Summary:
E-commerce has become an important component and driving force of China's economic development. As a representative of the Internet economy, online sales show great potential. Against the background of the booming digital economy, this paper studies the value of online sales to predict future returns, using the sales data of the e-commerce platforms of listed companies. Why do online sales predict expected returns？ First, online sales data provide incremental information to financial reports. With the rapid development of Internet technology, online sales have become an increasingly important business model. As the proportion of companies' online sales continues to grow, online sales are increasingly relevant to the companies' overall revenue. Online sales information is also instantly available. Therefore, it provides more timely and granular information on company operating performance than traditional financial statement data. Second, investors are not fully aware of the content of online sales information. According to the theory of limited investor attention, investors have limited time and energy, and they may not fully understand all of the available information in a timely manner, which creates a temporary pricing bias. The A-share market has a large proportion of individual investors and an intense, speculative atmosphere in which investors mainly pursue short-term interests and lack the non-financial information that reflects a firm's fundamentals. Because online sales information is costly, this information may not be fully acquired and understood by investors in the A-share market. Using the online sales data of 275 companies from January 2015 to April 2020, this paper shows that online sales data predicts future returns. Hedge portfolios based on online sales growth earn a 1.27% monthly excess return. The three-factor and five-factor adjusted returns are 1.40% and 1.35%, respectively. The Fama and MacBeth (1973) regression results show that online sales growth is positively correlated with future stock returns when we control for other related factors. To further illustrate the value of online sales, we investigate the predictability of online sales over a longer period. The results show that online sales predict stock returns for the next two months. The cross-sectional analyses show that the predictability of online sales is more pronounced for firms with limited investors' attention, a higher proportion of online sales and higher arbitrage cost. Furthermore, we find that the investment value of online sales stems from their ability to predict future firm fundamentals. Further analysis shows that combining online sales and operating revenue may earn higher expected returns. In the robustness test, the predictability of online sales is still significant when we consider the potential impact of management forecasts. Our study builds on and contributes to two strands of the literature. First, from the perspective of online sales, this paper shows the informational content of non-financial data, and it is supplemental to traditional financial statement information. Research has documented the investment value of non-financial information, but no study has examined the ability of online sales to predict stock returns. This paper expands the literature by investigating online sales data, which is an important form of non-financial information. Second, our study enriches the investing strategy research by showing that online sales information has investment value and that investors may obtain abnormal returns by constructing hedge portfolios based on online sales. From the theory perspective, the market anomaly based on online sales information challenges the efficient market theory. We take advantage of the unique big data on online sales in China. Our results have important practical value for improving the efficiency of the Chinese stock market and protecting minority investors.
Amir, E., and B. Lev. 1996. “Value-relevance of Nonfinancial Information: The Wireless Communications Industry”, Journal of Accounting and Economics, 22(1-3): 3～30.
[15]
Barber, B. M., and M. N. Darrough. 1996. “Product Reliability and Firm Value: The Experience of American and Japanese Automakers, 1973-1992”, Journal of Political Economy, 105(5):1084～1099.
[16]
Babenko, I., and R. Sen. 2016. “Do Nonexecutive Employees Have Valuable Information？ Evidence from Employee Stock Purchase Plans”, Management Science, 62(7): 1843～2147.
[17]
Beneish, M., C. M. Lee, and D. Nichols. 2015. “In Short Supply: Short-Sellers and Stock Returns”, Journal of Accounting and Economics, 60(2-3): 33～57.
[18]
Bushman, R. M., A. J. Smith. 2001. “Financial Accounting Information and Corporate Governance”, Journal of Accounting and Economics, 32(1): 237～333.
[19]
Carhart, M. M. 1997. “On Persistence in Mutual Fund Performance”, The Journal of finance, 52(1): 57～82.
[20]
Chen, H., P. De, Y. J. Hu, and B. Hwang. 2014. “Wisdom of Crowds: The Value of Stock Opinions Transmitted through Social Media”, The Review of Financial Studies, 27(5): 1367～1403.
[21]
Edmans, A. 2011. “Does the Stock Market Fully Value Intangibles？ Employee Satisfaction and Equity Prices”, Journal of Financial Economics, 101(3): 621～640.
[22]
Fama, E. F., and K. R. French. 1993. “Common Risk Factors in the Returns on Stocks and Bonds”, Journal of Financial Economics, 33:3～56.
[23]
Fama, E. F., and J. D. MacBeth. 1973. “Risk, Return, and Equilibrium: Empirical Tests”, Journal of Political Economy, 81(3): 607~636.
[24]
Fama, E. F., and K. R. French. 2015. “A Five-factor Asset Pricing Model”, Journal of Financial Economics, 116(1): 1～22.
[25]
Fang, L., and J. Peress. 2009. “Media Coverage and the Cross-section of Stock Returns”, The Journal of Finance, 64(5): 2023～2052.
[26]
Green, T. C., R. Huang, Q. Wen, and D. Zhou. 2019. “Crowdsourced Employer Reviews and Stock Returns”, Journal of Financial Economics, 134(1): 236～251.
[27]
Gul, F. A., J. Kim, and A. A. Qiu. 2010. “Ownership Concentration, Foreign Shareholding, Audit Quality and Stock Price Synchronicity: Evidence from China”, Journal of Financial Economics, 95(3): 425～442.
[28]
Herskovic, B., B. Kelly, H. Lustig. 2016. “The Common Factor in Idiosyncratic Volatility: Quantitative Asset Pricing Implications”, Journal of Financial Economics, 119(2): 249～283.
[29]
Hirshleifer, D., S. H. Teoh, and J. Yu. 2011. “Short Arbitrage, Return Asymmetry, and the Accrual Anomaly”, The Review of Financial Studies, 24(7): 2429～2461.
[30]
Hou, K., M. A. V. Dijk, and Y. Zhang. 2012. “The Implied Cost of Capital: A New Approach”, Journal of Accounting and Economics, 53(3): 504～526.
[31]
Huang, J. 2018. “The Customer Knows Best: The Investment Value of Consumer Opinions”, Journal of Financial Economics, 128(1): 164～182.
[32]
Hutton, A. P., A. J. Marcus, and H. Tehranian. 2009. “Opaque Financial Reports, R2, and Crash Risk”, Journal of Financial Economics, 94: 67～86.
[33]
Jegadeesh, N., and S. Titman. 1993. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”, The Journal of Finance, 48(1): 65～91.
[34]
Jiang, G., C. M. C. Lee, and H. Yue. 2010. “Tunneling Through Intercorporate Loans: The China Experience”, Journal of Financial Economics”, 98(1): 1～20.
[35]
Lee C. M. C., S. T. Sun, R. Wang, and R. Zhang. 2019. “Technological Links and Predictable Returns”, Journal of Financial Economics, 132(3): 76～96.
[36]
Lee, C. M. C., and B. Swaminathan. 2000. “Price Momentum and Trading Volume”, The Journal of Finance, 55(5): 2017～2069.
[37]
McLean, R. D., and J. Pontiff. 2016. “Does Academic Research Destroy Stock Return Predictability？”, The Journal of Finance, 71(1): 5～32.
[38]
Wurgler, J., and E. Zhuravskaya. 2002. “Does Arbitrage Flatten Demand Curves for Stocks？”, The Journal of Business, 75(4): 583～608.
