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- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Status: Published
Description
iWaandr is a travel platform that allows users to find and share unique experiences. It will be a website that users can find on the internet. Every user will be able to post their own experiences on the platform along with a description, important information, and a rating.
The problem we are trying to solve is that it still takes hours to search for and find unique non-touristy experiences around the world. At a time when people can use their smartphones to have a car show up to their doorstep in minutes, it is unacceptable that it still takes hours to find an non-touristy experience on the internet.
Our value proposition is that users will be able to be anywhere in the world and be able to find an authentic, non-touristy experience that interests them. iWaandr is the most complete experience discovery tool, providing the largest collection of unique and personal experiences around the world.
Our competition is the large incumbent travel and review companies like TripAdvisor and Airbnb. There are also less established competitors that see a similar gap in the market like Mapify and Cool Cousin. We also have niche competitors that are only focused on outdoor activities like AllTrails and Outbound Collective. Google and blogs would also be competitors because people search on Google for unique experiences.
Our innovation is that we are focusing on creating unique content while our competitors are focusing on new ways to display the same content. Our advantage isn’t in a feature we created because a company with more resources could easily copy it. In order to create unique and useful content, we had to figure out a way for users to intuitively and easily post an experience with as much relevant information as possible. This involved a lot of thought into our posting process. We believe our posting process allows users to consistently post unique and informative content.
The technology we are implementing is very similar to the FERN technology stack of Firebase as a database, ExpressJS and NodeJS as backend frameworks, and ReactJS as the front-end programming language. We chose this technology stack because it allows our platform to stay lean, and be efficient with data. This allows the platform to have increased performance and lower costs.
The problem we are trying to solve is that it still takes hours to search for and find unique non-touristy experiences around the world. At a time when people can use their smartphones to have a car show up to their doorstep in minutes, it is unacceptable that it still takes hours to find an non-touristy experience on the internet.
Our value proposition is that users will be able to be anywhere in the world and be able to find an authentic, non-touristy experience that interests them. iWaandr is the most complete experience discovery tool, providing the largest collection of unique and personal experiences around the world.
Our competition is the large incumbent travel and review companies like TripAdvisor and Airbnb. There are also less established competitors that see a similar gap in the market like Mapify and Cool Cousin. We also have niche competitors that are only focused on outdoor activities like AllTrails and Outbound Collective. Google and blogs would also be competitors because people search on Google for unique experiences.
Our innovation is that we are focusing on creating unique content while our competitors are focusing on new ways to display the same content. Our advantage isn’t in a feature we created because a company with more resources could easily copy it. In order to create unique and useful content, we had to figure out a way for users to intuitively and easily post an experience with as much relevant information as possible. This involved a lot of thought into our posting process. We believe our posting process allows users to consistently post unique and informative content.
The technology we are implementing is very similar to the FERN technology stack of Firebase as a database, ExpressJS and NodeJS as backend frameworks, and ReactJS as the front-end programming language. We chose this technology stack because it allows our platform to stay lean, and be efficient with data. This allows the platform to have increased performance and lower costs.
ContributorsChee, Christian Yoshiaki (Co-author) / Bingham, Joseph (Co-author) / Cho, Steve (Thesis director) / Witwer, Bob (Committee member) / Tech Entrepreneurship & Mgmt (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
The United States is in a period of political turmoil and polarization. New technologies have matured over the last ten years, which have transformed an individual’s relationship with society and government. The emergence of these technologies has revolutionized access to both information and misinformation. Skills such as bias recognition and critical thinking are more imperative than in any other time to separate truth from false or misleading information. Meanwhile, education has not evolved with these changes. The average individual is more likely to come to uninformed conclusions and less likely to listen to differing perspectives. Moreover, technology is further complicating and compounding other issues in the political process. All of this is manifesting in division among the American people who elect more polarized politicians who increasingly fail to find avenues for compromise.
In an effort to address these trends, we founded a student organization, The Political Literates, to fight political apathy by delivering political news in an easy to understand and unbiased manner. Inspired by our experience with this organization, we combine our insights with research to paint a new perspective on the state of the American political system.
This thesis analyzes various issues identified through our observations and research, with a heavy emphasis on using examples from the 2016 election. Our focus is how new technologies like data analytics, the Internet, smartphones, and social media are changing politics by driving political and social transformation. We identify and analyze five core issues that have been amplified by new technology, hindering the effectiveness of elections and further increasing political polarization:
● Gerrymandering which skews partisan debate by forcing politicians to pander to ideologically skewed districts.
● Consolidation of media companies which affects the diversity of how news is shared.
● Repeal of the Fairness Doctrine which allowed media to become more partisan.
● The Citizens United Ruling which skews power away from average voters in elections.
● A Failing Education System which does not prepare Americans to be civically engaged and to avoid being swayed by biased or untrue media.
Based on our experiment with the Political Literates and our research, we call for improving how critical thinking and civics is taught in the American education system. Critical thought and civics must be developed pervasively. With this, more people would be able to form more sophisticated views by listening to others to learn rather than win, listening less to irrelevant information, and forming a culture with more engagement in politics. Through this re-enlightenment, many of America’s other problems may evaporate or become more actionable.
In an effort to address these trends, we founded a student organization, The Political Literates, to fight political apathy by delivering political news in an easy to understand and unbiased manner. Inspired by our experience with this organization, we combine our insights with research to paint a new perspective on the state of the American political system.
This thesis analyzes various issues identified through our observations and research, with a heavy emphasis on using examples from the 2016 election. Our focus is how new technologies like data analytics, the Internet, smartphones, and social media are changing politics by driving political and social transformation. We identify and analyze five core issues that have been amplified by new technology, hindering the effectiveness of elections and further increasing political polarization:
● Gerrymandering which skews partisan debate by forcing politicians to pander to ideologically skewed districts.
● Consolidation of media companies which affects the diversity of how news is shared.
● Repeal of the Fairness Doctrine which allowed media to become more partisan.
● The Citizens United Ruling which skews power away from average voters in elections.
● A Failing Education System which does not prepare Americans to be civically engaged and to avoid being swayed by biased or untrue media.
Based on our experiment with the Political Literates and our research, we call for improving how critical thinking and civics is taught in the American education system. Critical thought and civics must be developed pervasively. With this, more people would be able to form more sophisticated views by listening to others to learn rather than win, listening less to irrelevant information, and forming a culture with more engagement in politics. Through this re-enlightenment, many of America’s other problems may evaporate or become more actionable.
ContributorsStenseth, Kyle (Co-author) / Tumas, Trevor (Co-author) / Mokwa, Michael (Thesis director) / Eaton, John (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: 1. Understand if there is monetization potential in autonomous vehicle data 2. Create a financial model of what detailing the viability of AV data monetization 3. Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data.
ContributorsCarlton, Corrine (Co-author) / Clark, Rachael (Co-author) / Quintana, Alex (Co-author) / Shapiro, Brandon (Co-author) / Sigrist, Austin (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / School of Accountancy (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Dodd-Frank should be celebrated for its success in stabilizing the financial sector following the last financial crisis. Some of its measures have not only contained financial disaster but contributed to economic growth. These elements of Dodd-Frank have been identified as "clear wins" and include the increase of financial institutions' capital requirements, the single-point-of-entry approach to regulating financial firms, and the creation of the Consumer Financial Protection Bureau (CFPB). The single-point-of-entry strategy (SPOE), specifically, has done much to bring an end to the age of "too big to fail" institutions. By identifying firms that could expect to be aided in case of financial crisis, the SPOE approach reduces uncertainty among financial institutions. Moreover, SPOE eliminates the significant source of risk by establishing clear protocols for resolving failed financial firms. Dodd-Frank has also taken measures to better protect consumers with the creation of the CFPB. Some of the CFPB's stabilizing actions have included the removal of deceptive financial products, setting guidelines for qualified mortgages, and other regulatory safeguards on money transfers. Despite the CFPB's many triumphs, however, there is room for improvement, especially in the agency's ability to reduce regulatory redundancies in supervision and collaboration with other financial sector controllers. The significant strengths of Dodd-Frank are evident in its elements that have secured financial stability. However, it is important to also consider any potential to stifle healthy economic growth. There are several areas for legislative amendments and reforms in order to improve the performance of Dodd-Frank given its sweeping regulatory impact. Several governing redundancies now exist with the creation of new regulatory authorities. Special efforts to increase the authority of the Financial Sector Oversight Council (FSOC) and preserving the impartiality of the Office of Financial Research (OFR) are specific examples of reforms still needed to elevate the effectiveness of Dodd-Frank. In addition, Dodd-Frank could do more to clarify the Volcker Rule in order to ease banks' burden to comply with excessive oversight. Going forward, policymakers must be willing to adjust parts of Dodd-Frank that encroach too far on the private sector's ability to foster efficiency or development. In addition, identifying and monitoring areas of the legislation deemed "too soon to tell" will provide insight on the accuracy and benefit of some Dodd-Frank measures.
ContributorsConrad, Cody Lee (Author) / Sadusky, Brian (Thesis director) / Hoffman, David (Committee member) / School of Politics and Global Studies (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
My Honors Thesis is about answering a central question regarding the business of real estate: "What is the return on investment of obtaining a real estate license?" I focused my research on the monetary, time, and other value factors that affect the initial cost of securing a real estate salesperson license in the State of Arizona (costs) and the amount of money a licensed salesperson makes as a result of having a salesperson license (income). Licensees make this trade-off: the cost in terms of real dollars to obtain a license, as well as the opportunity costs associated with the time to secure, start using, and begin to earn money by way of a salesperson license. To answer the central question I conducted a survey of active licensees in order to determine the value ascribed to holding a real estate salesperson license. Through my research, I concluded that there is not a single number that can be assigned to a real estate license that indicates its value, but the data collected reveals that the return on investment has the potential to be great. Upfront costs and fees necessary to obtain a license are insignificant when the commission a licensee can then make from a single transaction is enough to cover those expenses. Therefore, based on the survey results and research into the initial costs associated with obtaining a real estate license, there appears to be sufficient data to support a positive return on investment and warrant obtaining a real estate license.
ContributorsSanders, Sarah (Author) / Stapp, Mark (Thesis director) / Koblenz, Blair (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
All of the modern technology tools that are being used today, have a purpose to support a variety of human tasks. Ambient Intelligence is the next step to transform modern technology. Ambient Intelligence is an electronic environment that is sensitive and responsive to human interaction/activity. We understand that Ambient Intelligence(AmI) concentrates on connectivity within a person's environment and the purpose of having a new connection is to make life simpler. Today, technology is in the transition of a new lifestyle where technology is discretely living with us. Ambient Intelligence is still in progress, but we can analyze the technology we have today, ties a relationship with Ambient Intelligence. In order to examine this concern, I investigated how much awareness/knowledge users that range from Generation X to Xennials, that had experience from replacing habitual items and technologies they use on a daily basis. A few questions I mainly wanted answered: - What kind of technologies, software, or tech services replace items you use daily? - What kind of benefits did the technology give you, did it change the way you think/act on any kind of activities? - What kind of expectations/concerns do you have for future technologies? To accomplish this, I gathered information from interviewing multiples groups: millennials and other older generations (33+ years old). I retrieved data from students at Arizona State University, Intel Corporation, and a local clinic. From this study, I've discovered from both groups, that both sides agree that modern technology is rapidly growing to a point that computers think as humans. Through multiple interviews and research, I have found that the technology today makes an impact through all aspects of our lives and through artificial intelligence. Furthermore, I will discuss and predict what will society will encounter later on as the new technology discretely arises.
ContributorsPascua, Roman Paolo Bustos (Author) / Yang, Yezhou (Thesis director) / Caviedes, Jorge (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Autonomous vehicles (AV) are capable of producing massive amounts of real time and precise data. This data has the ability to present new business possibilities across a vast amount of markets. These possibilities range from simple applications to unprecedented use cases. With this in mind, the three main objectives we sought to accomplish in our thesis were to: Understand if there is monetization potential in autonomous vehicle data Create a financial model of what detailing the viability of AV data monetization Discover how a particular company (Company X) can take advantage of this opportunity, and outline how that company might access this autonomous vehicle data. First, in order to brainstorm how this data could be monetized, we generated potential use cases, defined probable customers of these use cases, and how the data could generate value to customers as a means to understand what the "price" of autonomous vehicle data might be. While we came up with an extensive list of potential data monetization use cases, we evaluated our list of use cases against six criteria to narrow our focus into the following five: Government, Insurance Companies, Mapping, Marketing purposes, and Freight. Based on our research, we decided to move forward with the insurance industry as a proof of concept for autonomous vehicle data monetization. Based on our modeling, we concluded there is a significant market for autonomous vehicle data monetization moving forward. Data accessibility is a key driver in how profitable a particular company and their competitors can be in this space. In order to effectively monetize this data, it would first be important to understand the method by which a company obtains access to the data in the first place. Ultimately, based on our analysis, Company X has positioned itself well to take advantage of the new trends in autonomous vehicle technology. With more strategic investments and innovation, Company X can be a key benefactor of this unprecedented space in the near future.
ContributorsShapiro, Brandon (Co-author) / Quintana, Alex (Co-author) / Sigrist, Austin (Co-author) / Clark, Rachael (Co-author) / Carlton, Corrine (Co-author) / Simonson, Mark (Thesis director) / Reber, Kevin (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05