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Description
When making investment decisions many different indicators are taken into consideration before picking a stock/corporation to invest in (retail or institutional). Traditionally these indicators tend to be financial measures such as earnings per share, price to earnings ratio, price to book value ratio, dividend yield/payout ratio, etc. Often these indicators do not take into consideration the actual running intricacies of a company as they are simply based on historical financial statements, thus limiting an investor's decision-making ability. In this paper I analyze several companies stock performance to see if analyzing operational factors such as supply chain management before making an investment decision would have resulted in a profitable investment and thus prove as a reliable investment indicator. To do this I focused my analysis over a period of 5 years on two companies within three different industries; Fast Food, Processing, and Ecommerce. These industries were selected as the nature of their businesses require intensive supply chains thus this strategy would be most applicable to them as opposed to a software or IT company. Of the two companies selected from each respective industry one company would be listed/analyzed in Gartner's ranking of the "Annual Supply Chain Top 25" while the other company would not be. This Gartner ranking would serve as a measure of whether or not a company had a good supply chain. These companies then had their traditional financial metrics evaluated to see if supply chain analysis indirectly encapsulated some of these metrics as well. The goal of this analysis was to find if there was a strong correlation between companies listed on Gartner's rating scale and strong stock performance. If this was true this would suggest that there is a benefit to be captured by investors through using supply chain analysis as an indicator when making investment decisions.
ContributorsThompson, Tyler Thomas (Author) / Kellso, James (Thesis director) / Smith, Geoffrey (Committee member) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Exchange traded funds (ETFs) in many ways are similar to more traditional closed-end mutual funds, although thee differ in a crucial way. ETFs rely on a creation and redemption feature to achieve their functionality and this mechanism is designed to minimize the deviations that occur between the ETF’s listed price and the net asset value of the ETF’s underlying assets. However while this does cause ETF deviations to be generally lower than their mutual fund counterparts, as our paper explores this process does not eliminate these deviations completely. This article builds off an earlier paper by Engle and Sarkar (2006) that investigates these properties of premiums (discounts) of ETFs from their fair market value. And looks to see if these premia have changed in the last 10 years. Our paper then diverges from the original and takes a deeper look into the standard deviations of these premia specifically.
Our findings show that over 70% of an ETFs standard deviation of premia can be explained through a linear combination consisting of two variables: a categorical (Domestic[US], Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market indicators such as the economic freedom index and investment freedom index are insignificant predictors of an ETFs standard deviation of premia when combined with the categorical variable. These findings differ somewhat from existing literature which indicate that these factors should have a significant impact on the predictive ability of an ETFs standard deviation of premia.
Our findings show that over 70% of an ETFs standard deviation of premia can be explained through a linear combination consisting of two variables: a categorical (Domestic[US], Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market indicators such as the economic freedom index and investment freedom index are insignificant predictors of an ETFs standard deviation of premia when combined with the categorical variable. These findings differ somewhat from existing literature which indicate that these factors should have a significant impact on the predictive ability of an ETFs standard deviation of premia.
ContributorsZhang, Jingbo (Co-author, Co-author) / Henning, Thomas (Co-author) / Simonson, Mark (Thesis director) / Licon, L. Wendell (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Exchange traded funds (ETFs) in many ways are similar to more traditional closed-end mutual
funds, although thee differ in a crucial way. ETFs rely on a creation and redemption feature to
achieve their functionality and this mechanism is designed to minimize the deviations that occur
between the ETF’s listed price and the net asset value of the ETF’s underlying assets. However
while this does cause ETF deviations to be generally lower than their mutual fund counterparts,
as our paper explores this process does not eliminate these deviations completely. This article
builds off an earlier paper by Engle and Sarkar (2006) that investigates these properties of
premiums (discounts) of ETFs from their fair market value. And looks to see if these premia
have changed in the last 10 years. Our paper then diverges from the original and takes a deeper
look into the standard deviations of these premia specifically.
Our findings show that over 70% of an ETFs standard deviation of premia can be
explained through a linear combination consisting of two variables: a categorical (Domestic[US],
Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds
that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market
indicators such as the economic freedom index and investment freedom index are insignificant
predictors of an ETFs standard deviation of premia. These findings differ somewhat from
existing literature which indicate that these factors should have a significant impact on the
predictive ability of an ETFs standard deviation of premia.
funds, although thee differ in a crucial way. ETFs rely on a creation and redemption feature to
achieve their functionality and this mechanism is designed to minimize the deviations that occur
between the ETF’s listed price and the net asset value of the ETF’s underlying assets. However
while this does cause ETF deviations to be generally lower than their mutual fund counterparts,
as our paper explores this process does not eliminate these deviations completely. This article
builds off an earlier paper by Engle and Sarkar (2006) that investigates these properties of
premiums (discounts) of ETFs from their fair market value. And looks to see if these premia
have changed in the last 10 years. Our paper then diverges from the original and takes a deeper
look into the standard deviations of these premia specifically.
Our findings show that over 70% of an ETFs standard deviation of premia can be
explained through a linear combination consisting of two variables: a categorical (Domestic[US],
Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds
that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market
indicators such as the economic freedom index and investment freedom index are insignificant
predictors of an ETFs standard deviation of premia. These findings differ somewhat from
existing literature which indicate that these factors should have a significant impact on the
predictive ability of an ETFs standard deviation of premia.
ContributorsHenning, Thomas Louis (Co-author) / Zhang, Jingbo (Co-author) / Simonson, Mark (Thesis director) / Wendell, Licon (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This paper classifies private equity groups (PEGs) seeking to engage in public to private transactions (PTPs) and determines (primarily through an examination of the implied merger arbitrage spread), whether certain reputational factors associated with the private equity industry affect a firm's ability to acquire a publicly-traded company. We use a sample of 1,027 US-based take private transactions announced between January 5, 2009 and August 2, 2018, where 333 transactions consist of private-equity led take-privates, to investigate how merger arbitrage spreads, offer premiums, and deal closure are impacted based on PEG- and PTP-specific input variables. We find that the merger arbitrage spread of PEG-backed deals are 2-3% wider than strategic deals, hostile deals have a greater merger arbitrage spread, larger bid premiums widen spreads and markets accurately identify deals that will close through a narrower spread. PEG deals offer lower premiums, as well as friendly deals and larger deals. Offer premiums are 8.2% larger among deals that eventually consummate. In a logistic regression, we identified that PEG deals are less likely to close than strategic deals, however friendly deals are much more likely to close and Mega Funds are more likely to consummate deals among their PEG peers. These findings support previous research on PTP deals. The insignificance of PEG-classified variables on arbitrage spreads and premiums suggest that investors do not differentiate PEG-backed deals by PEG due to most PEGs equal ability to raise competitive financing. However, Mega Funds are more likely to close deals, and thus, we identify that merger arbitrage spreads should be narrower among this PEG classification.
ContributorsSliwicki, Austin James (Co-author) / Schifman, Eli (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Economics (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In this research paper I explore former male athletes, specifically professional football players entering local journalism. Research paired with interviews in regards to the topic explain why there are nearly 10 former NFL players in local markets where an NFL team is present, and why local journalists along with future journalists should not be worried about the number of former male athletes in local journalism. The paper also dives into the side-by-side statistics of why there is significantly more former college athletes in local journalism than former NFL players. The research focused on more than 100 television stations, revealing that 100 former or current collegiate or pro athletes are journalists for local stations where an NFL team is present. The data is solely reliant on the information that the journalists provided in their bios on the station websites. This could be seen as a possible limitation, however, the likelihood of these journalists either lying or not identifying as a former athlete is minimal due to the size of the accomplishment of actually participating in college as an athlete. The basis of my research is to figure out if former NFL players and former athletes in general are taking journalism jobs from aspiring journalists. I conclude that future journalists are not at risk of losing jobs when it comes to retired football players entering the field of local journalism. With that said, aspiring journalists need to continue to develop their social media skills to compete with athletes’ audiences on social networks.
ContributorsTotri, Anthony Matthew (Author) / Kurland, Brett (Thesis director) / Reed, Sada (Committee member) / Walter Cronkite School of Journalism & Mass Comm (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: 1. Understand if there is monetization potential in autonomous vehicle data 2. Create a financial model of what detailing the viability of AV data monetization 3. Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data.
ContributorsCarlton, Corrine (Co-author) / Clark, Rachael (Co-author) / Quintana, Alex (Co-author) / Shapiro, Brandon (Co-author) / Sigrist, Austin (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Dodd-Frank should be celebrated for its success in stabilizing the financial sector following the last financial crisis. Some of its measures have not only contained financial disaster but contributed to economic growth. These elements of Dodd-Frank have been identified as "clear wins" and include the increase of financial institutions' capital requirements, the single-point-of-entry approach to regulating financial firms, and the creation of the Consumer Financial Protection Bureau (CFPB). The single-point-of-entry strategy (SPOE), specifically, has done much to bring an end to the age of "too big to fail" institutions. By identifying firms that could expect to be aided in case of financial crisis, the SPOE approach reduces uncertainty among financial institutions. Moreover, SPOE eliminates the significant source of risk by establishing clear protocols for resolving failed financial firms. Dodd-Frank has also taken measures to better protect consumers with the creation of the CFPB. Some of the CFPB's stabilizing actions have included the removal of deceptive financial products, setting guidelines for qualified mortgages, and other regulatory safeguards on money transfers. Despite the CFPB's many triumphs, however, there is room for improvement, especially in the agency's ability to reduce regulatory redundancies in supervision and collaboration with other financial sector controllers. The significant strengths of Dodd-Frank are evident in its elements that have secured financial stability. However, it is important to also consider any potential to stifle healthy economic growth. There are several areas for legislative amendments and reforms in order to improve the performance of Dodd-Frank given its sweeping regulatory impact. Several governing redundancies now exist with the creation of new regulatory authorities. Special efforts to increase the authority of the Financial Sector Oversight Council (FSOC) and preserving the impartiality of the Office of Financial Research (OFR) are specific examples of reforms still needed to elevate the effectiveness of Dodd-Frank. In addition, Dodd-Frank could do more to clarify the Volcker Rule in order to ease banks' burden to comply with excessive oversight. Going forward, policymakers must be willing to adjust parts of Dodd-Frank that encroach too far on the private sector's ability to foster efficiency or development. In addition, identifying and monitoring areas of the legislation deemed "too soon to tell" will provide insight on the accuracy and benefit of some Dodd-Frank measures.
ContributorsConrad, Cody Lee (Author) / Sadusky, Brian (Thesis director) / Hoffman, David (Committee member) / School of Politics and Global Studies (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
My Honors Thesis is about answering a central question regarding the business of real estate: "What is the return on investment of obtaining a real estate license?" I focused my research on the monetary, time, and other value factors that affect the initial cost of securing a real estate salesperson license in the State of Arizona (costs) and the amount of money a licensed salesperson makes as a result of having a salesperson license (income). Licensees make this trade-off: the cost in terms of real dollars to obtain a license, as well as the opportunity costs associated with the time to secure, start using, and begin to earn money by way of a salesperson license. To answer the central question I conducted a survey of active licensees in order to determine the value ascribed to holding a real estate salesperson license. Through my research, I concluded that there is not a single number that can be assigned to a real estate license that indicates its value, but the data collected reveals that the return on investment has the potential to be great. Upfront costs and fees necessary to obtain a license are insignificant when the commission a licensee can then make from a single transaction is enough to cover those expenses. Therefore, based on the survey results and research into the initial costs associated with obtaining a real estate license, there appears to be sufficient data to support a positive return on investment and warrant obtaining a real estate license.
ContributorsSanders, Sarah (Author) / Stapp, Mark (Thesis director) / Koblenz, Blair (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This paper intends to analyze the National Football League (NFL) and the role stadiums play within it. The NFL, being the nation's largest professional sports league, has experienced a large amount of volatility over the past couple of decades. Teams have relocated a significant number of times and stadium projects have grown in size, cost, and frequency. Because of these observations, we chose to focus in on this particular sports league in order to answer our many questions surrounding the role of a professional sports stadium in the economics of a city. We seek to understand the economics these sports stadiums impact on the league and the cities they reside in. To do this, we compiled data of NFL franchise wins, average ticket prices, stadiums, and franchise values, while researching the stadium building process and referencing the opinions of leading sports economists across the nation. Next, we discussed the process of building a stadium, which entails the core steps of design, construction, cost, and funding. We discuss tax-exempt municipal bonds, and explain what an impact economic analysis is and how teams use them to get cities to support their projects. Moreover, we discuss the threats of relocation and how the NFL can exert pressure on stadium project decisions. Finally, we talk about the future of the NFL, with a new trend of empty stadiums and make predictions for upcoming relocation destinations. Based on these findings, we draw conclusions on the economics of sports stadiums and offer our opinion on the current state of the NFL.
ContributorsGuillen, Sergio (Co-author) / Willms, Jacob (Co-author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: Understand if there is monetization potential in autonomous vehicle data Create a financial model of what detailing the viability of AV data monetization Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data. First, in order to brainstorm how this data could be monetized, we generated potential use cases, defined probable customers of these use cases, and how the data could generate value to customers as a means to understand what the "price" of autonomous vehicle data might be. While we came up with an extensive list of potential data monetization use cases, we evaluated our list of use cases against six criteria to narrow our focus into the following five: Government, Insurance Companies, Mapping, Marketing purposes, and Freight. Based on our research, we decided to move forward with the insurance industry as a proof of concept for autonomous vehicle data monetization. Based on our modeling, we concluded there is a significant market for autonomous vehicle data monetization moving forward. Data accessibility is a key driver in how profitable a particular company and their competitors can be in this space. In order to effectively monetize this data, it would first be important to understand the method by which a company obtains access to the data in the first place. Ultimately, based on our analysis, Company X has positioned itself well to take advantage of the new trends in autonomous vehicle technology. With more strategic investments and innovation, Company X can be a key benefactor of this unprecedented space in the near future.
ContributorsShapiro, Brandon (Co-author) / Quintana, Alex (Co-author) / Sigrist, Austin (Co-author) / Clark, Rachael (Co-author) / Carlton, Corrine (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05