Theses and Dissertations at Montana State University (MSU)
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/732
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Item Investing in a president(Montana State University - Bozeman, College of Agriculture, 2021) AlSaad, Faisal Khalid; Chairperson, Graduate Committee: Joseph AtwoodThis paper examined the 2012 presidential election between Barack Obama and Mitt Romney on the stock market. Presidential elections pose a political uncertainty that can be hedged using the stock market. The paper constructs three portfolios using three different weighting methods: equally weighted, market capital, and individuals' donations. This study uses Fama and French 5-Factor model to estimate the annual return for Obama's and Romney's portfolios. The results show that Obama's portfolio generates an annual expected return of 11.8%, 35.6%, and 39.5% for equally weighted, market capital and donation, respectively. The results also show that Romney's portfolio generates annual expected returns of 5%, 26.2%, and - 0.8% for equally weighted, market capital, and donation, respectively. Investors can adjust their investment portfolio position by observing the candidates' probability of winning the election. This paper establishes a stock market pattern before presidential elections that investors can capitalize on to ensure against the effects of political uncertainty upon the value of their investment portfolio.Item Hierarchical fuzzy spectral clustering in campaign finance social networks(Montana State University - Bozeman, College of Engineering, 2021) Wahl, Scott Allen; Chairperson, Graduate Committee: John SheppardCommunity detection in networks is an important tool in understanding complex systems. Finding these communities in complex real-world systems is important in many disciplines, such as computer science, sociology, biology, and others. In this research, we develop an algorithm for performing hierarchical fuzzy spectral clustering. The clustering algorithm is applied to small benchmark problems, as well as a large real-world campaign finance network. Afterwards, we extend the hierarchical fuzzy spectral clustering for use in evolving networks. The discovered communities are tracked through the evolving network and their underlying properties analyzed. Third, we apply association rule mining on community-based partitions of the data. A comparison of the results within and between communities show the effectiveness of this method for adding interpretability to the underlying system. Fourth, we examine the ability of hierarchical fuzzy spectral clustering on a graph to predict behavior that is not present in the graph itself. The results are shown to be effective in predicting votes in the United States legislature based on the campaign finance networks. Finally, we develop an orthogonal spectral autoencoder that is used to perform graph embedding. This approximation model avoids the eigenvector decomposition of the full network, as well as allows out-of-sample spectral clustering. The results show the embedding performs comparably to the full spectral clustering.