Original paper
Abstract
We quantitatively measure the interactions between daily consumption and the stock market. We find that daily consumption, proxied by the cyclical component of theatrical box office earnings, can significantly and positively predict stock returns for up to five days. We also demonstrate a trading strategy using our consumption measures that yield non-trivial excess returns with little risk. These findings suggest that the box office effect is an economically important factor for equities. The framework implies that daily consumption carries value-relevant public information, which leads to price reaction at a daily frequency.
Keywords:Â Predictability of stock returns, Information, Discretionary Consumption, Trading strategy, box office earnings, asset pricing implications
Trading rules
- Investment Universe: CRSP Equally-Weighted Index (or S&P500 Index as an alternative)
- Data Source: Box Office Mojo for box office earnings
- Calculate daily box office earnings change over a season average (5 movie seasons per year: Winter, Spring, Summer, Fall, Holiday)
- Define Changebox: (Total daily box office earnings on day d - Same day season’s average) / Same day season’s average
- Practical Changebox: (Total daily box office earnings on day d - Total daily box office earnings on day d-7) / Total daily box office earnings on day d-7
- Long Position: Enter at end of day d if Changebox growth > 15%
- Short Position: Enter at end of day d if Changebox growth ≤ 15%
Python code
Backtrader
import backtrader as bt
class BoxOfficeStrategy(bt.Strategy):
def __init__(self):
self.box_office = self.datas[0].close # Replace with box office data source
self.changebox = bt.indicators.SimpleMovingAverage(self.box_office, period=7)
def next(self):
current_box_office = self.box_office[0]
previous_box_office = self.box_office[-7]
changebox_growth = (current_box_office - previous_box_office) / previous_box_office
if changebox_growth > 0.15:
self.buy()
elif changebox_growth <= 0.15:
self.sell()
if __name__ == '__main__':
cerebro = bt.Cerebro()
data = bt.feeds.YahooFinanceData(dataname='SPY', fromdate='2000-01-01', todate='2021-12-31') # Replace with CRSP data source
cerebro.adddata(data)
cerebro.addstrategy(BoxOfficeStrategy)
cerebro.run()Please note that this code is a template and you will need to replace the data sources for both the box office earnings and the CRSP Equally-Weighted Index (or S&P500 Index) with the appropriate data sources. The provided code uses Yahoo Finance data for S&P500 Index, and you should replace it with the correct CRSP data source. The box office data source is also not provided and should be replaced with the correct data source from Box Office Mojo.