Barrett

Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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401(k) Plans: Do You Get What You Pay For?

Description
This paper looks at defined contribution 401(k) plans in the United States to analyze whether or not participants have plans with better plan characteristics defined in this study by paying more for administration services, advisory services, and investments. By collecting and analyzing Form 5500 and audit data, I find that…

This paper looks at defined contribution 401(k) plans in the United States to analyze whether or not participants have plans with better plan characteristics defined in this study by paying more for administration services, advisory services, and investments. By collecting and analyzing Form 5500 and audit data, I find that there is no relation between how much a plan and its participants are paying for recordkeeping, advisory, and investment fees and the analyzed characteristics of the plan that they receive in regards to active/passive allocation, revenue share, and the performance of the funds.
ContributorsAziz, Julian (Author) / Wahal, Sunil (Thesis director) / Bharath, Sreedhar (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Finance (Contributor)
Created2015-05

Sports Finance: Determining the Value of Financial Contracts in the Soccer Industry

Description

When you watch a soccer team play, the major determinant of success, wins, attributes to the quality of players and coaches, and consequently the actions they make in a game. The commercial success of a sports business, in some fashion, is affected by those same factors. It is the job…

When you watch a soccer team play, the major determinant of success, wins, attributes to the quality of players and coaches, and consequently the actions they make in a game. The commercial success of a sports business, in some fashion, is affected by those same factors. It is the job of an effective sports business to manage financial values in order to maximize the potential for the management of the team. Contracting, in terms of sports management, is a key component for the financial capabilities of every soccer organization, and the valuation of player contracts can be identified as an indicator of success or detriment of a team. The goal of this paper is to explore the current contractual inconsistencies of professional soccer organizations and create a model that projects success based on average player contract and market values. The model summarizes the actual market and contract values from the top 5 leagues over the last 5 years (2018-2023) and segments the data into 4 projections based on UEFA Champions League qualification, top half of total standings, bottom half of total standings, and relegation. This summary is then filtered to establish a range of expected contract and market values for each segment, which in turn can be used as an estimation tool for professional teams to make financial projections for the success of their team.
ContributorsHarris, James (Author) / Simonson, Mark (Thesis director) / Harris Jr., James (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor)
Created2023-05

Exploring Financial Credit Contracts Using Natural Language Processing Techniques

Description
Natural Language Processing (NLP) techniques have increasingly been used in finance, accounting, and economics research to analyze text-based information more efficiently and effectively than primarily human-centered methods. The literature is rich with computational textual analysis techniques applied to consistent annual or quarterly financial fillings, with promising results to identify similarities…

Natural Language Processing (NLP) techniques have increasingly been used in finance, accounting, and economics research to analyze text-based information more efficiently and effectively than primarily human-centered methods. The literature is rich with computational textual analysis techniques applied to consistent annual or quarterly financial fillings, with promising results to identify similarities between documents and firms, in addition to further using this information in relation to other economic phenomena. Building upon the knowledge gained from previous research and extending the application of NLP methods to other categories of financial documents, this project explores financial credit contracts, better understanding the information provided through their textual data by assessing patterns and relationships between documents and firms. The main methods used throughout this project is Term Frequency-Inverse Document Frequency (to represent each document as a numerical vector), Cosine Similarity (to measure the similarity between contracts), and K-Means Clustering (to organically derive clusters of documents based on the text included in the contract itself). Using these methods, the dimensions analyzed are various grouping methodologies (external industry classifications and text derived classifications), various granularities (document-wise and firm-wise), various financial documents associated with a single firm (the relationship between credit contracts and 10-K product descriptions), and how various mean cosine similarity distributions change over time.
ContributorsLiu, Jeremy J (Author) / Wahal, Sunil (Thesis director) / Bharath, Sreedhar (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School for the Future of Innovation in Society (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05