How does information, dispersed among diverse geographic markets, customer segments and employees, get incorporated into the stock price? I have endeavored to create novel datasets that incorporate information from thousands of disaggregated investors, employees and customers. I utilize these datasets to construct aggregate measures of information and study how the information measures affect future returns and corporate events.
Specifically in the first chapter titled "Information Aggregation and Asset Prices", I utilize a unique data set based on Google Trends to construct a search index and use it to proxy for the information seeking behavior of retail investors. I find that abnormal search index predicts future buying pressure on the stock of a company. The portfolio with the highest increase in the search index has positive and significant alphas. The search index also predicts earnings surprises and is associated with the pre-earnings announcement drift. My results are robust to alternative specifications of CAR windows, past returns, news coverage, information available to investors prior to the release of earnings numbers, and the information environment surrounding the earnings announcements. Overall, my results are in line with the hypothesis that retail investors' trades have information content relevant to stock prices.
In the second chapter titled "Effect of Employee Satisfaction on Earnings Surprises", I use a unique data set drawn from self administered employee surveys for 1495 US public corporations. I construct an Employee Satisfaction Index (ESI) and use it as a proxy for employee satisfaction. I find that ESI is higher for larger firms, high market to book ratio firms and firms that have low leverage. I also look at the effect of the changes in ESI on quarterly earnings announcements. I find that the changes in ESI are positively and (weakly) significantly related to the future quarterly earnings surprises. Moreover, the effect is stronger for companies that have higher information asymmetries and are more human capital dependent. The results are consistent with the theories that state that employees are insiders in a company and have information relevant to the future corporate performance. Moreover, consistent with human-capital centric theories I find evidence that the change in employee satisfaction has a greater effect on the performance of human-capital dependent companies.
In the final chapter titled "Private Equity Ownership and the Performance of Reverse Leveraged Buyouts", I study the effect of private equity exit on the target firm performance. Using a hand collected sample of 133 reverse leveraged buyout firms from 1997-2002, I examine the financial performance of the firms immediately before the IPO and up to four years after the IPO. I find that for three years after the IPO they continue to outperform their industries. However, performance deteriorates after the IPO. Cross-sectional regression at time of the IPO suggests that long term performance after the IPO is related to changes in ownership by the private equity sponsors and is not related to changes in ownership by other insiders (all officers or directors who are not PE sponsors) or change in leverage. Even after the IPO, I find the positive relation between PE sponsor ownership and future performance continues. To establish causality between PE sponsor ownership and future performance I use the 2SLS-IV approach. The identifying instrument is the number of years since LBO and is a proxy for impatience of PE sponsors to free up their capital. I find that an IV regression finds a weakly significant relation between PE sponsor ownership and future performance.