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- All Subjects: Finance
- Creators: School of Mathematical and Statistical Sciences
Description
This thesis takes the form of a market research report with the goal of analyzing the implications of the United Kingdom (UK) leaving the European Union (EU) (known as “Brexit”) on London’s office commercial real estate market. The ultimate goal of this report is to make a prediction, firmly grounded in quantitative and qualitative research conducted over the past several months, as to the direction of London’s commercial real estate market going forward (post-Brexit). Within the commercial real estate sector, this paper narrows its focus to the office segment of the London market.
Understanding the political landscape is crucial to formulating a reasonable prediction as to the future of the London market. Aside from research reports and articles, our main insights into the political direction of Brexit come from our recordings from meetings in March of 2017 with two high-ranking members of Parliament and one member of the House of Lords—all of whom are members of the Tory Party (the meetings being held under the condition of anonymity). The below analysis will be followed by a discussion of the economics of Brexit, primarily focusing on the economic risks and uncertainties which have emerged after the vote, and which currently exist today. Such risks include the UK losing its financial passporting rights, weakening GDP and currency value, the potential for a reduction in foreign direct investment (FDI), and the potential loss of the service sector in the city of London due to not being able to access the European Single Market.
The report will shift focus to analyzing three competing viewpoints of the direction of the London market based on recordings from interviews of stakeholders in the London real estate market. One being an executive of one of the largest REITs in the UK, another being the Global Head of Real Estate at a top asset management firm, and another being a director at a large property consulting firm. The report includes these differing “sub-theses” in order to try to make sense of the vast market uncertainties post-Brexit as well as to contrast their viewpoints with where the market is currently and with the report’s investment recommendation.
The remainder of the report will consist of the methods used for analyzing market trends including how the data was modeled in order to make the investment recommendation. The report will analyze real estate and market metrics pre-Brexit, immediately after the vote, post-Brexit, and will conclude with future projections encapsulating the investment recommendation.
Understanding the political landscape is crucial to formulating a reasonable prediction as to the future of the London market. Aside from research reports and articles, our main insights into the political direction of Brexit come from our recordings from meetings in March of 2017 with two high-ranking members of Parliament and one member of the House of Lords—all of whom are members of the Tory Party (the meetings being held under the condition of anonymity). The below analysis will be followed by a discussion of the economics of Brexit, primarily focusing on the economic risks and uncertainties which have emerged after the vote, and which currently exist today. Such risks include the UK losing its financial passporting rights, weakening GDP and currency value, the potential for a reduction in foreign direct investment (FDI), and the potential loss of the service sector in the city of London due to not being able to access the European Single Market.
The report will shift focus to analyzing three competing viewpoints of the direction of the London market based on recordings from interviews of stakeholders in the London real estate market. One being an executive of one of the largest REITs in the UK, another being the Global Head of Real Estate at a top asset management firm, and another being a director at a large property consulting firm. The report includes these differing “sub-theses” in order to try to make sense of the vast market uncertainties post-Brexit as well as to contrast their viewpoints with where the market is currently and with the report’s investment recommendation.
The remainder of the report will consist of the methods used for analyzing market trends including how the data was modeled in order to make the investment recommendation. The report will analyze real estate and market metrics pre-Brexit, immediately after the vote, post-Brexit, and will conclude with future projections encapsulating the investment recommendation.
ContributorsHorn, Jonathan (Co-author) / Sidi, Adam (Co-author) / Bonadurer, Werner (Thesis director) / McDaniel, Cara (Committee member) / Department of Finance (Contributor) / School of Politics and Global Studies (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The purpose of our research was to develop recommendations and/or strategies for Company A's data center group in the context of the server CPU chip industry. We used data collected from the International Data Corporation (IDC) that was provided by our team coaches, and data that is accessible on the internet. As the server CPU industry expands and transitions to cloud computing, Company A's Data Center Group will need to expand their server CPU chip product mix to meet new demands of the cloud industry and to maintain high market share. Company A boasts leading performance with their x86 server chips and 95% market segment share. The cloud industry is dominated by seven companies Company A calls "The Super 7." These seven companies include: Amazon, Google, Microsoft, Facebook, Alibaba, Tencent, and Baidu. In the long run, the growing market share of the Super 7 could give them substantial buying power over Company A, which could lead to discounts and margin compression for Company A's main growth engine. Additionally, in the long-run, the substantial growth of the Super 7 could fuel the development of their own design teams and work towards making their own server chips internally, which would be detrimental to Company A's data center revenue. We first researched the server industry and key terminology relevant to our project. We narrowed our scope by focusing most on the cloud computing aspect of the server industry. We then researched what Company A has already been doing in the context of cloud computing and what they are currently doing to address the problem. Next, using our market analysis, we identified key areas we think Company A's data center group should focus on. Using the information available to us, we developed our strategies and recommendations that we think will help Company A's Data Center Group position themselves well in an extremely fast growing cloud computing industry.
ContributorsJurgenson, Alex (Co-author) / Nguyen, Duy (Co-author) / Kolder, Sean (Co-author) / Wang, Chenxi (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Department of Management (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
A Guide to Financial Mathematics is a comprehensive and easy-to-use study guide for students studying for the one of the first actuarial exams, Exam FM. While there are many resources available to students to study for these exams, this study is free to the students and offers an approach to the material similar to that of which is presented in class at ASU. The guide is available to students and professors in the new Actuarial Science degree program offered by ASU. There are twelve chapters, including financial calculator tips, detailed notes, examples, and practice exercises. Included at the end of the guide is a list of referenced material.
ContributorsDougher, Caroline Marie (Author) / Milovanovic, Jelena (Thesis director) / Boggess, May (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
This paper provides evidence through an event study, portfolio simulation, and regression analysis that insider trading, when appropriately aggregated, has predictive power for abnormal risk-adjusted returns on some country and sector exchange traded funds (ETFs). I examine ETFs because of their broad scope and liquidity. ETF markets are relatively efficient and, thus, the effects I document are unlikely to appear in ETF markets. My evidence that aggregated insider trading predicts abnormal returns in some ETFs suggests that aggregated insider trading is likely to have predictive power for financial assets traded in less efficient markets. My analysis depends on specialized insider trading data covering 88 countries is generously provided by 2iQ.
ContributorsKerker, Mackenzie Alan (Author) / Coles, Jeffrey (Thesis director) / Mcauley, Daniel (Committee member) / Licon, Wendell (Committee member) / Barrett, The Honors College (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor)
Created2014-05
Description
In order to discover if Company X's current system of local trucking is the most efficient and cost-effective way to move freight between sites in the Western U.S., we will compare the current system to varying alternatives to see if there are potential avenues for Company X to create or implement an improved cost saving freight movement system.
ContributorsPicone, David (Co-author) / Krueger, Brandon (Co-author) / Harrison, Sarah (Co-author) / Way, Noah (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Economics Program in CLAS (Contributor) / School of Accountancy (Contributor) / W. P. Carey School of Business (Contributor) / Sandra Day O'Connor College of Law (Contributor)
Created2015-05
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
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 investigates whether measures of investor sentiment can be used to predict future total returns of the S&P 500 index. Rolling regressions and other statistical techniques are used to determine which indicators contain the most predictive information and which time horizons' returns are "easiest" to predict in a three year data set. The five "most predictive" indicators are used to predict 180 calendar day future returns of the market and simulated investment of hypothetical accounts is conducted in an independent six year data set based on the rolling regression future return predictions. Some indicators, most notably the VIX index, appear to contain predictive information which led to out-performance of the accounts that invested based on the rolling regression model's predictions.
ContributorsDundas, Matthew William (Author) / Boggess, May (Thesis director) / Budolfson, Arthur (Committee member) / Hedegaard, Esben (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor)
Created2013-12
Description
The purpose of this paper is to study the impact that poison pills have on the value of share prices after the cancellation of a transaction. While various studies have focused on the generic share price impact of poison pills, very few have focused on the impact of poison pills in cancelled transactions. Based on our research and analysis, in cancelled transactions, target firms that have poison pills prior to the transaction and target firms without poison pills generate returns above the announcement date premium and subsequent investment in the S&P 500 when held to the cancellation of the transaction and when held from cancellation to 6 months after the transaction. This analysis can contribute to the argument that holding shares of firms regardless of cancellation risk is preferable to taking profit at announcement date. Additionally, it can contribute to the study of undiscovered pricing impact of poison pills.
ContributorsChotalla, Gurkaran (Co-author) / Amjad, Hamza (Co-author) / Reddy, Samir (Co-author) / Stein, Luke (Thesis director) / Lindsey, Laura (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12