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Federal Reserve Interest Rate Management: Combatting Speculation of Market Volatility and Recessionary Sentiment

Description

Abstract: Handling the multiple functions of monetary policy that protect the U.S. economy not only on a short term, but also long-term scale is a complicated responsibility assigned to Federal Reserve, in which their actions present a profound impact on

Abstract: Handling the multiple functions of monetary policy that protect the U.S. economy not only on a short term, but also long-term scale is a complicated responsibility assigned to Federal Reserve, in which their actions present a profound impact on consumer confidence towards financial markets and global economies. Specifically, one of the most important goals of the Federal Reserve is to mitigate the risk of the United States to enter a recession, while maintaining a balanced approach when making those policy decisions. In this thesis, we focus on the monetary policy of the Federal Reserve, particularly, their role in controlling interest rates to prevent recessionary sentiment in the current state of the economy. Since 2008, markets have been stronger and previous policies like Dodd-Frank have ensured that market collapses during the Great Recession do not repeat itself. Yet, fluctuations in the yield curve, polarizing investment views, and unsettled consumer confidence has pointed to another recession in the near future. In this case, we will look at the way the Fed has implemented short term policies to lower this risk in order to fight volatile markets, however, fluctuating interest rates has its consequences. The goal of this thesis is to analyze the various ways the Fed has managed interest rates in the past and present, and further, to offer a framework to serve as the most effective policy to combat volatility and recessionary sentiment in the U.S. economy.

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Date Created
2020-05

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Networking Pricing Analysis

Description

This thesis examines the impact of price changes of select microprocessors on the market share and 5-year gross profit net present values of Company X in the networking market through a multi-step analysis. The networking market includes segments including media

This thesis examines the impact of price changes of select microprocessors on the market share and 5-year gross profit net present values of Company X in the networking market through a multi-step analysis. The networking market includes segments including media processing, cloud services, security, routers & switches, and access points. For this thesis our team focused on the routers & switches, as well as the security segments. Company X wants to capitalize on the expected growth of the networking market as it transitions to its fifth generation (henceforth referred to as 5G) by positioning itself favorably in its customers eyes through high quality products offered at competitive prices. Our team performed a quantitative analysis of benchmark data to measure the performances of Company X's products against those of its competitors. We collected this data from third party computer reviewers, as well as the published reports of Company X and its competitors. Through the use of a preference matrix, we then normalized this performance data to adjust for different scales. In order to provide a well-rounded analysis, we adjusted these normalized performances for power consumption (using thermal design power as a proxy) as well as price. We believe these adjusted performances are more valuable than raw benchmark data, as they appeal to the demands of price-sensitive customers. Based on these comparisons, our team was able to assess price changes for their market and discounted financial impact on Company X. Our findings challenge the current pricing of one of the two products being analyzed and suggests a 9% decrease in the price of said product. This recommendation most effectively positions Company X for the development of 5G by offering the best balance of market share and NPV.

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Date Created
2018-05

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Monetization of Autonomous Vehicle Data

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

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.

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Created

Date Created
2018-05

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The Monetization of Autonomous Vehicle Data

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

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.

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Created

Date Created
2018-05

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Market Analysis of a Potential Discrete GPU Product Offering for the Internet of Things

Description

There is a growing demand for discrete graphics processing units (dGPU) in the internet of things. Our subject company, Company X, has decided to develop a dGPU to be used in client computing (desktops, laptops, etc). This project will address

There is a growing demand for discrete graphics processing units (dGPU) in the internet of things. Our subject company, Company X, has decided to develop a dGPU to be used in client computing (desktops, laptops, etc). This project will address whether or not company X should invest time and money into adopting their existing client focused dGPU for applications in IoT such as digital signage, gaming, or medical imaging. If this investment is to be made, we will also make specific recommendations about how Company X should enter the IoT space. The project will be completed in 3 stages. The first stage will consist of an analysis of the competitive landscape and research on dGPUs and how they differ from integrated GPUs. Stage two will focus primarily on the IoT space and how the competitors are using dGPUs in the IoT along with an analysis of three potential use cases for Company X’s dGPU. Finally, we will build a comprehensive financial model based on our research of one specific IoT segment where Company X could potentially enter. Based on these stages, we will then offer a conclusion and recommendation on whether Company X should invest in this project.

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Agent

Created

Date Created
2019-05

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Exploring the Relation Between NAV and Price of ETFs in Financial Markets

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

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.

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Agent

Created

Date Created
2019-05

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Exploring the Relation Between NAV and Price of ETFs in Financial Markets

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

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.

Contributors

Agent

Created

Date Created
2019-05

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Private Equity Reputation: Arbitrage Spreads and Offer Premiums of Public to Private Transactions

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

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.

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Agent

Created

Date Created
2019-05

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Value Creation in Leveraged Buyouts

Description

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

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.

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Created

Date Created
2019-05

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Evolution of the Mobile Gaming Market and Contextualizing the ATVI-KING Acquisition

Description

This paper discusses the development of the mobile gaming industry and analyzes a mobile game acquisition to provide context to the entire market. By discussing the history and growth of the industry, I discovered that mobile gaming was a massive

This paper discusses the development of the mobile gaming industry and analyzes a mobile game acquisition to provide context to the entire market. By discussing the history and growth of the industry, I discovered that mobile gaming was a massive opportunity for companies to generate lucrative earnings. The discussion revolving around the evolution of the mobile gaming business model serves to provide context on the industry’s unique opportunities and risk factors. Candy Crush’s developer King is the main focus in this paper as they were the highest-performing public company in the market. The company is the greatest example of the mobile gaming phenomenon, experiencing rapid growth due to the success of its games, faltering in financial performance after going public, and finally becoming a subsidiary of a larger video game company that recognized King’s potential. King’s acquirer, Activision-Blizzard (ATVI), is an industry veteran of the overall video game industry that bought out King in an attempt to capitalize on the rising popularity of mobile games and to improve their strategic position in the larger video game market. The mergers & acquisitions (M&A) analysis between ATVI and King serves to determine whether or not the acquisition was an appropriately priced deal and if King represented a worthy buy. A discounted cash flows model is the basis for the analysis using a wide range of assumptions to account for the volatility of the industry. Finally, an event study and post-acquisition analysis are conducted to determine if any financial synergies were achieved in the ATVI-King acquisition. While the analyses do not offer a definitive conclusion on King’s post-acquisition performance, it can be said that the company has managed to achieve some measure of longevity. In the context of the entire mobile gaming market, the potential of mobile games should make developers attractive in the eyes of investors and acquirers, provided they understand the mobile gaming industry’s unique risks.

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Agent

Created

Date Created
2019-05