Barrett, The Honors College Thesis/Creative Project Collection
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.
Displaying 1 - 4 of 4
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- All Subjects: Finance
- Creators: Wahal, Sunil
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 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
Description
In this paper we conduct an out-of-sample test on gross profitability and investment in the same manner as Davis, Fama, and French (2000) for the pre-Compustat period (1926-1955). We hand-collect financial statement data from Moodys Industrial Manuals using the company PERMNO list first created by DFF. In total, we collect data from 1,291 firms, largely industrial firms but with some utilities. We then run Fama-Macbeth (1973) regressions using gross profit, scaled operating profit, scaled net income, and investment along with existing variables like book-to-market, market equity, one-month reversal, and one-year momentum. We find that the premiums on gross profitability and investment are not significant for any part of our sample period. For the overall sample period as well as the first half (before the 1933 Securities Act), our accounting data is often missing or cross-sectionally inconsistent. Despite the better-quality data in the period after 1935, however, neither gross profitability not investment have significant Fama-Macbeth slopes. We believe this is caused by inconsistent and incomplete accounting data, chiefly the number of firms that combine SG&A and COGS data into one "cost" number and the inclusion of investment-like costs, like R&D, in COGS or SG&A. This causes gross profitability to not reflect direct economic profitability as closely as in prior research. However, net income has significantly positive coefficients during this period and is not subsumed by gross profitability; this contradicts prior research for the post-1962 period. More data cleaning and analysis is needed in order to form firm conclusions on the gross profitability, net income, and investment premiums during this period.
ContributorsBergauer, Stephen (Co-author) / Pashayev, Iskandar (Co-author) / Wahal, Sunil (Thesis director) / Bessembinder, Hank (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This report is a summary of a long-term project completed by Ido Gilboa for his Honors Thesis. The purpose of this project is to determine if an arbitrage between different crypto-currency exchanges exists, and if it is possible to acts upon such triangular arbitrage. Bitcoin, the specific crypto-currency this report focuses on, has become a household name, yet most do not understand its origin and patterns. The report will detail the process of collecting data from different sources, manipulating it in order to run the algorithms, explain the meaning behind the algorithms, results and important statistics found, and conclusion of the project. In addition to that, the report will go into detail discussing financial terms such as triangular arbitrage as well as information system concepts such as sockets and server communication. The project was completed with the assistance of Dr. Sunil Wahal and Dr. Daniel Mazzola, professors in the W.P. Carey School of business. This project has been stretched over along period of time, spanning from early 2013 to fall of 2015.
ContributorsGilboa, Ido (Author) / Wahal, Sunil (Thesis director) / Mazzola, Daniel (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
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 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