The U.S. National Market System (NMS), the largest marketplace in the world for securities and exchange traded funds, suffers from geographic market fragmentation which leads to reduced market efficiency. Communication lines transmit price updates and other information between geographically isolated exchanges at varying speeds, bounded above by the speed of light. Market participants have access to federally mandated information provided by the Securities Information Processor (SIP) and privately offered information provided by the exchanges, often called direct feeds. These feeds are quantitatively and qualitatively distinct, with the direct feeds tending to provide more information at a faster rate than the SIP feed. Differences between the SIP and direct feeds can lead to information asymmetries between market participants, which in turn create arbitrage opportunities. Under the market conditions of the NMS in 2016, these arbitrage opportunities occur regularly and many can be captured by market participants with fast connectivity. Several methods exist which allow market participants to reduce their communication latency with trading centers, including the practice of co-location where market participants pay to have their trading infrastructure located in the same building as the matching engines of an exchange. Such regularly occurring and executable arbitrage opportunities run counter to the Efficient-Market Hypothesis (EMH) in all forms, where even the weak form of the EMH claims that market participants should not be able to systematically profit from market inefficiencies. This thesis investigates the market inefficiencies and related effects introduced by geographic market fragmentation in two baskets of stocks: the Dow Jones Industrial Average (Dow), and the 30 largest stocks by market capitalization in the Standard \& Poor's 500 index (S&P 30).

Thesis advisor: Peter Ireland / It is not unreasonable to surmise that public sentiment views stock market behavior as an indicator of economic health. Historically, movements in the the stock market indeed correspond to business cycles, but this is not always the case, and the COVID-19 pandemic serves as a distinct case to highlight such an irregularity. The contrast between the behavior of the stock market and that of the economy during the pandemic compels an analysis of the pandemic's actual impact on the stock market, and this paper finds a negative and significant relationship between the interpolated daily closing prices of the S&P 500 and the daily number of COVID-19 cases. / Thesis (BA) — Boston College, 2023. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Economics.

The global economy has been severely impacted during the Covid-19 period. The U.S. stock market has also experienced greater volatility. Based on data from January 2020 to June 2021, this paper studies the volatility of daily returns on the stock market in the United States. The Standard and Poor's 500 (SPX) index and eight companies traded on major exchanges such as the New York Stock Exchange and the Nasdaq are used to calculate volatility. Combining the statistical analysis methods GARCH, GARCH-M, and TARCH, the time series of each security is modeled. It is demonstrated that the conditional heteroskedasticity of stock returns depends not only on the observed historical volatility (ARCH term) but also on the conditional heteroskedasticity of prior periods (GARCH term). As expected for financial markets, the COVID-19 outbreak increased the volatility of U.S. stock market returns. After the COVID-19 outbreak, the volatility of the U.S. stock market rose dramatically. It reached an extremely high level for the first quarter of 2020 and continued to move downwards in the following quarters. The significant heteroskedasticity in the return volatility indicates that external variables significantly affect the stock. Furthermore, this study combines the Capital Asset Pricing Model (CAPM) and the research of Engle et al. (1987), which provides a way to quantify the liquidity premium. However, with the results of the GARCH-M model, this study does not find a significant liquidity premium over time. Additionally, The TARCH model reveals a significant asymmetry in stock market returns during this epidemic, suggesting that negative news has a more substantial impact on U.S. financial markets. For investors and financial institutions, this research helps identify potential volatility in the face of similar risk events. It is helpful for investors to comprehensively consider various factors when investing in special periods or consider other investment portfolios to reduce investment risks in specific periods based on research results.

This paper is a study on how U.S stock market reacts on sovereign credit rating announcements, and if there is a significant difference between low or high debt firms. We have used an event study based on historical stock prices from 30 companies, 15 with high debt and 15 with low debt. All companies are taken from the S&P`s 500 index which we also use as a market index. We use a regression model with 10 % significance level to see if there is a significant impact on high debt firms. Our result shows that the market will be affected by the downgrade. We also conclude that there was a significant negative impact on the high debt firms.