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  • Thumbnail for The Impact of Mass Shootings on Gun Stock Prices and Sales: Exploring Long Run Correlation
    The Impact of Mass Shootings on Gun Stock Prices and Sales: Exploring Long Run Correlation by Petersilge, Stephen

    Mass shootings have been increasing at an alarming rate in recent decades. While no economic research has currently been completed on the subject, popular literature points to a national response of increased gun sales and rising gun stock prices. By examining the entirety of mass shootings between 2000 and 2016, I explore if all mass shootings elicit a response of increased gun sales, resulting in increased gun stock prices. I examine if there are specific traits of mass shootings that evoke a national response. The results suggest that not all mass shootings induce a spike in gun sales or gun stock prices. However, there are attributes of mass shootings that draw a larger response. This study pinpoints the traits of mass shootings that cause spikes in both gun sales and gun stock prices.

  • Thumbnail for Stocks, United States, Statistics
    Stocks, United States, Statistics by Izquierdo, Emilio

    This paper explores the relationship between levels of short selling and stock prices in the U.S. securities market. The calculations are based on a dataset that contains company information from the three major domestic exchanges (NYSE, NASDAQ, and AMEX) and spans a two-year period from January 2015 to January 2017. If short sellers are the informed traders they are said to be, short interest should be a negative predictor of stock price. For the NYSE, an increase in short interest is negatively correlated with share price in the following month, but this finding does not hold at the aggregate level. Firms with increased short interest and low institutional ownership are more likely to have negative price movements in the subsequent month.

  • Thumbnail for The Distribution of Stock Price Fluctuations: An Econophysical Study
    The Distribution of Stock Price Fluctuations: An Econophysical Study by Korman, Hayley

    Many widely-used financial models assume a normal distribution of stock price returns, despite increasing evidence that this is not the case. These assumptions ignore the real chances of extreme events and fluctuations occurring, and they underestimate the risk of investing in the market. This thesis examines the distribution of daily stock price fluctuations, using decades of data from exchange-traded funds (ETFs) that encapsulate the market. Findings are that a power law model is a good fit for the distribution, after a certain minimum x value, and a lognormal distribution is a good fit up until and after that x value. Both distributions allow for the possibility of unpredictable, uncertain, significant events.