News Release

Daily movie theatre ticket sales can predict stock market returns

Box office earnings create upward pressure on stock prices for at least five days

Peer-Reviewed Publication

University of Waterloo

Daily box office earnings can accurately predict stock market returns, according to a new study.

Traditionally quarterly and monthly consumption data is used to predict stock market performance. But using box office earnings – a measure that captures consumption on a more frequent basis – offers more timely and relevant data for decision-makers in the financial markets.

“U.S. box office earnings carry value relevant information for investors. We show that box office earnings can predict returns up to five days,” said Seda Oz, a professor at the University of Waterloo’s School of Accounting and Finance. “Our research provides evidence that daily consumption carries new, timelier and relevant information for stock markets.”

Oz and co-author Steve Fortin, a professor at Waterloo’s School of Accounting and Finance, test the hypothesis that a daily consumption variable – box office earnings – has implications for the stock market. Based on data available from 1997 to 2019, the authors found that daily measures predict future aggregate stock market returns significantly and positively for up to six days using the Center for Research in Security Prices value-weighted market excess return and for up to five days using the Standard and Poor’s 500 market return. The results demonstrate that box office earnings create upward pressure on stock prices for at least up to five days.

“We interpret box office earnings as early signs of spending,” said Oz. “Our evidence may represent a profitable market timing strategy that investors could potentially exploit.”

The link between money spent on movie theatre tickets and market returns suggests that box office earnings capture consumption among investors and can be used to create potentially profitable trading strategies. This is the first study exploiting daily consumption data in a stock market context. 

The study authored by Oz and Fortin was recently published in the journal Financial Management.

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