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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 the words of W. Edwards Deming, "the central problem in management and in leadership is failure to understand the information in variation." While many quality management programs propose the institution of technical training in advanced statistical methods, this paper proposes that by understanding the fundamental information behind statistical theory, and by minimizing bias and variance while fully utilizing the available information about the system at hand, one can make valuable, accurate predictions about the future. Combining this knowledge with the work of quality gurus W. E. Deming, Eliyahu Goldratt, and Dean Kashiwagi, a framework for making valuable predictions for continuous improvement is made. After this information is synthesized, it is concluded that the best way to make accurate, informative predictions about the future is to "balance the present and future," seeing the future through the lens of the present and thus minimizing bias, variance, and risk.
ContributorsSynodis, Nicholas Dahn (Author) / Kashiwagi, Dean (Thesis director, Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
The NFL is one of largest and most influential industries in the world. In America there are few companies that have a stronger hold on the American culture and create such a phenomena from year to year. In this project aimed to develop a strategy that helps an NFL team be as successful as possible by defining which positions are most important to a team's success. Data from fifteen years of NFL games was collected and information on every player in the league was analyzed. First there needed to be a benchmark which describes a team as being average and then every player in the NFL must be compared to that average. Based on properties of linear regression using ordinary least squares this project aims to define such a model that shows each position's importance. Finally, once such a model had been established then the focus turned to the NFL draft in which the goal was to find a strategy of where each position needs to be drafted so that it is most likely to give the best payoff based on the results of the regression in part one.
ContributorsBalzer, Kevin Ryan (Author) / Goegan, Brian (Thesis director) / Dassanayake, Maduranga (Committee member) / Barrett, The Honors College (Contributor) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
The concentration factor edge detection method was developed to compute the locations and values of jump discontinuities in a piecewise-analytic function from its first few Fourier series coecients. The method approximates the singular support of a piecewise smooth function using an altered Fourier conjugate partial sum. The accuracy and characteristic features of the resulting jump function approximation depends on these lters, known as concentration factors. Recent research showed that that these concentration factors could be designed using aexible iterative framework, improving upon the overall accuracy and robustness of the method, especially in the case where some Fourier data are untrustworthy or altogether missing. Hypothesis testing methods were used to determine how well the original concentration factor method could locate edges using noisy Fourier data. This thesis combines the iterative design aspect of concentration factor design and hypothesis testing by presenting a new algorithm that incorporates multiple concentration factors into one statistical test, which proves more ective at determining jump discontinuities than the previous HT methods. This thesis also examines how the quantity and location of Fourier data act the accuracy of HT methods. Numerical examples are provided.
ContributorsLubold, Shane Michael (Author) / Gelb, Anne (Thesis director) / Cochran, Doug (Committee member) / Viswanathan, Aditya (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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
Coherent vortices are ubiquitous structures in natural flows that affect mixing and transport of substances and momentum/energy. Being able to detect these coherent structures is important for pollutant mitigation, ecological conservation and many other aspects. In recent years, mathematical criteria and algorithms have been developed to extract these coherent structures in turbulent flows. In this study, we will apply these tools to extract important coherent structures and analyze their statistical properties as well as their implications on kinematics and dynamics of the flow. Such information will aide representation of small-scale nonlinear processes that large-scale models of natural processes may not be able to resolve.
ContributorsCass, Brentlee Jerry (Author) / Tang, Wenbo (Thesis director) / Kostelich, Eric (Committee member) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05