I aim to explain significantly different industry abnormal returns and changes in risk as a result of the 1933 Bank Holiday imposed by President D. Franklin Roosevelt from March 3, 1933 to March 15, 1933. I identify no strong relationship between unregulated industry leverage and abnormal returns following the Bank Holiday, but find regulated industries, the most highly levered at the time, experienced the most statistically significant negative abnormal returns. I find a strong correlation between abnormal returns and leverage when including regulated and unregulated industries. Thus, the evidence is consistent with the story that highly regulated industries who experienced negative abnormal returns were not able to take advantage of the benefits brought on by the Bank Holiday. The addition of historically accurate leverage data fails to fully account for a lack of significant results from Ingram (2016), who analyzed industry specific returns and risk surrounding the Bank Holiday and attempted to explain industry differences by including measures of industry leverage by using a proxy for leverage. I find that industries related to manufacturing experience positive abnormal returns following the Bank Holiday, most likely due to the ability to borrow money and finance capital, brought on by newly established financial stability.

This thesis concerns banking and financial systems in the United States of America during the time of the Great Depression. In the first stage, I will focus on the Federal Reserve System's monetary policy and its expansive character. Then I will emphasize on the events taking place in American banking system during the 20's, the linkage between these events and the great contradiction of American economy, which took place at the end of the decade. The key part of my thesis will be devoted to the analysis of the most important state interventions, which were supposed to save the banking and financial system from collapsing between 1930 -- 1933, and make the economy prosperous again. I will devote to the steps taken by Hervert Hoover; the president between years 1929 -- 1933, as well as the steps from the first year of F. D. Roosevelt's presidency. F. D. Roosevelt's era started in March 1933, and it was the time when the American banking system found itself in the most critical situation ever. During this era, the crucial measures were taken, and I will examine the hypothesis in which there were close connections between the political and the big business representatives standing behind their enactment. I will attempt to demonstrate that the officially proclaimed goals of those measures were unlike the real content, and the consequences of the new legislatives being enacted during these times.

Utilizing the industry portfolio classifications that Fama and French provide in their data library, I analyze the specific effects that the 1933 Bank Holiday has on various industries. My empirical results go beyond what Silber (2009) determines to be significantly positive abnormal market returns on March 15, 1933, which is the day after the Bank Holiday and the largest ever one-day increase in the stock market. I use the CAPM and the Fama-French 3-factor Model to find significant systematic risk decreases after the Bank Holiday in the Coal and Transportation industries, as well as systematic risk increases in Consumer Goods and Apparel. To determine the driving factors behind these changes in systematic risk and abnormal returns, I test the correlation between industry leverage ratios and differences in systematic risk changes after the Bank Holiday. The Bank Holiday helps stabilize the economy and the nation’s banking system, which I expect industries with larger debt obligations will benefit more after the Bank Holiday. Inconsistent with my expectations, I don’t find significant evidence that the systematic risks of highly leveraged industries decreases more than industries with lower leverage ratios. I develop my argument to leave room for changes in the model used to estimate systematic risks in order to identify the variables that are the true drivers of the systematic risk changes that I observe.