Original paper
Abstract
Sports betting markets offer a novel laboratory to test theories of cross-sectional asset pricing anomalies. Two features of this market – no systematic risk and terminal values exogenous to betting activity – evade the joint hypothesis problem, allowing mispricing to be detected. Examining a large and diverse set of liquid betting contracts, I find strong evidence of momentum, consistent with delayed overreaction and inconsistent with underreaction and rational pricing. Returns are a fraction of those in financial markets and fail to overcome transactions costs, preventing arbitrage from eliminating them. An insight from betting also predicts value and momentum returns in U.S. equities.
Trading rules
- Concentrate on the top quintile of stocks from NYSE, NASDAQ, and AMEX in terms of size.
- Select top 20% stocks with best 12-month momentum (performance).
- Construct a portfolio giving equal weight to each chosen stock.
- Adjust the portfolio composition once a month.
- Benchmark: A portfolio with equal allocation among the top 20% large-cap stocks from NYSE, NASDAQ, and AMEX.
Python code
Backtrader
import backtrader as bt
import backtrader.feeds as btfeeds
import datetime
class Momentum(bt.Strategy):
params = (
('momentum_period', 252),
('rebalance_interval', 21),
('num_stocks', 0.2),
)
def __init__(self):
self.add_timer(
bt.timer.SESSION_END, monthdays=[1], monthcarry=True
) # Rebalance monthly
self.inds = {}
for d in self.datas:
self.inds[d] = bt.indicators.ROC(d.close, period=self.params.momentum_period)
def prenext(self):
self.next()
def notify_timer(self, timer, when, *args, **kwargs):
if self._getminperstatus() < 0:
return
stocks = list(self.datas)
stocks.sort(key=lambda d: self.inds[d][0], reverse=True)
selected_stocks = stocks[:int(len(stocks) * self.params.num_stocks)]
for d in self.datas:
target = self.broker.getvalue() * (1 / len(selected_stocks)) if d in selected_stocks else 0
self.order_target_value(d, target)
cerebro = bt.Cerebro()
# Add data for NYSE, NASDAQ, and AMEX stocks
# Replace 'NYSE.csv', 'NASDAQ.csv', and 'AMEX.csv' with appropriate files
for market in ['NYSE.csv', 'NASDAQ.csv', 'AMEX.csv']:
data = btfeeds.GenericCSVData(
dataname=market,
fromdate=datetime.datetime(2000, 1, 1),
todate=datetime.datetime(2020, 12, 31),
dtformat=('%Y-%m-%d'),
openinterest=-1,
nullvalue=0.0,
plot=False,
)
cerebro.adddata(data)
cerebro.broker.setcash(1000000.0)
cerebro.broker.setcommission(commission=0.001) # 0.1% commission
cerebro.addstrategy(Momentum)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
results = cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.plot()Make sure to replace ‘NYSE.csv’, ‘NASDAQ.csv’, and ‘AMEX.csv’ with the appropriate files containing the stock data for the mentioned exchanges. This code uses the Backtrader framework to implement the trading strategy as described.