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- All Subjects: Finance
- All Subjects: Computer Science
- Creators: Barrett, The Honors College
Description
For this thesis, the authors would like to create a hypothetical Private Equity Real Estate Investment firm that focuses on creating value for partners by taking an opportunistic approach to acquiring under-performing urban multi-family properties with large upside potential for investing. The project will focus on both the market analysis and financial modeling associated with investment strategy and transactions. There is a substantial amount of complexity within commercial real estate and this thesis seeks to offer an accurate and comprehensive documentary of the process, while simplifying it for everyday readers. Additionally, there are a significant amount of risk factors associated with investment decisions, so the best practices from the industry documented in this manuscript are valuable tools for successful investing in the future. To gain the most profound and reliable industry knowledge, the authors leveraged the experience of dozens of industry professionals through research and personal interviews. Through careful analysis, the authors were able to ascertain the current economic position in the real estate cycle and to create a plan for future investing. Additionally, they were able to identify and evaluate a specific asset for purchase. As a result, the authors found that multifamily properties are a sound investment for the next two years and that the company should slowly start to shift directions to office and retail in 2018.
ContributorsBacon, David (Co-author) / Soto, Justin (Co-author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Marketing (Contributor) / W. P. Carey School of Business (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Since the passage of the Federal Election Campaign Act Amendments of 1974 (FECA) up until the most recent election in 2012, presidential campaign funds have risen over five hundred percent. While money has always been an essential and critical part of any political campaign, this rise has been drastic and continues to increase at a higher rate with every election cycle, even when the numbers are adjusted for inflation. The purpose of this paper is to examine this continuous increase in cost of presidential campaigns and to analyze the different pieces that have contributed to this rise. The main pieces include two Supreme Court cases: Buckley v. Valeo and Citizens United v. Federal Elections Commission, the rise and fall of federally regulated public funding and the various pieces of a presidential campaign that have considerably higher ticket prices with each election cycle. This paper first goes through both Buckley and Citizens, describing what each Supreme Court decision did and how they effected how much money can be spent in a presidential campaign and by whom. The paper then examines each presidential election since the passage of FECA in 1974 through the last election with President Barack Obama and Mitt Romney in 2012. Each election cycle is broken down to show how much money was spent by each candidate and the Republican and Democratic National Committees, whether or not the money was received through public funds or raised privately, and subsequently the percentages of where the money was spent. While the examination of the Court cases helps to understand why so much money can be donated and contributed directly to campaigns or spent on behalf of a presidential candidate, the breakdown of where the money is spent including advertising, travel, staff salaries etc. helps to show why a presidential campaign costs over five hundred percent more today than it did forty years ago. By understanding this increase, how it was caused and where the money is going, it is more feasible to comprehend whether or not campaign finance reform should be proposed and if so, how it should be brought about.
ContributorsColby, Mikaela Nicole (Author) / Critchlow, Donald (Thesis director) / Shair-Rosenfield, Sarah (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
By the year 2021, the gift card market is expected to grow to more than 200 billion dollars. Gift cards are extremely popular among consumers and retailers. They are the most requested gift of the holiday season. They are popular for retailers because gift card sales mean more first time customers, more returning customers, and more money spent in their stores. The growth of gift cards has been very rapid since their introduction by Blockbuster Entertainment in 1994. As the gift card market has increased, so too has gift card breakage. According to FASB ASC 405-20-40-3 gift card breakage is, "the portion of the dollar value of prepaid stored-value products that ultimately is not redeemed by product holders for cash or not used to purchase goods and/or services". The average consumer may contribute to gift card breakage by not using the full dollar amount of their gift card, or by losing the gift card and therefore not redeeming it. For 2011, breakage was expected to be around two billion dollars, roughly two percent of all gift cards purchased. Gift card breakage is free money for the retail companies. They are able to recognize the breakage as revenue without having to give up merchandise or services. Recently, abandoned property laws have minimized the profits on gift card breakage for large retailers. In states where abandoned property laws include gift cards, retailers have been forced to turn over the cash from their unused gift cards. These laws are going to have an effect on many large retailers as some recognize tens of millions of dollar in gift card breakage income but will no longer be able to do so.
ContributorsClasen, Jeffrey Steven (Author) / Call, Andrew (Thesis director) / Huston, Janet (Committee member) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Company X is one of the world's largest manufacturer of semiconductors. The company relies on various suppliers in the U.S. and around the globe for its manufacturing process. The financial health of these suppliers is vital to the continuation of Company X's business without any material interruption. Therefore, it is in Company X's interest to monitor its supplier's financial performance. Company X has a supplier financial health model currently in use. Having been developed prior to watershed events like the Great Recession, the current model may not reflect the significant changes in the economic environment due to these events. Company X wants to know if there is a more accurate model for evaluating supplier health that better indicates business risk. The scope of this project will be limited to a sample of 24 suppliers representative of Company X's supplier base that are public companies. While Company X's suppliers consist of both private and public companies, the used of exclusively public companies ensures that we will have sufficient and appropriate data for the necessary analysis. The goal of this project is to discover if there is a more accurate model for evaluating the financial health of publicly traded suppliers that better indicates business risk. Analyzing this problem will require a comprehensive understanding of various financial health models available and their components. The team will study best practice and academia. This comprehension will allow us to customize a model by incorporating metrics that allows greater accuracy in evaluating supplier financial health in accordance with Company X's values.
ContributorsLi, Tong (Co-author) / Gonzalez, Alexandra (Co-author) / Park, Zoon Beom (Co-author) / Vogelsang, Meridith (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
As the IoT (Internet of Things) market continues to grow, Company X needs to find a way to penetrate the market and establish larger market share. The problem with Company X's current strategy and cost structure lies in the fact that the fastest growing portion of the IoT market is microcontrollers (MCUs). As Company X currently holds its focus in manufacturing microprocessors (MPUs), the current manufacturing strategy is not optimal for entering competitively into the MCU space. Within the MCU space, the companies that are competing the best do not utilize such high level manufacturing processes because these low cost products do not demand them. Given that the MCU market is largely untested by Company X and its products would need to be manufactured at increasingly lower costs, it runs the risk of over producing and holding obsolete inventory that is either scrapped or sold at or below cost. In order to eliminate that risk, we will explore alternative manufacturing strategies for Company X's MCU products specifically, which will allow for a more optimal cost structure and ultimately a more profitable Internet of Things Group (IoTG). The IoT MCU ecosystem does not require the high powered technology Company X is currently manufacturing and therefore, Company X loses large margins due to its unnecessary leading technology. Since cash is king, pursuing a fully external model for MCU design and manufacturing processes will generate the highest NPV for Company X. It also will increase Company X's market share, which is extremely important given that every tech company in the world is trying to get its hands into the IoT market. It is possible that in ten to thirty years down the road, Company X can manufacture enough units to keep its products in-house, but this is not feasible in the foreseeable future. For now, Company X should focus on the cost market of MCUs by driving its prices down while maintaining low costs due to the variables of COGS and R&D given in our fully external strategy.
ContributorsKadi, Bengimen (Co-author) / Peterson, Tyler (Co-author) / Langmack, Haley (Co-author) / Quintana, Vince (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / School of Accountancy (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (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 project was centered around designing a processor model (using the C programming language) based on the Coldfire computer architecture that will run on third party software known as Open Virtual Platforms. The end goal is to have a fully functional processor that can run Coldfire instructions and utilize peripheral devices in the same way as the hardware used in the embedded systems lab at ASU. This project would cut down the substantial amount of time students spend commuting to the lab. Having the processor directly at their disposal would also encourage them to spend more time outside of class learning the hardware and familiarizing themselves with development on an embedded micro-controller. The model will be accurate, fast and reliable. These aspects will be achieved through rigorous unit testing and use of the OVP platform which provides instruction accurate simulations at hundreds of MIPS (million instructions per second) for the specified model. The end product was able to accurately simulate a subset of the Coldfire instructions at very high rates.
ContributorsDunning, David Connor (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-12
Description
This paper presents the design and evaluation of a haptic interface for augmenting human-human interpersonal interactions by delivering facial expressions of an interaction partner to an individual who is blind using a visual-to-tactile mapping of facial action units and emotions. Pancake shaftless vibration motors are mounted on the back of a chair to provide vibrotactile stimulation in the context of a dyadic (one-on-one) interaction across a table. This work explores the design of spatiotemporal vibration patterns that can be used to convey the basic building blocks of facial movements according to the Facial Action Unit Coding System. A behavioral study was conducted to explore the factors that influence the naturalness of conveying affect using vibrotactile cues.
ContributorsBala, Shantanu (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Department of Psychology (Contributor)
Created2014-05
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