Measuring The Effect Of Investors' Private Information On Stock Prices: Implications For Earnings Management And Stock Liquidity

Abstract

This thesis has five chapters. Chapter 1 serves as the introduction, Chapters 2, 3, and 4 each present separate empirical studies that focus on the private information investors embed in stock prices and its impact on the real economy, and Chapter 5 concludes the research. The first article is presented in Chapter 2, where I develop a new measure for investors’ private information called the probability of informed trading with size effects (SDPIN) embedded in stock prices. Contrary to the existing measures, SDPIN considers the trade order size (volume) and the trade frequency. I advocate that the existence of private information can be captured through the trade volume. I test the validity of this new measure and examine how it relates to the stock price synchronicity, using various proxies for private information. The findings show that it is more accurate than the existing private information measures. Hence, it can help reduce information asymmetries among market players and enhance both market transparency and managers’ awareness of private information, so as to make more optimal decisions. The second article is presented in Chapter 3 and studies the interplay between financial markets and the real economy, focusing on the effect of the investors' private information on earnings management. I use two econometric models (single-level and multilevel regression models) and three private information measures: the probability of informed trading (PIN), dynamic measure for the probability of informed trading (DPIN), and dynamic measure of the probability of informed trading with size effects (SDPIN). The findings show that managers are less likely to engage in earnings manipulation when investors’ private information is higher. I examine the upward and downward earnings management and conclude that private information has a greater effect on the upward earnings management than on downward earnings management. Hence, it is concluded that firms’ managers can gain valuable insights from the analysis of the stock price movements. This finding is in alignment with the hypothesis of managerial learning, incentive channels, and the information flow from secondary markets to the economy. The third article is presented in Chapter 4, where I examine the impact of informed trading on stock liquidity in the context of high-frequency trading, relying on the probability of informed trading (DPIN) developed by Chang et al. (2014) and the probability of informed trading with size effects (SDPIN) proposed in Chapter 2 of this thesis. I analyse daily, weekly, monthly, quarterly, and yearly data from S&P 500 companies covering the period between 2018 and 2021. While the analysis with daily and weekly data reveals that informed trading enhances stock liquidity, those with monthly, quarterly, and yearly data, reveal that such an effect is not evident. Specifically, for daily and weekly data the findings show that informed trading enhances stock liquidity, whereas for monthly, quarterly, and yearly data such effects do not exist. Finally, the above findings hold during the COVID-19 period.