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Frontier markets is a section of the business world where a lot of money could be made but is often overlooked for different reasons. A big reason is that there are a lot of unknowns about investing in these markets. With any business investments comes risk, but through proven years

Frontier markets is a section of the business world where a lot of money could be made but is often overlooked for different reasons. A big reason is that there are a lot of unknowns about investing in these markets. With any business investments comes risk, but through proven years of research and following trends a lot of that risk can become hedged. With knowledge there comes power and, in this context, with taking the time to learn about underdog markets such as frontier markets comes great investment opportunities. This thesis will look to analyze three Sub-Sahara African countries of Tanzania, Kenya, and Ghana; and will answer the questions of why to invest in frontier economies in Africa, and how investors can minimize risk and maximize returns.

ContributorsWanjiru, Ruth Grace (Author) / Ault, Joshua (Thesis director) / Babarinde, Olufemi (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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In this paper, I introduce the fake news problem and detail how it has been exacerbated<br/>through social media. I explore current practices for fake news detection using natural language<br/>processing and current benchmarks in ranking the efficacy of various language models. Using a<br/>Twitter-specific benchmark, I attempt to reproduce the scores of

In this paper, I introduce the fake news problem and detail how it has been exacerbated<br/>through social media. I explore current practices for fake news detection using natural language<br/>processing and current benchmarks in ranking the efficacy of various language models. Using a<br/>Twitter-specific benchmark, I attempt to reproduce the scores of six language models<br/>demonstrating their effectiveness in seven tweet classification tasks. I explain the successes and<br/>challenges in reproducing these results and provide analysis for the future implications of fake<br/>news research.

ContributorsChang, Ariz Bay (Author) / Liu, Huan (Thesis director) / Tahir, Anique (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

This thesis will be exploring the situation of one of the most vulnerable groups during the COVID-19 pandemic, low-income renters. As businesses and whole states were shutdown, jobs and wages were lost and the over 100 million renters in the United States, many of whom spend a significant chunk of

This thesis will be exploring the situation of one of the most vulnerable groups during the COVID-19 pandemic, low-income renters. As businesses and whole states were shutdown, jobs and wages were lost and the over 100 million renters in the United States, many of whom spend a significant chunk of their income on their rent, were forced into a precarious situation. <br/><br/>The Federal Rent Moratorium that is currently in effect bars any evictions for missed rent payments, but these are expenses that if left unpaid, are just continuously accruing. These large sums of rent payments are currently scheduled to be dropped on struggling individuals at the end of the recently extended date of June 30th, 2021. As these renters are unable to pay for their housing, landlords lose the revenue streams from their investment properties, and are in turn unable to cover the debt service on the financing they utilized to acquire the property. In turn, financial institutions can then face widespread defaults on these loans.<br/><br/>The rental property market is massive, as roughly 34% of the American population consist of renters. If left unaddressed, this situation has the potential to cause cataclysmal consequences on the economy, including mass homelessness and foreclosures of rental properties and complexes. Everyone, from the tenants to the bankers and beyond, are stakeholders in this dire situation and this paper will seek to explore the issues, desires, and potential solutions applicable to all parties involved. Beginning with the pre-pandemic outlook of the rental housing market, then examining the impact of the coronavirus and the resulting federal actions, to finally explore solutions that may prevent or mitigate this potential disaster.

ContributorsMorris, Michael H (Author) / Sadusky, Brian (Thesis director) / Licon, Wendell (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.

ContributorsLi, Vincent (Author) / Turaga, Pavan (Thesis director) / Buman, Matthew (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

The COVID-19 pandemic has and will continue to radically shift the workplace. An increasing percentage of the workforce desires flexible working options and, as such, firms are likely to require less office space going forward. Additionally, the economic downturn caused by the pandemic provides an opportunity for companies to secure

The COVID-19 pandemic has and will continue to radically shift the workplace. An increasing percentage of the workforce desires flexible working options and, as such, firms are likely to require less office space going forward. Additionally, the economic downturn caused by the pandemic provides an opportunity for companies to secure favorable rent rates on new lease agreements. This project aims to evaluate and measure Company X’s potential cost savings from terminating current leases and downsizing office space in five selected cities. Along with city-specific real estate market research and forecasts, we employ a four-stage model of Company X’s real estate negotiation process to analyze whether existing lease agreements in these cities should be renewed or terminated.

ContributorsSaker, Logan (Co-author) / Ries, Sarah (Co-author) / Hegardt, Brandon (Co-author) / Patterson, Jack (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

ContributorsMarkabawi, Jah (Co-author) / Masud, Abdullah (Co-author) / Lobo, Ian (Co-author) / Koleber, Keith (Co-author) / Yang, Yingzhen (Thesis director) / Wang, Yancheng (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a

This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.

ContributorsMasud, Abdullah Bin (Co-author) / Koleber, Keith (Co-author) / Lobo, Ian (Co-author) / Markabawi, Jah (Co-author) / Yang, Yingzhen (Thesis director) / Wang, Yancheng (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Dr. Dean Kashiwagi created a new thinking paradigm, Information Measurement Theory (IMT), which utilizes the understanding of natural laws to help individuals minimize decision-making and risk, which leads to reduced stress. In this new paradigm, any given situation can only have one unique outcome. The more information an individual has

Dr. Dean Kashiwagi created a new thinking paradigm, Information Measurement Theory (IMT), which utilizes the understanding of natural laws to help individuals minimize decision-making and risk, which leads to reduced stress. In this new paradigm, any given situation can only have one unique outcome. The more information an individual has for the given situation, the better they can predict the outcome. Using IMT can help correctly "predict the future" of any situation if given enough of the correct information. A prime example of using IMT would be: to correctly predict what a young woman will be like when she's older, simply look at the young woman's mother. In essence, if you can't fall in love with the mother, don't marry the young woman. The researchers are utilizing the concept of IMT and extrapolating it to the financial investing world. They researched different financial investing strategies and were able to come to the conclusion that a strategy utilizing IMT would yield the highest results for investors while minimizing stress. Investors using deductive logic to invest received, on average, 1300% more returns than investors who did not over a 25-year period. Where other investors made many decisions and were constantly stressed with the tribulations of the market, the investors utilizing IMT made one decision and made much more than other investors. The research confirms the stock market will continue to increase over time by looking at the history of the stock market from a birds-eye view. Throughout the existence of the stock market, there have been highs and lows, but at the end of the day, the market continues to break through new ceilings. Investing in the stock market can be a dark and scary place for the blind investor. Using the concept of IMT can eliminate that blindfold to reduce stress on investors while earning the highest financial return potential. Using the basis of IMT, the researchers predict the market will continue to increase in the future; in conclusion, the best investment strategy is to invest in blue chip stocks that have a history of past success, in order to capture secure growth with minimal risk and stress.
ContributorsBerns, Ryan (Co-author) / Ybanez, Julian (Co-author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Marketing (Contributor) / W. P. Carey School of Business (Contributor)
Created2015-05
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Description
The pharmaceutical industry is heavily regulated. This regulation results in a high number of recalls in this industry compared to other industries. The pharmaceutical industry is subject to high regulation because of the harmful effects pharmaceuticals can have on consumers. In this paper I examine the valuation effects that a

The pharmaceutical industry is heavily regulated. This regulation results in a high number of recalls in this industry compared to other industries. The pharmaceutical industry is subject to high regulation because of the harmful effects pharmaceuticals can have on consumers. In this paper I examine the valuation effects that a drug recall has on both the recalling firm and the recalling firm's rivals. I perform an event study analysis on the data. I show that there exists a statistically significant negative effect for a drug recall on the recalling firm's market value immediately surrounding the announcement. Additionally, there is a statistically significant positive effect for a drug recall on the recalling firm's rivals after the announcement.
ContributorsPaulos, Erica Marie (Author) / Hertzel, Michael (Thesis director) / Smith, Geoffrey (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
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Over the course of six months, we have worked in partnership with Arizona State University and a leading producer of semiconductor chips in the United States market (referred to as the "Company"), lending our skills in finance, statistics, model building, and external insight. We attempt to design models that hel

Over the course of six months, we have worked in partnership with Arizona State University and a leading producer of semiconductor chips in the United States market (referred to as the "Company"), lending our skills in finance, statistics, model building, and external insight. We attempt to design models that help predict how much time it takes to implement a cost-saving project. These projects had previously been considered only on the merit of cost savings, but with an added dimension of time, we hope to forecast time according to a number of variables. With such a forecast, we can then apply it to an expense project prioritization model which relates time and cost savings together, compares many different projects simultaneously, and returns a series of present value calculations over different ranges of time. The goal is twofold: assist with an accurate prediction of a project's time to implementation, and provide a basis to compare different projects based on their present values, ultimately helping to reduce the Company's manufacturing costs and improve gross margins. We believe this approach, and the research found toward this goal, is most valuable for the Company. Two coaches from the Company have provided assistance and clarified our questions when necessary throughout our research. In this paper, we begin by defining the problem, setting an objective, and establishing a checklist to monitor our progress. Next, our attention shifts to the data: making observations, trimming the dataset, framing and scoping the variables to be used for the analysis portion of the paper. Before creating a hypothesis, we perform a preliminary statistical analysis of certain individual variables to enrich our variable selection process. After the hypothesis, we run multiple linear regressions with project duration as the dependent variable. After regression analysis and a test for robustness, we shift our focus to an intuitive model based on rules of thumb. We relate these models to an expense project prioritization tool developed using Microsoft Excel software. Our deliverables to the Company come in the form of (1) a rules of thumb intuitive model and (2) an expense project prioritization tool.
ContributorsAl-Assi, Hashim (Co-author) / Chiang, Robert (Co-author) / Liu, Andrew (Co-author) / Ludwick, David (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Department of Economics (Contributor) / Department of Supply Chain Management (Contributor) / School of Accountancy (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / WPC Graduate Programs (Contributor)
Created2015-05