Undergraduate research in the field of finance is vibrant at Elon with students delving into a variety of topics ranging from the valuation of professional golfers and European Football League Game Theory Optimization to exploring networking and diversity issues in venture capital. If you are interested in pursuing undergraduate research in finance, we encourage you to reference finance faculty pages to learn more about their research and then schedule meetings to find a faculty member whose research interests and abilities overlap with your potential research topic. The Department of Finance asks students to sign its research contract after finalizing a topic and finding a faculty mentor.
Recent Research Projects
“The Relationship Between Trade Credit and Intangible Assets”
Sarah Taylor Hartsema (mentor: Dr. Christopher Harris)
Many companies use trade credit as a way to finance growth. Trade credit is when a supplier extends credit to a company, allowing them to receive the supplies or product now and pay for it in the future. Suppliers are willing to use trade credit because it allows them to increase sales and gain new customers. Previous research shows that trade credit is most affected by firms that sell differentiated goods and services, in particular businesses with traditional manufacturing processes (Giannetti, 2011; Falato, 2014). However, there has been a decline in the level of traditional manufacturing, as the economy shifts to more service-oriented businesses. Because of this shift, a growing area of interest is examining how intangible assets impact financial growth. Intangible assets are items that a company owns that aren’t physical, such as goodwill, brand recognition, or a patent. These assets are important to service firms because they don’t have as many tangible assets (that would normally be used for manufacturing processes). The purpose of this study is to first examine whether a firm’s trade credit policy is influenced by the amount of intangible assets the firm has, and whether this relationship is influenced by the type of product sold. This study finds a correlation between intangible assets and trade credit, and firms will extend less trade credit if they have more intangible assets. For firms in industries that care more about trade credit, such as service firms, this relationship is even more pronounced. Another important aspect of this research is how trade credit policy is changed when firms are in financial distress. This research shows that firms that care about trade credit are those that are affected by financial constraint. This research was conducted using a fixed effect regression with panel data from Compustat and using STATA to generate results. This study is furthering research in the finance discipline by building on work that other scholars have accomplished and showing how intangible assets and trade credit are related in ways that have not been shown previously.
“A Study of the Impact of President Donald Trump’s Company-Specific Tweets on Company Stock Performance”
Charlie Trinco ’20 (mentor: Dr. Kate Upton)
This study investigates the impact of President Donald Trump’s (“the President,” “President Trump”) company-specific Twitter mentions (“tweets”) on company stock performance. The impact of Presidential statements on the stock market is of interest and importance to those who invest money in the markets, which fuel the economy. As social media and knowledge-sharing platforms continue to increase in usage, the significance of what is posted by economic influencers is also important to consider when investing. From his election as the 45th president of the United States on November 8, 2016, through the midterm elections of November 6, 2018, President Trump’s Twitter account, @realDonaldTrump, released 5,877 tweets. These tweets were used as the sample for this study. Each tweet was coded for company-specific mentions and overall connotation, which was extracted by the researcher. Tweets with a company mention were extracted from the sample, an event window of 30 days prior to the tweet and 1 day after the tweet was created for each tweet, and three data points were collected for each event window: returns of the S&P 500 stock index, returns of the respective stock mentioned in the tweet, and tweet connotation. Cumulative abnormal returns were calculated to measure the impact of the President’s tweets on company stock performance. The results show that a company mentioned in a positively connotated tweet by president Trump results in a 66 to 95 basis point higher cumulative abnormal return during the tweet’s event window. These results are substantially stronger for companies other than Fox News; Fox News accounted for 53.9% of the company mentions, and the results of their cumulative abnormal returns were insignificant, suggesting a desensitization to repetitive tweeting about the same company.
“Does Gender Diversity Impact Financial Performance and Risk?”
Simran Puri ’20 (mentor: Dr. Kate Upton)
Companies in the United States have started to publically display their high diversity rates to their shareholders and customers because they are aware of the beneficial impact that it has on its firm. Previous studies have indicated that gender diversity is positively related to a firm’s profitability. This research will focus on gender diversity and the effects it may have on a firm’s risk metrics and financial health. To measure financial health this study employs Return on Assets, Return on Equity, and Profit Margin. For risk metrics this research will utilize Beta and Standard deviation to measure if the percentage of women employed at a firm affects risk taking within a firm. This study uses regression analysis to determine if there is significant correlation between the percentage of women employed at a firm and the aforementioned risk metrics and financial health variables. The study will explore all companies in the S&P 500, examining effects at the aggregate and then within various sectors.
“Breaking Through the Glass Ceiling: An Investigation into Women’s Compensation and Qualifications in the C-Suite”
Julia R. Goldstein ’20 (mentor: Dr. Kate Upton)
The study into women executives in the business environment is a topic that has garnered significant research and media interest in the last few years. This study investigates gender trends in the corporate executive suite (C-suite) of Fortune 500 companies between 1992-2018, and is split into two portions, quantitative and qualitative. By collecting data from the Execucomp database, three goals are accomplished for the quantitative portion. First, the percentage of women in leadership positions across the entire market is analyzed for significant trend changes. Second, the data is divided into 63 Standard Industry Classification (SIC) Codes, and the percentage of women leaders is examined in each industry to determine if it is significantly different from the mean of the entire sample (5.75%). Third, compensation gaps in each SIC Code are analyzed by comparing the average female compensation to the average male compensation across the different industries. The second portion of the study is qualitative in nature, and features data collected from Bloomberg vetted biographies. One female executive is randomly selected from each SIC Code in 2017, and matched by compensation and age in the year observed to two male executives in the same industry. Each executive’s qualifications are statistically coded and analyzed with hopes to determine if women executives are required to be more or less qualified to achieve the same compensation level as male executives. This study attempts to shed light on if and why there has been an increase of women executives in the C-suite, and to provide insight on how women can structure their career to have opportunities to achieve higher leadership positions and further change the business landscape. Significant findings include determining that the percentage of female executives in the market has been decreasing over the past few years contrary to popular belief. Additionally, this study determined that the Manufacturing, Transportation & Utilities, Services, and Wholesale & Retail Trade industries all have a higher percentage of female executives compared to the average. This study also found that only the Manufacturing industry as a whole has a positive compensation gap where women have a higher salary then men, while various SIC Code Level 2 industries have a positive compensation gap. Finally, in the quantitative portion of the study, factors such as enrolling in post-graduate and terminal education, holding certifications, and having a higher number of past positions, memberships held and committees served on were found significantly different between male and female executives.
“The Impact of Venture Capital Funding Patterns on Innovation and Success”
Clarence M. Mourot ’19 (mentor: Dr. Kate Upton)
This study contributes to previous literature on the role of venture capitalist in innovation by examining the patterns of how companies invest VC (venture capital) funds. A venture capital firm’s goal is to identify, fund, and expand companies with strong growth potential. The focus of this study is to better understand how startup funding patterns, including frequency and amount, have an impact on the company’s innovation and success. I seek to identify if there is an ideal combination of funding pattern to foster innovation and more broadly promote success through exits (IPOs and acquisitions). It is important to understand if VC firms effectively promote innovation and if that innovation is rewarded by the market. This study is conducted in two phases. First, I analyze the impact of funding patterns on innovation measures. Then I study the effect this innovation has on the ultimate success of the firm. To conduct the analysis, I extract data from Crunchbase Pro, a startup database compiling all funding information, investors and firm characteristics. The data is used in regression analysis with the main independent variables of interest being: number of acquisitions, IPO status and money raised, patents and trademarks and IT spending. These empirical results aid in the understanding of optimal funding patterns for encouraging innovation and ultimately a successful exit. We expect to find a correlation between the number of funding rounds/amount of funding with our measures of innovation, frequency of IT spending and patents/trademarks.
“A Genetic Algorithmic Approach to Statistical Arbitrage Strategies”
Zachary J. Lahey (mentor: Dr. Adam Aiken)
Statistical arbitrage trading strategies attempt to profit by identifying securities that are incorrectly priced. This research utilizes a popular statistical arbitrage trading technique known as pairs trading. The pairs trading technique looks to find two securities that have similar price movements.The idea of pairs trading was developed by a team of physicists, mathematicians, and computer scientists at Morgan Stanley in the 1980’s. Statistical arbitrage strategies utilize statistical techniques to identify securities that have similar price movements and profit from “mispricing’s” between the securities when the prices move away from each other and then converge. Although quantitative based statistical arbitrage strategies are an important tool to identify security mispricing’s, their popularity has started to affect their profitability. Rad, Kwong, Low, and Faff (2016) 1 find that, since 2009, the number of trading opportunities has decreased significantly for traditional pairs trading strategies. In addition, they also found that during different market periods, some pairs trading strategies performed better than others. This project seeks to improve the profitability of statistical arbitrage trading strategies by applying a Genetic Algorithm optimization method to identify optimal parameters for these strategies. A Genetic Algorithm is a search algorithm that is modeled after Darwin’s theory of natural selection. Like natural selection, there are five phases in a genetic algorithm: 1. Initial Population, 2. Fitness test of population, 3. Selection of best in population, 4. Crossover of best in population, 5. Mutation of the population. To test this method, equity price data from the year 1963 to the year 2017 was collected from The Center for Research in Security Prices (CRSP). The three different types of statistical arbitrage signals were used to identify statistical arbitrage opportunities, Distance, Cointegration, and Copula methods. The optimization method will be applied to identify statistical arbitrage trading opportunities over the 53 year period. This strategy will be benchmarked against industry standard statistical arbitrage strategies, comparing different portfolio metrics to test for outperformance.
“Effects of the JOBS Act on IPOs of Biotechnology and Pharmaceutical Firms in the U.S.”
Anthony J. Potenza ’19 (mentor: Dr. Margarita Kaprielyan)
Initial public offerings [IPOs] occur when private companies are taken public where they issue shares in order to gain access to broader capital markets. The IPO process can be very long, tedious, and costly. However, the Jumpstart Our Business Startups Act [JOBS Act], signed into law in 2012, served the purpose of making the IPO process easier and less expensive for emerging growth companies (EGCs). Two main provisions of the act with the largest beneficial effect on EGCs include: (i) de-risking provisions and (ii) de-burdening provisions. These provisions affect the levels of asymmetric information present in IPOs between investors, companies, and underwriters; thus, I expect to find that the JOBS Act affected the three underlying IPO phenomena by asymmetric information theories: (i) underpricing at the time of issuance, (ii) long term stock price underperformance, and (iii) high volatility of returns. With regards to my study, the de-risking provisions of the JOBS Act may have a greater impact on IPOs of Biotechnology and Pharmaceutical firms than others and recent studies show that IPO volume post-JOBS Act increased significantly for these industries. Due to the extremely large investments required for R&D expenses and product development, which often pose significant risks, the de-risking provisions may prove beneficial for firms in this sector by reducing costly proprietary disclosure costs associated with IPO registration process and ultimately freeing up capital for use elsewhere. This study serves to test whether these two main sets of provisions of the JOBS Act have a significant impact on IPO underpricing, long-term performance, and price volatility of Biotechnology and Pharmaceutical firms to estimate the benefits/risks for the industry and the investors. To test the hypotheses, I use multivariate regressions analysis with first-day return, long-run return (1 and 2–year buy and hold return), and annual return volatility as dependent variables. The independent variable of interest is the post-JOBS Act time indicator variable and I control for various firm characteristics, venture capital backing, and the emerging growth company status. I anticipate the findings to be consistent with the predictions of the asymmetric information theories.
“Empirical Test of Weak-Form Efficient Market Hypothesis Using Representative Technical Trading Strategies”
Jonas R. Hauser ’18 (mentor: Dr. Jongwan Bae)
Technical trading strategies attempt to predict future stock movements based on past price developments. Permutations of these strategies are ever evolving with several books documenting a variety of techniques. Nonetheless, there is much controversy about the ability of such strategies to generate returns above those of passive trading strategies. Some investors rely on technical trading, while many people believe that asset markets are efficient and that these strategies cannot consistently and systematically provide investment opportunities above market standards. The hypothesis of the project is that returns will vary across geographic regions (i.e. countries) because even though investors have come to allocate funds much more internationally over the last decades, domestic investors still are a crucial factor. Furthermore, the returns from the selected strategies over the last 30 years are expected to not beat market standards the same way prior research has found them to do over the first 80 years of the 20th century. To test these hypotheses, the project examines the profitability of some of the most popular trading strategies over the last 30 years, using various statistical tools to determine if the technical trading strategies examined can indeed provide superior performance. These tests were executed for three markets in different geographic regions. The results for the different samples are evaluated to determine if there are any substantial differences in the returns generated across different geographic regions. If well-known active trading strategies can consistently outperform passive strategies, this could indicate possible inefficiencies in the market. This should help draw a conclusion about the development of market efficiency regarding the use of the selected technical trading strategies.
“The Role of Venture Capital Networks in Startup Success”
Matt Snow ’18 (mentor: Dr. Adam Aiken)
Venture Capitalists (VCs) perform a vital service in our economy by providing the financing necessary for new companies to be born. Arguably more important than merely receiving funding, however, is receiving funding from venture capitalists that can leverage network effects. Venture capitalists typically invest alongside other venture capitalists. Network effects are created when certain investors are better informed about opportunities than others due to their personal relationships. Read more.
“The Gender Dynamics in Venture Capital Funding Decisions in the United States: An Empirical Analysis”
Timon Merk ’18 (mentor: Dr. Kate Upton)
Venture capital is a critical source of funding for early-stage emerging firms in the startup ecosystem in the US to support those companies’ growth and innovation opportunities. Yet, women entrepreneurs appear to face significant challenges in accessing venture capital to grow their businesses, resulting in a gender gap and disparity in venture capital funding decisions. This research aims at identifying the gender dynamics in these funding decisions based on effects derived from the body of literature. Specifically, effects examined include the geographic distribution of startup firms with female entrepreneurs, size of the firms, stage in the venture funding cycle, funding rounds, funding amount and an interaction between female entrepreneurs and female investors. Based on venture capital transaction data retrieved from Crunchbase, which provides information on funding rounds, investors and startups, and using a descriptive research design, this study unveils underfunding patterns for female entrepreneurs perpetuated in the venture capital ecosystem in the US once controlling for effects beyond gender that impact the funding outcome. In addition to that, the results unearth a gender pairing effect grounded in the interaction between female entrepreneurs and female investors, resulting in a reversal of underfunding patterns observed in this ecosystem. This research contributes to a growing body of literature that examines the underlying mechanisms and dynamics in venture capital funding decisions. By conceptualizing gender-induced biases that exist in the interaction between entrepreneurs and investors and that affect the funding outcome for female entrepreneurs, this study contributes to fostering a better understanding of these characteristics and offers a reference point for investors and entrepreneurs alike to raise awareness of the importance to dismantle explicit and implicit barriers that prevent female entrepreneurs from accessing venture capital resources.