Original paper
Abstract
A contrast effect occurs when the value of a previously-observed signal inversely biases perception of the next signal. We present the first evidence that contrast effects can distort prices in sophisticated and liquid markets. Investors mistakenly perceive earnings news today as more impressive if yesterday’s earnings surprise was bad and less impressive if yesterday’s surprise was good. A unique advantage of our financial setting is that we can identify contrast effects as an error in perceptions rather than expectations. Finally, we show that our results cannot be explained by a key alternative explanation involving information transmission from previous earnings announcements.
Keywords:Â contrast effects, behavioral economics, relative thinking, financial markets
Trading rules
- Investment universe: Top quintile of size-traded NYSE stocks
- Daily long-short trading strategy
- Long (buy) on day t if surprise on day t-1 is low (below 25th percentile of previous quarter)
- Short (sell) the market
- Hold for days t and t+1
- Short (sell) on day t if surprise on day t-1 is high (above 75th percentile of previous quarter)
- Long (buy) the market
- Hold for days t and t+1
- Portfolios value-weighted by market capitalization at t-3 for firms announcing earnings on day t
- Investment scope: Top 20% of NYSE stocks based on trading volume.
- Strategy involves daily alternating long-short positions.
- On day t, go long if the previous day's surprise is beneath the 25th percentile of the last quarter.
- At the same time, take a short position in the market.
- Retain these positions for both day t and day t+1.
- On day t, opt for a short position if the surprise from day t-1 exceeds the 75th percentile from the preceding quarter.
- Conversely, take a long position in the market.
- These positions should be maintained through days t and t+1.
- The value of portfolios is determined by market capitalization from day t-3 for companies that disclose their earnings on day t.
Python code
Backtrader
import backtrader as bt
class LongShortEarningsStrategy(bt.Strategy):
def __init__(self):
self.order = None
self.buy_sell_signal = 0
def log(self, txt, dt=None):
dt = dt or self.datas[0].datetime.date(0)
print(f'{dt.isoformat()}, {txt}')
def notify_order(self, order):
if order.status in [order.Submitted, order.Accepted]:
return
if order.status in [order.Completed]:
if order.isbuy():
self.log(f'BUY EXECUTED, Price: {order.executed.price}, Cost: {order.executed.value}, Comm: {order.executed.comm}')
elif order.issell():
self.log(f'SELL EXECUTED, Price: {order.executed.price}, Cost: {order.executed.value}, Comm: {order.executed.comm}')
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
self.order = None
def next(self):
if self.order:
return
if not self.position:
if self.buy_sell_signal == 1:
self.order = self.buy()
self.log(f'BUY CREATED, {self.data.close[0]}')
elif self.buy_sell_signal == -1:
self.order = self.sell()
self.log(f'SELL CREATED, {self.data.close[0]}')
else:
if (len(self) - self.bar_executed) > 1:
self.close()
self.log(f'Position closed, {self.data.close[0]}')
self.buy_sell_signal = 0
def update_buy_sell_signal(self, signal):
self.buy_sell_signal = signal
def run_strategy():
cerebro = bt.Cerebro()
data = bt.feeds.YourDataFeed(dataname='top_quintile_nyse_stocks.csv')
cerebro.adddata(data)
cerebro.addstrategy(LongShortEarningsStrategy)
cerebro.addobserver(bt.observers.Value)
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())
if __name__ == '__main__':
run_strategy()Note that this code is just a starting point for implementing the LongShortEarningsStrategy in Backtrader. You will need to create a custom data feed (bt.feeds.YourDataFeed) that includes the required data such as earnings surprises, market data, and market capitalization. Additionally, you will need to implement the logic for calculating the 25th and 75th percentiles, as well as the value-weighting of the portfolios based on market capitalization at t-3.