The investment universe consists of 330 US stocks in various industries. Data concerning stock returns and prices come from CRSP, and financial statements data come from Compustat. Construct three sales proxies: consumers’ intention to visit a retail shop (IN-STORE), the number of visits to the firm’s website (WEB), and an interest in product brand names (BRAND).
ASSET CLASS: stocks | REGION: United States | FREQUENCY:
Monthly | MARKET: equities | KEYWORD: Predicting Performance, Consumer Big Data
I. STRATEGY IN A NUTSHELL
The strategy trades 330 US stocks using consumer data—web visits, in-store activity, and brand interest—to form long-short quintile portfolios. The top quintile is held long, the bottom quintile short, and positions are equally weighted across three proxies.
II. ECONOMIC RATIONALE
Alternative consumer data provides an edge over traditional information sources. Web activity predicts earnings surprises, while brand and in-store data forecast revenue growth, allowing the strategy to capture predictable stock returns efficiently.
III. SOURCE PAPER
Predicting Performance Using Consumer Big Data [Click to Open PDF]
Kenneth Froot, Emeritus at Harvard University, Graduate School of Business; Namho Kang, Finance at Bentley University; Gideon Ozik, EDHEC Business School; Ronnie Sadka, Carroll School of Management, Boston College
<Abstract>
To predict firms’ fundamentals, the authors construct three proxies for real-time corporate sales from fully distinct information sources: In-store foot traffic (IN-STORE), web traffic to companies’ websites (WEB), and consumers’ interest level in corporate brands and products (BRAND). The authors demonstrate that trading using these proxies, estimated for a sample of 330 firms over 2009–2020, result in significant net-of-transaction-costs profitability. During the pandemic, WEB activity increases significantly while there is remarkable decrease in IN-STORE, reflecting the migration of consumers from physical stores toward online. The results suggest that the information contained in IN-STORE and BRAND is not immediately available to investors, while the WEB information is diffused more quickly, and that overall information diffusion worsened during the pandemic.
IV. BACKTEST PERFORMANCE
| Annualised Return | 9.29% |
| Volatility | 14.29% |
| Beta | N/A |
| Sharpe Ratio | 0.62 |
| Sortino Ratio | N/A |
| Maximum Drawdown | N/A |
| Win Rate | N/A |
