Original paper
Abstract
Exchange Traded Funds (ETFs) consistently trade away from their net asset value. In violation of market efficiency, these discounts vary substantially over time and are found to be significant in the explanation of future returns. Returns to simple strategies which incorporate information in the variation of discounts outperform buy-and-hold strategies by an annualized 15%, net of transaction costs, but only expose the investor to about one fifth the risk. ETFs, on average, are found to be about 17% more volatile than their underlying assets; 70% of the excess volatility can be explained by proxies for transaction and holding costs which inhibit successful arbitrage. The findings in this paper are consistent with noise trader models of costly arbitrage and are inconsistent with hypotheses of financial market efficiency.
Keywords:Â Limits of Arbitrage, Efficient Markets Hypothesis, Exchange Traded Fund, Closed-end Fund Puzzle, Noise Trader Model
Trading rules
- Investment scope: Every ETF available in the market.
- Data acquisition: Gather historical End of Day premium/discount rates from sources like Bloomberg, Yahoo, or directly from ETF providers.
- Analytical approach: Determine the average premium/discount and its standard deviation for every ETF.
- Buying criteria: Acquire ETF at day-end when its discount surpasses two standard deviations from its historical average.
- Selling criteria: Liquidate the position when the ETF premium exceeds one standard deviation from its historical average in upcoming trading sessions.
- Asset allocation: Ensure uniform weightage across all active ETF positions in the portfolio.
Python code
Backtrader
import backtrader as bt
class ETFStatArb(bt.Strategy):
def __init__(self):
self.avg_premium_discount = dict()
self.std_premium_discount = dict()
def prenext(self):
if len(self.data) < 2:
return
for d in self.datas:
if d._name not in self.avg_premium_discount:
self.avg_premium_discount[d._name] = bt.indicators.SimpleMovingAverage(d.premium_discount, period=2)
self.std_premium_discount[d._name] = bt.indicators.StdDev(d.premium_discount, period=2)
def next(self):
for d in self.datas:
avg_pd = self.avg_premium_discount[d._name]
std_pd = self.std_premium_discount[d._name]
position = self.getposition(d)
if d.premium_discount[0] < avg_pd[0] - 2 * std_pd[0] and not position:
self.buy(data=d, size=self.broker.get_cash() / len(self.datas))
elif position and d.premium_discount[0] > avg_pd[0] + std_pd[0]:
self.sell(data=d, size=position.size)
if __name__ == '__main__':
cerebro = bt.Cerebro()
# Add data feeds for all ETFs
# Example: cerebro.adddata(bt.feeds.YahooFinanceData(dataname='SPY', fromdate=start, todate=end, plot=False))
cerebro.addstrategy(ETFStatArb)
cerebro.broker.setcash(100000.0)
cerebro.broker.setcommission(commission=0.001)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())Please note that you will need to add the data feeds for all the ETFs you want to use in your investment universe, and make sure to set the correct date range for your historical data. The example line provided in the code is a placeholder for adding the data feeds.