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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

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
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Company X has developed RealSenseTM technology, a depth sensing camera that provides machines the ability to capture three-dimensional spaces along with motion within these spaces. The goal of RealSense was to give machines human-like senses, such as knowing how far away objects are and perceiving the surrounding environment. The key

Company X has developed RealSenseTM technology, a depth sensing camera that provides machines the ability to capture three-dimensional spaces along with motion within these spaces. The goal of RealSense was to give machines human-like senses, such as knowing how far away objects are and perceiving the surrounding environment. The key issue for Company X is how to commercialize RealSense's depth recognition capabilities. This thesis addresses the problem by examining which markets to address and how to monetize this technology. The first part of the analysis identified potential markets for RealSense. This was achieved by evaluating current markets that could benefit from the camera's gesture recognition, 3D scanning, and depth sensing abilities. After identifying seven industries where RealSense could add value, a model of the available, addressable, and obtainable market sizes was developed for each segment. Key competitors and market dynamics were used to estimate the portion of the market that Company X could capture. These models provided a forecast of the discounted gross profits that could be earned over the next five years. These forecasted gross profits, combined with an examination of the competitive landscape and synergistic opportunities, resulted in the selection of the three segments thought to be most profitable to Company X. These segments are smart home, consumer drones, and automotive. The final part of the analysis investigated entrance strategies. Company X's competitive advantages in each space were found by examining the competition, both for the RealSense camera in general and other technologies specific to each industry. Finally, ideas about ways to monetize RealSense were developed by exploring various revenue models and channels.
ContributorsDunn, Nicole (Co-author) / Boudreau, Thomas (Co-author) / Kinzy, Chris (Co-author) / Radigan, Thomas (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / WPC Graduate Programs (Contributor) / Department of Psychology (Contributor) / Department of Finance (Contributor) / School of Accountancy (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Science (Contributor) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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The focus shift towards Silicon Valley and similar ecosystems in the past decade, the recent boom in startups and entrepreneurship, and the resurgence of venture capital funding is fueling rapid advancement of modern technologies, such as software, biotechnology, and renewable energy. One facet of the growing entrepreneurial landscape features healthcare

The focus shift towards Silicon Valley and similar ecosystems in the past decade, the recent boom in startups and entrepreneurship, and the resurgence of venture capital funding is fueling rapid advancement of modern technologies, such as software, biotechnology, and renewable energy. One facet of the growing entrepreneurial landscape features healthcare technology—a field of research centered upon various technical advances in medicine, software, and hardware. Trends in healthcare technology commercialization represent a promising opportunity for disruption in the healthcare industry. The integration of rapidly iterating software with medical research, timed perfectly with the passage of the Affordable Care Act and the boom of venture capital investment in both Big Data and mobile technology, has the healthcare technology primed for explosive growth over the next decade. Investment data indicates that strong public market activity in the past year will continue to fuel venture capital growth in both the biotechnology and digital health sectors, with the potential for multiple large exits by life sciences companies, more than even software, in the coming year.
ContributorsPatel, Nisarg (Co-author) / Yun, Kwanho (Co-author) / Wang, Xiao (Thesis director) / Marchant, Gary (Committee member) / Peck, Sidnee (Committee member) / Barrett, The Honors College (Contributor) / Department of Management (Contributor) / School of Politics and Global Studies (Contributor) / School of Life Sciences (Contributor)
Created2014-05
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The abortion debate has been a heated topic since the early 1970's when the monumental case, Roe v. Wade was decided. Roe v. Wade, along side of it's sister case Doe v. Bolton, ruled that no law restricting abortion could be passed and set the precedent that life did not

The abortion debate has been a heated topic since the early 1970's when the monumental case, Roe v. Wade was decided. Roe v. Wade, along side of it's sister case Doe v. Bolton, ruled that no law restricting abortion could be passed and set the precedent that life did not exist before birth. Before this time, people were largely unaware of what life inside the womb looked like and therefore had no reason to believe that life truly began at conception. As medical technology has revealed more about life inside the womb, the pro-life movement has been tasked with the uphill battle to shift the discussion around the topic. Because people now spend so much time using various forms of technology, it has become an effective way for groups and organizations to come in contact with large amounts of people. This is something the pro-life group has not only done, but has excelled at. By successfully utilizing advancing technology combined with new medical tools and discoveries, the pro-life movement has successfully gained an increasingly large momentum and following in a relatively short amount of time. Recognizing that technology alone does not have the ability to change people's hearts, but must be backed up with arguments and strong evidence, this paper will explore the medical advances that helped drive pro-life arguments, the technological advances that have become a platform to disseminate this information, and ways the pro-life movement has utilized each new form of technology. Lastly, this paper will explore the amount of growth the pro-life movement has experienced since the early 1970's. In the end, the pro-life movement has successfully combined all these different advances to create a movement that has reached a vast audience and gained exponential awareness and momentum. They have used everything from social media, the Internet, and videos to spread the truth about abortion. As a result, minds are being changed, people are driven into action, and babies are being saved.
ContributorsSnyder, Lorne Lynn (Author) / Critchlow, Donald (Thesis director) / Anderson, Owen (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / School of Politics and Global Studies (Contributor)
Created2014-12
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We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones

We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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This study analyzed currently existing statute at the state, federal, and international level to ultimately build a criteria of recommendations for policymakers to consider when building regulations for facial recognition technology usage by law enforcement agencies within the United States.

ContributorsHong, Susan Suggi (Author) / Royal, K (Thesis director) / Marchant, Gary (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Politics and Global Studies (Contributor, Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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The foundations of legacy media, especially the news media, are not as strong as they once were. A digital revolution has changed the operation models for and journalistic organizations are trying to find their place in the new market. This project is intended to analyze the effects of new/emerging technologies

The foundations of legacy media, especially the news media, are not as strong as they once were. A digital revolution has changed the operation models for and journalistic organizations are trying to find their place in the new market. This project is intended to analyze the effects of new/emerging technologies on the journalism industry. Five different categories of technology will be explored. They are as follows: the semantic web, automation software, data analysis and aggregators, virtual reality and drone journalism. The potential of these technologies will be broken up according to four guidelines, ethical implications, effects on the reportorial process, business impacts and changes to the consumer experience. Upon my examination, it is apparent that no single technology will offer the journalism industry the remedy it has been searching for. Some combination of emerging technologies however, may form the basis for the next generation of news. Findings are presented on a website that features video, visuals, linked content, and original graphics. Website found at http://www.explorenewstech.com/
Created2016-05
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This thesis details our experience assisting BASE Equity Partners, a private equity firm based in New York City, on three prospective agricultural dealership deals over the course of this past academic year. The firm is currently structured as a Fundless Sponsor. This distinct structural trait is common for a type

This thesis details our experience assisting BASE Equity Partners, a private equity firm based in New York City, on three prospective agricultural dealership deals over the course of this past academic year. The firm is currently structured as a Fundless Sponsor. This distinct structural trait is common for a type of private equity firm known among practitioners as pledge funds. This creates an interesting element for our experience as there is very limited academic research on these types of firms, which, since the Great Recession, have become popular players in middle-market private equity deals. We, first, provide some historical context on pledge funds and identify their primary differences with traditional private equity. The remainder of the paper documents our experience working on the agricultural dealership deals. We have organized this portion after the manner in which we received assignments. We go into detail on the specific projects with which we were tasked, our interactions with the partners and the major takeaways we had from this learning experience. This thesis paper will enrich the academic knowledge regarding pledge funds—and private equity generally—by documenting a real experience of what it is like performing analyst-level tasks at a real firm. Additionally, we were privy to information that is highly confidential, and though we have protected the confidentiality of the companies through pseudonyms and redaction of confidential material, all of the financial data shown, models provided and qualitative discussion is real.
ContributorsTang, Ivan (Co-author) / Johnson, Bradley (Co-author) / Panosian, Tro (Co-author) / Simonson, Mark (Thesis director) / Bonadurer, Werner (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of English (Contributor) / School of Accountancy (Contributor) / School of International Letters and Cultures (Contributor)
Created2015-05
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The following thesis discusses the primary drivers of value creation in a leveraged buyout. Value creation is defined by two broad criteria: enterprise value creation and financial value creation. With enterprise value creation, the company itself may be improved, which in turn may have positive implications on the economy at

The following thesis discusses the primary drivers of value creation in a leveraged buyout. Value creation is defined by two broad criteria: enterprise value creation and financial value creation. With enterprise value creation, the company itself may be improved, which in turn may have positive implications on the economy at large. As the analysis of enterprise value creation is outside the scope of publicly available information and data, the core focus of this thesis is financial value creation. Financial value creation is defined as the financial returns to a given private equity firm. Amongst this segment of value creation, there are roughly three primary categories responsible for generating returns: financial engineering, governance improvements, and operational improvements. The attached literature review and subsequent chapters of this thesis discuss the academic drivers of value creation and the outputs of a leveraged buyout model conducted on a public company, Schnitzer Steel, that has been determined to be an ideal candidate for a buyout.
ContributorsAlivarius, Chadwick (Author) / Simonson, Mark (Thesis director) / Stein, Luke (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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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

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