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- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
Elon Musk is known for making controversial tweets, which often lead to lawsuits. Our thesis focuses on analyzing the effect that these individual tweets have on stock prices. Our hypothesis focuses on the idea that when Elon Musk makes a controversial tweet, the volatility of Tesla stock will increase, while the price of Tesla stock will on average decrease. The thirteen tweets that we are examining are the tweets that we deemed to be most important, which are measured by the amount of press coverage that they have received. We also evaluated the effect that two different lawsuits that stemmed from Musk’s reckless tweets had on Tesla stock. After evaluating the effect that Elon Musk’s tweets had on the stock volume and price, we will then determine whether or not Elon Musk and other CEO’s alike should be able to tweet in a similar manner. In order to analyze stock movement, volume, and significance we imported statistical data from Yahoo Finance and Nasdaq into Excel. From there, We added charts to model the volatility and the direction of price data. Additionally, we created separate indexes to compare stock moves and test for abnormal returns. From these returns we were able to calculate the alpha and beta for Tesla, its peers and competitors. To analyze Musk’s tweets, we collected close to 7,000 tweets and ordered them chronologically in Excel. With the combination of the stock and tweet data, we were in an excellent spot to analyze the data and come to a conclusion.
ContributorsDe Roo, Gilles (Co-author) / Lueck, Elliott (Co-author) / Budolfson, Arthur (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Regulations in the financial sector of the United States have had the same purpose of protecting the economy and consumers since their modern establishment. Deregulation in the 1980’s led to an environment that allowed banks to take on high risk choices. This, among other economic circumstances, lead to the 2008 Great Recession that brought down the United States and global economies. The government was forced to act with bailouts to keep many big banks from shutting down. Some were bailed out and others failed to keep the economy stable. In June 2009, the recession was over, but the recovery process was not. To help prevent another crash, the Dodd Frank Act was passed in July 2010. The act is a long and complex legislation with the main purpose of enforcing regulations to keep banks in check to prevent another recession. The Act’s enforcement was felt immediately, forcing businesses to adapt to its regulation standards. Opinions on Dodd-Frank are mixed. Some see it serving its purpose with regulating the financial sector and others see it being a costly burden that has slowed the progress of the economy. As the economy continues to evolve, we can expect changes to the regulations on the financial sector which will continue to cause businesses to adapt, change, and modify their operations.
ContributorsCastro, Jonathan Patrick (Author) / Jordan, Erin (Thesis director) / Sadusky, Brian (Committee member) / Department of Finance (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Current methods measuring the consumption of prescription and illicit drugs are often hampered by innate limitations, the data is slow and often restricted, which can impact the relevance and robustness of the associated data. Here, wastewater-based epidemiology (WBE) was applied as an alternative metric to measure trends in the consumption of twelve narcotics within a collegiate setting from January 2018 to May 2018 at a Southwestern U.S. university. The present follow-up study was designed to identify potential changes in the consumption patterns of prescription and illicit drugs as the academic year progressed. Samples were collected from two sites that capture nearly 100% of campus-generated wastewater. Seven consecutive 24-hour composite raw wastewater samples were collected each month (n = 68) from both locations. The study identified the average consumption of select narcotics, in units of mg/day/1000 persons in the following order: cocaine (528 ± 266), heroin (404 ± 315), methylphenidate (343 ± 396), amphetamine (308 ±105), ecstasy (MDMA; 114 ± 198), oxycodone (57 ± 28), methadone (58 ± 73), and codeine (84 ± 40). The consumption of oxycodone, methadone, heroin, and cocaine were identified as statistically lower in the Spring 2018 semester compared to the Fall 2017. Universities may need to increase drug education for the fall semester to lower the consumption of drugs in that semester. Data from this research encompasses both human health and the built environment by evaluating public health through collection of municipal wastewater, allowing public health officials rapid and robust narcotic consumption data while maintaining the anonymity of the students, faculty, and staff.
ContributorsCarlson, Alyssa Rose (Author) / Halden, Rolf (Thesis director) / Gushgari, Adam (Committee member) / School of Human Evolution & Social Change (Contributor) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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 goal of this thesis project was to build an understanding of supersonic projectile dynamics through the creation of a trajectory model that incorporates several different aerodynamic concepts and builds a criteria for the stability of a projectile. This was done iteratively where the model was built from a foundation of kinematics with various aerodynamic principles being added incrementally. The primary aerodynamic principle that influenced the trajectory of the projectile was in the coefficient of drag. The drag coefficient was split into three primary components: the form drag, skin friction drag, and base pressure drag. These together made up the core of the model, additional complexity served to increase the accuracy of the model and generalize to different projectile profiles.
ContributorsBlair, Martin (Co-author) / Armenta, Francisco (Co-author) / Takahashi, Timothy (Thesis director) / Herrmann, Marcus (Committee member) / Mechanical and Aerospace Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Accessible STEAM (Science, Technology, Engineering, Art, and Mathematics) education is imperative in creating the future innovators of the world. This business proposal is for a K-8 STEAM Museum to be built in the Novus Innovation Corridor on Arizona State University (ASU)’s Tempe campus. The museum will host dynamic spaces that are constantly growing and evolving as exhibits are built by interdisciplinary capstone student groups- creating an internal capstone project pipeline. The intention of the museum is to create an interactive environment that fosters curiosity and creativity while acting as supplemental learning material to Arizona K-8 curriculum. The space intends to serve the greater Phoenix area community and will cater to underrepresented audiences through the development of accessible education rooted in equality and inclusivity.
ContributorsPeters, Abigail J (Author) / McCarville, Daniel R. (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Technology has managed to seamlessly grow into every industry fathomable without much resistance. This could be due to the fact that the majority of industries that have integrated technology have lacked insurmountable barriers which could hold back strategic innovations. Even with a wide array of industries applying technology to their framework, some haven’t managed to reach the true capability of technological advances. One industry that has both taken wide advantage of technology while also barely scraping the surface of the depth behind its potential has been politics. Electronic voting booths, targeted online marketing campaigns, and live streamed debates have been integral parts of our modern-day political environment, however, approval rating-based forecasting for elections has been an area that isn’t commonly referenced by both large political players.
In an age of information where data can be extracted just about anywhere and interpolated using extensive statistical processing, the fact that systems modeling isn’t a pillar of campaign efforts seems ludicrous. A field that is heavily dependent on pivoting concern based on lack of support would make sense to heavily depend on a modeling system that can accurately predict future points of interest.
This report aims to lay the foundation that can be built upon through providing pitfalls in potential modeling, importance of a modeling system, and a barebones skeleton model in AnyLogic with a scheme of how the model would work. I hope this report can serve political interests by providing context on which modeling can accurately provide insight.
ContributorsSchiazzano, John (Author) / McCarville, Daniel R. (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Our project examines The Blackstone Group’s $6.1 billion leveraged buyout of TeamHealth in 2016 in detail, as well as the broader implications of the transaction on the healthcare industry. The transaction was preceded by Blackstone’s initial acquisition of the company in 2005, followed by the company’s subsequent IPO in 2009. Our project first covers the history of the target company and profiles key subsidiaries, with an emphasis on the 2015 $1.6B acquisition of IPC by TeamHealth. We then detail the sources and uses of the transaction and explore Blackstone’s stated transaction rationale. We construct a base case financial model that explores Blackstone’s potential projected internal rate of return based on organic growth and potential synergies with IPC alone and without any further tuck-in acquisitions, as well as an acquisition case model that incorporates several future tuck-in acquisitions. Both cases include a detailed buildout of revenue projections, key income statement and balance sheet drivers (including an analysis of changes in healthcare economics and their impact on our revenue build), and forward-looking assumptions on various items including capital expenditures for the target company. Discounted cash flow analysis and leveraged buyout analysis outputs are detailed and discussed for both the base case and acquisition case. We examine the risks and mitigants associated with the transaction and how they may exacerbate issues in a downside case, namely leverage and public markets-related risks that may affect Blackstone’s strategy. Lastly, we investigate the impact the transaction may have on the broader industry from the patient, payor, and physician perspective.
ContributorsBamford, Maxwell Blake (Co-author) / Jha, Neil (Co-author) / Doughty, Alexander (Co-author) / Leibovit-Reiben, Zachary (Co-author) / Mindlin, Jeff (Thesis director) / Stein, Luke (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The purpose of this thesis was to create a valuation of Spotify (Ticker: SPOT) and estimate a share price for the company. Spotify is one of the largest music streaming services in the world, currently operating in 79 markets globally with a subscriber base of over 100 million people. Spotify initially offered April 3, 2018 at $132 per share and sees a huge amount of financial assets on their balance sheet due to continued investment. As a newly established high-growth company, Spotify has enjoyed a 30% average revenue growth year over year from 2014 to 2019. Although Spotify’s reach is quite large, the company is dwarfed by competitors such as Apple, Google, and Amazon in the extremely competitive music streaming industry. Within this paper, we first analyze the competitive landscape that makes up the music streaming industry. Once a baseline understanding of the music streaming industry has been reached, we turn the focus more directly onto Spotify through examining Spotify’s position within the market as well as the company’s current strategic goals and objectives. We then forecasted Spotify’s financial statements forward and created a residual income model (RIM) based on Spotify’s financial statements. As was previously stated, the purpose of this model was to arrive at a share price for Spotify that we believe accurately reflects its value and compare that with its current market trading price. After successfully accomplishing this goal, we conducted a comprehensive final analysis and offered Spotify recommendations based on the model as well and its output.
ContributorsRice, Ian (Co-author) / Nagele, Benjamin (Co-author) / Samuels, Janet (Thesis director) / Orpurt, Steven (Committee member) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The diagnosis for an attention deficit/hyperactivity disorder (ADHD) in children is heavily based on teacher or parent opinion, and not on scientific evidence. This causes children to be wrongly diagnosed with a disorder and be prescribed medicine that they do not need to be taking. This paper discusses a project that was completed for the Child Study Lab (CSL) preschool at Arizona State University (ASU), in which children’s activity within a classroom was automatically recorded using ultra-wideband technology. This project’s goal was to gather location data on the children in the CSL and analyze and assess the collected data for any patterns of behavior. The hope was that if a child’s data displayed a pattern that strayed from the norm, that this analysis could pose as a more objective way to indicate that a child may have an attention deficit problem. Fractal Dimensions and Levy Flights were researched and applied to the data analysis portion of this project.
ContributorsKjerstad, Kamryn R (Author) / Kozicki, Michael (Thesis director) / Kupfer, Anne (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05