Matching Items (115)
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
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
The global network, serving as the backbone of the Internet and the flow of all digital information, has evolved since the birth of the internet in 1983 1. From the first generation of networks that supported the first cell phones, the system has matured to the existing fourth generation (4G), providing greater speed and expanded capabilities beyond basic calling and texting. The goal of our project was to identify how well Company X is positioned to capitalize on the transition to 5G, which will involve almost twice the amount of interconnected devices than today. To do this, we completed the following various tasks: 1. Identify the networking segments to focus our research. 2. Identify the major customers in these segments and how much of the market share each occupies. 3. Analyze the major benchmarks and metrics these customers value when purchasing the microprocessors for their networking devices. 4. Identify Company X's major competitors and their comparable products. 5. Compare the benchmark performances of Company X's offerings and its competitors 6. Create a model that provides a 5-year NPV as well as Company X's market share 7. Employ this model to evaluate various pricing strategies. 8. Determine the optimal pricing strategy and make a final recommendation. As 5G evolves, the demand for high-quality, low-power networking devices (e.g. routers, switches, access points) will increase. After performing our analyses, we will be able to decide how Company X's networking-oriented SoCs (chipset) compare to those of the other major competitors and use this relative performance to determine an appropriate ASP (average selling price) for these SoCs.
ContributorsDimitroff, Alex (Co-author) / Arias, Stephen (Co-author) / Masson, Taylor (Co-author) / McCall, Kyle (Co-author) / Hardy, Sebastian (Co-author) / Simonson, Mark (Thesis director) / Haller, Marcie (Committee member) / Department of Finance (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
Description
Company X has developed minicomputing products that can change the way people think about minicomputer. The Product A (PRODUCT A) and Product B are relatively new products on the market that have the ability to change the way some industries use technology and increase end-user convenience. The key issue for Company X is finding targeted use cases to which Company X can market these products and increase sales. This thesis reports how our team has researched, calculated, and financially forecasted use cases for both the PRODUCT A and Product B. The Education and Healthcare industries were identified as those providing significant potential value propositions and an array of potential use cases from which we could choose to evaluate. Key competitors, market dynamics, and information obtained through interviews with a Product Line Analyst were used to size the available, obtainable, and attainable market numbers for Company X. The models built for this research provided insight into the PRODUCT A and Product B's potential growth in the education and healthcare industries. This led to the selection of education and healthcare use cases for the Product B and the PRODUCT A use cases for healthcare. This report concludes with recommendations for success in education and healthcare with the PRODUCT A and Product B.
ContributorsHoward, James (Co-author) / Kazmi, Abbas (Co-author) / Ralston, Nicholas (Co-author) / Salamatin, Mikkaela Alexis (Co-author) / Simonson, Mark (Thesis director) / Hopkins, David (Committee member) / W.P. Carey School of Business (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
It is evident that this generation of young people are buried in debt, both during college and the years after graduating. There is lots of talk about the problem, but not much talk about the solution. I created a video blog (vlog) that is about personal finances targeted at college students and recent college graduates. The videos cover different topics in each video, on things from saving to investing to budgeting and others. I tried to make it both funny and informative, so that it would not be boring to watch and each video would be easy to learn from. I made 10 videos in total, putting out one video per week. The blog can be found at : wherethisdoughgo.blogspot.com
ContributorsStaley, Matthew Michael Gallagher (Author) / Budolfson, Arthur (Thesis director) / Simonson, Mark (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Department of Information Systems (Contributor)
Created2014-05
Description
I will determine what the Average Handle Time goal should be for the company and establish why the Average Handle Time has increased over time. I am looking at data compiled from July 1st 2012 to June 30th 2013 in order to assist the company so it can budget resources more effectively in the future. For this project, there are a few questions I aim to answer. The questions are as followed: 1. What should the budgeted Average Handle Time be for the company in future time periods? 2. What are some improvements that can be made in order to manage Average Handle Time in the future time periods? Employees in the department spend their time answering client inquires over the phone, these questions are vital for the business to answer. Average Handle Time is the average duration of time it takes for the employee to answer questions from clients on each phone call. The company budgets the department based on how many calls they have answered in prior periods as well as the Average Handle Time goal for the entire department. They use this information for scheduling as well as hiring purposes to predict the number of employees needed to answer phone calls in future periods. I will attempt to answer these questions by using multiple data sources. I will gather information from multiple reports generated from the department software and data mine in order to answer some of the questions related to the project. I will also gain management feedback in terms of what is viewed as important as well as what they believe can be used to improve the Average Handle Time number in the future. These quantitative and qualitative measures will help narrow down on what the Average Handle Time should be budgeted for the department as well as what management could do to improve it in future periods.
ContributorsKing, Dylan Michael Hiroshi (Author) / Simonson, Mark (Thesis director) / Luna, David (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / W. P. Carey School of Business (Contributor)
Created2014-05
Description
The object of the present study is to examine methods in which the company can optimize their costs on third-party suppliers whom oversee other third-party trade labor. The third parties in scope of this study are suspected to overstaff their workforce, thus overcharging the company. We will introduce a complex spreadsheet model that will propose a proper project staffing level based on key qualitative variables and statistics. Using the model outputs, the Thesis team proposes a headcount solution for the company and problem areas to focus on, going forward. All sources of information come from company proprietary and confidential documents.
ContributorsLoo, Andrew (Co-author) / Brennan, Michael (Co-author) / Sheiner, Alexander (Co-author) / Hertzel, Michael (Thesis director) / Simonson, Mark (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / WPC Graduate Programs (Contributor) / School of Accountancy (Contributor)
Created2014-05