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- All Subjects: Finance
- Creators: Simonson, Mark
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
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
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
Description
The purpose of this research project is to develop a recommendation for Company X on the strategies it should use to enter a new market. This was done through the compilation and interpretation of data from the company and the construction of a financial model capable of analyzing our different proposed strategies. Company X is a leading producer of silicon chips which seeks to remain one of the leading forces in new technologies. Currently Company X wants to assess the value and risks associated with introducing a new packaging technology (FO-WLP) into their products either by developing the technology in-house or outsourcing production. The first portion of the research consisted mostly of gathering the necessary business acumen to be able to to fully understand our research findings. Market research was conducted to discover what competitors exist and what inputs should be included for the model with help from employees at Company X. The research then proceeded with the identification of three possible strategies and construction of financial models to analyze these options. Using the results from our analysis we were able to develop our recommendation for Company X and lay out the next steps which the Company needs to take before investing in the new technology.
ContributorsRubenzer, Jack (Co-author) / Galaviz, Roberto (Co-author) / Mariani, Stephanie (Co-author) / Mecinas, Freddy (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Department of Supply Chain Management (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The following thesis discusses the primary drivers of value creation in a leveraged buyout. Value creation is defined by two broad criteria: enterprise value creation and financial value creation. With enterprise value creation, the company itself may be improved, which in turn may have positive implications on the economy at large. As the analysis of enterprise value creation is outside the scope of publicly available information and data, the core focus of this thesis is financial value creation. Financial value creation is defined as the financial returns to a given private equity firm. Amongst this segment of value creation, there are roughly three primary categories responsible for generating returns: financial engineering, governance improvements, and operational improvements. The attached literature review and subsequent chapters of this thesis discuss the academic drivers of value creation and the outputs of a leveraged buyout model conducted on a public company, Schnitzer Steel, that has been determined to be an ideal candidate for a buyout.
ContributorsAlivarius, Chadwick (Author) / Simonson, Mark (Thesis director) / Stein, Luke (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This paper discusses the development of the mobile gaming industry and analyzes a mobile game acquisition to provide context to the entire market. By discussing the history and growth of the industry, I discovered that mobile gaming was a massive opportunity for companies to generate lucrative earnings. The discussion revolving around the evolution of the mobile gaming business model serves to provide context on the industry’s unique opportunities and risk factors. Candy Crush’s developer King is the main focus in this paper as they were the highest-performing public company in the market. The company is the greatest example of the mobile gaming phenomenon, experiencing rapid growth due to the success of its games, faltering in financial performance after going public, and finally becoming a subsidiary of a larger video game company that recognized King’s potential. King’s acquirer, Activision-Blizzard (ATVI), is an industry veteran of the overall video game industry that bought out King in an attempt to capitalize on the rising popularity of mobile games and to improve their strategic position in the larger video game market. The mergers & acquisitions (M&A) analysis between ATVI and King serves to determine whether or not the acquisition was an appropriately priced deal and if King represented a worthy buy. A discounted cash flows model is the basis for the analysis using a wide range of assumptions to account for the volatility of the industry. Finally, an event study and post-acquisition analysis are conducted to determine if any financial synergies were achieved in the ATVI-King acquisition. While the analyses do not offer a definitive conclusion on King’s post-acquisition performance, it can be said that the company has managed to achieve some measure of longevity. In the context of the entire mobile gaming market, the potential of mobile games should make developers attractive in the eyes of investors and acquirers, provided they understand the mobile gaming industry’s unique risks.
ContributorsDai, Yongjun (Author) / Simonson, Mark (Thesis director) / Geoffrey, Smith (Committee member) / Department of Finance (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05