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Informing the Investor: A Comparative Analysis of the Importance of Pre-Initial Public Offering (IPO) Information on Stock Performance
by Paul Snyder
This paper answers which available information about the company, macroeconomic and market environment, regulatory constraints, and offering before an IPO is most impactful on year-long buy-and-hold abnormal returns and how that changes across time while analyzing the IPO markets of 1999 and 2019. Data was gathered from predominantly company prospectuses and proprietary datasets to select a total of 419 IPOs across two samples and regress abnormal geometric returns against the aforementioned information using multivariate OLS regressions. There are a number of interesting findings. First, certain information or factors that act as signals of stock performance before an IPO that correlate with stock performance change across time. Second, there is evidence that companies abiding by more regulation pre-IPO tend to perform better on the stock market after the fact, particularly with the Sarbanes-Oxley and JOBS Acts. While the direction of causality is unknown, there is now a clear and quantified relationship between IPO regulation requirements and stock performance. Third, there is evidence that the IPO market has become more strong-form efficient when comparing 1999 to 2019.
Advisors: Professor Edward Tower, Professor Grace Kim | JEL Codes: G1, G12, G14
Google Search Volume Index: Predicting Returns, Volatility and Trading Volume of Tech Stocks
By Rui Xu
This paper investigates the efficacy of using Google Search Volume Index (SVI), a publicly available tool Google provides via Google Trends, to predict stock movements within the tech sector. Relative changes in weekly search volume index are recorded from April 2004 to March 2015 and correlated with weekly returns, realized volatility and trading volume of 10 actively traded tech stocks. Correlations are drawn for three different time periods, each representing a different stage of the financial business cycle, to find out how Search Volume Index correlates with stock market movements in economic recessions and booms. Google SVI is found to be significantly and positively correlated with trading volume and weekly closing price across 2004 to 2015, and positively correlated with realized volatility from 2009-2015. There exists a positive correlation between weekly stock returns and SVI for half of the stocks sampled across all 3 periods. The regression model was a better fit before and during the recession, suggesting the possibility of stronger “herding” behavior during those periods than in recent years.
Advisor: Edward Tower | JEL Codes: G1, G14, G17 | Tagged: Analysis, Information, market efficiency, Stock Returns
