Matching Items (52)
Filtering by
- All Subjects: Finance
- Creators: Simonson, Mark
Description
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
Description
There is a growing demand for discrete graphics processing units (dGPU) in the internet of things. Our subject company, Company X, has decided to develop a dGPU to be used in client computing (desktops, laptops, etc). This project will address whether or not company X should invest time and money into adopting their existing client focused dGPU for applications in IoT such as digital signage, gaming, or medical imaging. If this investment is to be made, we will also make specific recommendations about how Company X should enter the IoT space. The project will be completed in 3 stages. The first stage will consist of an analysis of the competitive landscape and research on dGPUs and how they differ from integrated GPUs. Stage two will focus primarily on the IoT space and how the competitors are using dGPUs in the IoT along with an analysis of three potential use cases for Company X’s dGPU. Finally, we will build a comprehensive financial model based on our research of one specific IoT segment where Company X could potentially enter. Based on these stages, we will then offer a conclusion and recommendation on whether Company X should invest in this project.
ContributorsNickel, Jack Peter (Co-author) / Bergauer, Kevin (Co-author) / Morey, Jake (Co-author) / Nickel, Jack (Co-author) / Sethia, Priyanka (Co-author) / Smith, Jesse (Co-author) / Simonson, Mark (Thesis director) / Kreutner, Caleb (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Opening a business is often an exciting time in one’s life, as they take their business idea into the marketplace. But, most individuals fail to adequately address whether their business can actually succeed before entering the marketplace. The thesis, Creating a Successful Gluten-Free Bakery: A Financial Model and Analysis analyzes whether or not a gluten-free bakery is a viable business to open in today’s marketplace. By costing the main financial variables, creating a financial model of a gluten-free bakery, and running scenario analysis, I was able to find whether or not opening a gluten-free bakery was a viable business in today’s marketplace.
ContributorsDantonio, Adam (Author) / Simonson, Mark (Thesis director) / Arthur, Budolfson (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
This thesis discusses the case for Company X to improve its vast supply chain by implementing an artificial intelligence solution in the management of its spare parts inventory for manufacturing-related machinery. Currently, the company utilizes an inventory management system, based on previously set minimum and maximum thresholds, that doesn’t use predictive analytics to stock required spares inventory. This results in unnecessary costs and redundancies within the supply chain resulting in the stockout of spare parts required to repair machinery. Our research aimed to quantify the cost of these stockouts, and ultimately propose a solution to mitigate them. Through discussion with Company X, our findings led us to recommend the use of Artificial Intelligence (A.I.) within the inventory management system to better predict when stockouts would occur. As a result of data availability, our analysis began on a smaller scale, considering only a single manufacturing site at Company X. Later, our findings were extrapolated across all manufacturing sites. The analysis includes the cost of stockouts, the capital that would be saved with A.I. implementation, costs to implement this new A.I. software, and the final net present value (NPV) that Company X could expect in 10 years and 25 years. The NPV calculations explored two scenarios, an external partnership and the purchase of a small private company, that lead to our final recommendations regarding the implementation of an A.I. software solution in Company X’s spares inventory management system. Following the analysis, a qualitative discussion of the potential risks and market opportunities associated with the explored implementation scenarios further guided the determination of our final recommendations.
ContributorsHolohan, Joseph Michael Houston (Co-author) / Shahriari, Rosie (Co-author) / Aun, Jose (Co-author) / Heineke, Christopher (Co-author) / Gurrola, Macario (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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
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
Abstract: Handling the multiple functions of monetary policy that protect the U.S. economy not only on a short term, but also long-term scale is a complicated responsibility assigned to Federal Reserve, in which their actions present a profound impact on consumer confidence towards financial markets and global economies. Specifically, one of the most important goals of the Federal Reserve is to mitigate the risk of the United States to enter a recession, while maintaining a balanced approach when making those policy decisions. In this thesis, we focus on the monetary policy of the Federal Reserve, particularly, their role in controlling interest rates to prevent recessionary sentiment in the current state of the economy. Since 2008, markets have been stronger and previous policies like Dodd-Frank have ensured that market collapses during the Great Recession do not repeat itself. Yet, fluctuations in the yield curve, polarizing investment views, and unsettled consumer confidence has pointed to another recession in the near future. In this case, we will look at the way the Fed has implemented short term policies to lower this risk in order to fight volatile markets, however, fluctuating interest rates has its consequences. The goal of this thesis is to analyze the various ways the Fed has managed interest rates in the past and present, and further, to offer a framework to serve as the most effective policy to combat volatility and recessionary sentiment in the U.S. economy.
ContributorsPatel, Dylan (Author) / Sacks, Jana (Thesis director) / Simonson, Mark (Committee member) / Economics Program in CLAS (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05