Will data on internet queries predict the performance in the marketplace: an empirical study on online searches and IPO stock returns

Abstract

It has become a popular research topic how data on Internet queries are used to make reliable predictions about changes in the marketplace. In this study we analyze the relationship between the online search volume of IPO or initial public offering stocks and their post-IPO stock returns. We obtain the online search data sets of the NAVER, one of the largest online search services in Korea, and the information on IPO stocks and their post-IPO stock returns at the Korea Exchange. We investigate the daily online search volume data of 87 companies that went public in the year of 2016. After analyzing the relations of the abnormal returns and online search volumes using the event study methods, we find that the lower the amount of online search for stocks before IPO, the higher the stock returns after IPO both in short and long-term. One standard deviation increase in search volume decreases one-day return by 3 basis points, two-day return by 6 basis points, and 4-day return by 10 basis points. These economically significant results become stronger if we control for benchmark returns, firm size, and book-to-market ratio. This finding suggests that IPO stocks with low investors' attention based on the Internet search volume may be undervalued.