Original paper
Abstract
A crowded trade emerges when speculators' positions are large relative to the asset's liquidity, making exit difficult. We study this problem of recent regulatory concern by focusing on short-selling. We show that days to cover (DTC), the ratio of short interest to trading volume, measures the costliness of exiting crowded trades. Crowding is an important concern as short-sellers avoid illiquid stocks, which we establish using an instrumental-variables strategy involving staggered stock market decimalization reforms. Arbitrageurs require a premium to enter into such trades as a strategy shorting high DTC stocks and buying low DTC stocks generates a 1.2% monthly return. A smaller days-to-cover effect also exists on the long positions of levered hedge funds.
Keywords:Â Days to Cover, Crowded Trades, Stock Returns
Trading rules
- Define the investment scope to encompass all firms listed on NYSE, AMEX, and NASDAQ.
- Determine daily turnover by comparing the number of traded shares to the total number of shares available.
- Calculate the monthly turnover by averaging the daily turnover ratios throughout the month.
- Evaluate the Days-to-Cover (DTC) metric: Divide a stock's short ratio by its mean daily shares turnover, done on a monthly basis.
- At the end of each month, categorize stocks into ten groups based on their DTC values.
- Implement a long-short approach: Invest in the decile with the lowest DTC and short the one with the highest DTC.
- Every month, adjust the portfolio to value-weighted stocks.
Python code
Backtrader
import backtrader as bt
import pandas as pd
class DaysToCoverStrategy(bt.Strategy):
params = (
('deciles', 10),
)
def __init__(self):
self.turnover = {}
self.dtcs = {}
def turnover_calc(self, data):
return (data.volume[0] / data.outstanding_shares[0])
def dtc_calc(self, ticker):
short_ratio = self.getdatabyname(ticker).short_ratio[0]
avg_daily_turnover = self.turnover[ticker][-1]
return short_ratio / avg_daily_turnover
def prenext(self):
for data in self.datas:
ticker = data._name
if ticker not in self.turnover:
self.turnover[ticker] = []
self.turnover[ticker].append(self.turnover_calc(data))
def next(self):
if self._dplotter.count % 20 == 0: # Monthly rebalancing
for ticker, data in self.turnover.items():
self.dtcs[ticker] = self.dtc_calc(ticker)
sorted_dtcs = sorted(self.dtcs.items(), key=lambda x: x[1])
decile_size = len(sorted_dtcs) // self.params.deciles
long_decile = sorted_dtcs[:decile_size]
short_decile = sorted_dtcs[-decile_size:]
for ticker, dtc in long_decile:
data = self.getdatabyname(ticker)
if self.getposition(data).size == 0:
self.buy(data=data, exectype=bt.Order.Market)
for ticker, dtc in short_decile:
data = self.getdatabyname(ticker)
if self.getposition(data).size == 0:
self.sell(data=data, exectype=bt.Order.Market)
# Close positions not in the current long/short deciles
for data in self.datas:
ticker = data._name
if ticker not in [x[0] for x in long_decile + short_decile]:
self.close(data=data, exectype=bt.Order.Market)
else:
for data in self.datas:
ticker = data._name
self.turnover[ticker].append(self.turnover_calc(data))
cerebro = bt.Cerebro()
# Add data feeds and strategy to cerebro
cerebro.addstrategy(DaysToCoverStrategy)
cerebro.run()