstack(0)
61
62last_prices = {}
63performance = {}
64
65for symbol in symbols:
66    if symbol in history:
67        closes = history[symbol]
68        if len(closes) == self.period:
69            performance[symbol] = closes[-1] / closes[0] - 1
70            last_prices[symbol] = closes[-1]
71        
72# Stock which have not been traded last 6 months.
73silence = []
74
75# Traded stocks.
76nid = {}
77
78for stock in fine:
79    symbol = stock.Symbol
80    
81    # Get number of buys and sells during last 6 months.
82    ticker = symbol.Value
83    buys = [row['Shares'] for index, row in self.insiders_trading[ticker].iterrows() if row['Symbol'] == ticker and row['Tran.Date'] >= (self.Time - timedelta(days = 6 * 30)) and row['Tran.Date'] <= self.Time and row['Action'] == 'B'] 
84    sells = [row['Shares'] for index, row in self.insiders_trading[ticker].iterrows() if row['Symbol'] == ticker and row['Tran.Date'] >= (self.Time - timedelta(days = 6 * 30)) and row['Tran.Date'] <= self.Time and row['Action'] == 'S']
85            
86    total_buy_shares = sum(buys)
87    total_sell_shares = sum(sells)
88    
89    if len(buys) != 0 or len(sells) != 0:
90        nid[symbol] = (total_buy_shares - total_sell_shares) / stock.EarningReports.BasicAverageShares.ThreeMonths
91    else:
92        # Stock was not traded during last 6 months.
93        silence.append(symbol)
94        
95decile = int(len(performance) / 10)
96sorted_by_performance = [x[0] for x in sorted(performance.items(), key=lambda item: item[1])]
97winners = sorted_by_performance[-decile:]
98losers = sorted_by_performance[:decile]
99
100# long = [x[0] for x in performance.items() if x[1] > 0 and x[0] in nid and nid[x[0]] > 0]
101# short = [x[0] for x in performance.items() if x[1] < 0 and x[0] in silence]
102
103# Each month investor goes long past winners with a positive NID and goes short past losers from “silence” portfolio.
104long = [x for x in winners if x in nid and nid[x] > 0]
105short = [x for x in losers if x in silence]
106
107if len(long) != 0:
108    long_w = self.Portfolio.TotalPortfolioValue / self.holding_period / len(long)
109    # symbol/quantity collection
110    long_symbol_q = [(x, floor(long_w / last_prices[x])) for x in long]
111else:
112    long_symbol_q = []
113
114if len(short) != 0:
115    short_w = self.Portfolio.TotalPortfolioValue / self.holding_period / len(short)
116    # symbol/quantity collection
117    short_symbol_q = [(x, -floor(short_w / last_prices[x])) for x in short]
118else:
119    short_symbol_q = []
120
121self.managed_queue.append(RebalanceQueueItem(long_symbol_q + short_symbol_q))
122
123return long + short
124def OnData(self, data):
125if not self.selection_flag:
126    return
127self.selection_flag = False
128
129# Trade execution
130remove_item = None
131
132# Rebalance portfolio
133for item in self.managed_queue:
134    if item.holding_period == self.holding_period + 1: # Each month investor goes long past winners with a positive NID and goes short past losers from “silence” portfolio.
135        # Liquidate
136        for symbol, quantity in item.symbol_q:
137            self.MarketOrder(symbol, -quantity)
138        
139        remove_item = item
140        
141    elif item.holding_period == 1: # Each month investor goes long past winners with a positive NID and goes short past losers from “silence” portfolio.
142        open_symbol_q = []
143        
144        for symbol, quantity in item.symbol_q:
145            if symbol in data and data[symbol]:
146                self.MarketOrder(symbol, quantity)
147                open_symbol_q.append((symbol, quantity))
148                    
149        # Only opened orders will be closed        
150        item.symbol_q = open_symbol_q
151        
152    item.holding_period += 1
153    
154# We need to remove closed part of portfolio after loop. Otherwise it will miss one item in self.managed_queue.
155if remove_item:
156    self.managed_queue.remove(remove_item)
157    
158def Selection(self):
159self.selection_flag = True
160class RebalanceQueueItem():
161def __init__(self, symbol_q):
162# symbol/quantity collections
163self.symbol_q = symbol_q
164self.holding_period = 0
165# Custom fee model
166class CustomFeeModel(FeeModel):
167def GetOrderFee(self, parameters):
168fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
169return OrderFee(CashAmount(fee, "USD"))