Short Interest Strategy
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The Good News in Short Interest
Ekkehart Boehmer; Zsuzsa R. Huszár; Bradford D. Jordan
- SGSingapore Management University
- ?Singapore Management University - Lee Kong Chian School of Business
- ATCentral European University
- SGNational University of Singapore
- University of Florida
- ?University of Florida - Department of Finance, Insurance and Real Estate
Strategy in a nutshell
All stocks from NYSE, AMEX, and NASDAQ are part of the investment universe. The short-interest ratio is used as the predictor variable. Stocks are sorted based on their short interest ratio, and the first percentile is held. The portfolio is equally weighted and rebalanced monthly.
Economic rationale
The literature offers two popular explanations for this predictability, namely the overvaluation hypothesis and the information hypothesis. The first possible explanation for the short interest effect – the overvaluation hypothesis stems from the work of Miller (1977). His theory says that stocks with high levels of short interest are overvalued because pessimistic investors are unable to establish short positions, leaving only the optimists to participate in the pricing process. In this model, market forces are unable to prevent overpricing in the amount of shorting costs when these costs are high. The greater the shorting costs, the greater the possible overpricing, and therefore, the lower the subsequent stock returns.
The second and probably more valid explanation is the information hypothesis. The information hypothesis builds on a broadening base of empirical research that demonstrates that short sellers are well-informed traders. The aforementioned could be the reason for the functionality because if one follows the decisions of the short-sale practitioners, who tend to be investors with superior analytical skills (for example, according to the research of Gutfleish and Atzil, 2004). The main idea is simple; the research says, that these investors typically initiate short positions only if they can infer low fundamental valuation from public sources. For example, short-sellers may engage in forensic accounting, looking for high levels of accrual as evidence of hidden bad news. Still, there is a large number of other possibilities than just accruals.
Backtest performance
Full Python code
from AlgoLib import *
class ShortInterestEffect(XXX):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
# NOTE: We use only s&p 100 stocks so it's possible to fetch short interest data from quandl.
self.symbols = [
'AAPL','MSFT','AMZN','FB','BRK.B','GOOGL','GOOG','JPM','JNJ','V','PG','XOM','UNH','BAC','MA','T','DIS','INTC','HD','VZ','MRK','PFE',
'CVX','KO','CMCSA','CSCO','PEP','WFC','C','BA','ADBE','WMT','CRM','MCD','MDT','BMY','ABT','NVDA','NFLX','AMGN','PM','PYPL','TMO',
'COST','ABBV','ACN','HON','NKE','UNP','UTX','NEE','IBM','TXN','AVGO','LLY','ORCL','LIN','SBUX','AMT','LMT','GE','MMM','DHR','QCOM',
'CVS','MO','LOW','FIS','AXP','BKNG','UPS','GILD','CHTR','CAT','MDLZ','GS','USB','CI','ANTM','BDX','TJX','ADP','TFC','CME','SPGI',
'COP','INTU','ISRG','CB','SO','D','FISV','PNC','DUK','SYK','ZTS','MS','RTN','AGN','BLK'
]
for symbol in self.symbols:
data = self.AddEquity(symbol, Resolution.Daily)
data.SetFeeModel(CustomFeeModel())
data.SetLeverage(5)
self.AddData(QuandlFINRA_ShortVolume, 'FINRA/FNSQ_' + symbol, Resolution.Daily)
self.recent_month = -1
def OnData(self, data):
if self.recent_month == self.Time.month:
return
self.recent_month = self.Time.month
short_interest = {}
for symbol in self.symbols:
sym = 'FINRA/FNSQ_' + symbol
if sym in data and data[sym] and symbol in data and data[symbol]:
short_vol = data[sym].GetProperty("SHORTVOLUME")
total_vol = data[sym].GetProperty("TOTALVOLUME")
short_interest[symbol] = short_vol / total_vol
long = []
if len(short_interest) >= 10:
sorted_by_short_interest = sorted(short_interest.items(), key = lambda x: x[1], reverse = True)
decile = int(len(sorted_by_short_interest) / 10)
long = [x[0] for x in sorted_by_short_interest[-decile:]]
# trade execution
stocks_invested = [x.Key.Value for x in self.Portfolio if x.Value.Invested]
for symbol in stocks_invested:
if symbol not in long:
self.Liquidate(symbol)
for symbol in long:
self.SetHoldings(symbol, 1 / len(long))
class QuandlFINRA_ShortVolume(PythonQuandl):
def __init__(self):
self.ValueColumnName = 'SHORTVOLUME' # also 'TOTALVOLUME' is accesible.
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))