Business Analytics Commons^™

U.S. Options Exchange-Traded Funds: Performance Dynamics And Managerial Expertise, Elroi Hadad, Davinder K. Malhotra, Robert Mcleod

School of Business Faculty Papers

This study examines the performance dynamics of U.S. options exchange-traded funds (ETFs), whose investment strategy involves options contracts. Analyzing monthly returns data from February 2014 to April 2023, we evaluate the risk-adjusted performance, volatility, and market sensitivity of U.S. options ETFs relative to U.S. and global equities. Using Carhart's four-factor model, we find that U.S. options ETFs yield lower monthly returns than those of U.S. equities but outperform global equities, suggesting potential diversification benefits. While U.S. options ETFs underperformed during the COVID-19 pandemic, they demonstrated resilience thereafter, offering higher rewards for downside risk. We also find that managerial expertise may …

Decoding The Algorithm: The Mathematics Behind Tiktok’S Short-Form Content Success, Ashley N. Lynch

Honors Scholar Theses

Within the realm of social networks, TikTok has become the central hub for short-form video content. The network’s unique ability to capture individual preferences using predictive analytics has greatly contributed to its massive success, allowing the company to optimize its performance and content personalization. In an age where digital media have such a significant influence on society, it is essential that users develop an understanding of how social network algorithms function to make more informed online decisions. Although TikTok’s technological system is primarily undisclosed, the platform certifiably leverages several key mathematical principles within its algorithm to achieve its core goals …

The Impact Of Ai On Salary Trends And Employment Projections, Anastasiia Semerianova, Annie Chen, Rebecca Mui, Ekaterina Viro, Hui Ting Huang, Thiy Alsaidi

Publications and Research

This project is designed to research the potential future impact of AI implementation on job displacement across key industries such as healthcare, manufacturing, and others. It aims to analyze how automation and AI technologies influence employment trends and workforce demands, as well as the correlation between AI integration and changes in salary structures. The goal is to provide data-driven insights that can inform policymakers, educators, and industry leaders on how to prepare for and adapt to evolving labor market dynamics.

Gap, Inc. : A Strategic Audit Of Gap, Inc. Company, Amber Hanson, Ike Mcley, Sophie Thomas, Avery Plessel, Kira Pavlik

Honors Program: Senior Projects (Public)

The following paper is a comprehensive overview of the company Gap, Inc., a leading global retailer with a diverse brand portfolio that includes Gap, Old Navy, Banana Republic, and Athleta. They compete in the Family Clothing Industry through their operation of over 3,352 brick-and-mortar stores along with offering their digital shopping platform to align with changing consumer preferences. Gap, Inc. plans to maintain financial rigor, reinvigorating its brands, and strengthening its platform. Their strengths are found in their expanding e-commerce segment and their strong commitment to sustainability. Through a broad differentiation strategy, Gap, Inc. targets a diverse consumer demographic across …

Sustainabilty Reporting And E-Waste Management In The Electronic Industry, Sai Prasad Pochampally

Electronic Theses, Projects, and Dissertations

The rapid expansion of the electronic and electrical equipment (EEE) industry has led to a critical surge in electronic waste (e-waste), which is currently growing at an annual rate of 4%. E-waste contains hazardous substances such as mercury, cadmium, and lead, which contribute significantly to environmental degradation and pose serious health risks—particularly in low- and middle-income countries where proper recycling infrastructure is lacking. In response, electronics manufacturers increasingly rely on Environmental, Social, and Governance (ESG) reporting frameworks to demonstrate transparency and accountability. However, a significant gap persists between what companies choose to report and what stakeholders—particularly consumers—expect to see.

This …

Challenges The Sporting Industry Faced During Covid-19 On Fan Attendance: The Case Of The National Basketball Association, Marlon Long

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the challenges the sporting industry faced during the COVID-19 pandemic, with a focused look at the National Basketball Association. The research questions are: (Q1) How did the NBA arena shutdowns impact fan attendance for each team during the COVID-19 pandemic? (Q2) What factors influenced NBA arena attendance before the COVID-19 shutdowns? (Q3) What factors influenced NBA arena attendance after the COVID-19 shutdown? The data collected includes all 30 NBA teams from 2019 through the 2024 seasons. The research questions were analyzed using multilinear regression analysis and comparison of attendance data over 6 seasons including the …

Leveraging Data Analytics For Enchanced Economics Prediction: A Study Of Predictive Models And Economics Indicators, Janelle Rueda

Electronic Theses, Projects, and Dissertations

The purpose of this project is intended to investigate the lack of use of data analytics in predicting and managing the impacts of economic crises. This project investigates what economic key factors can be used to create a framework model and evaluate the effectiveness of the key economic indicator being tested as early warning signs. The project uses programs such as Excel, RStudio, and Tableau for the collection of historical data, and regression analysis to determine if certain economic indicators have a statistically significant correlation with GDP, that may help enforce systems in place to help us mitigate economic crises …

Analytics Insights From Text: Machine Learning, Ai, And Sentiment Analysis On Beige Books, Charlie Smith

Graduate Theses and Dissertations (2019 - present)

Business analytics is about drawing actionable insights from data. These distinct but connected essays represent a novel approach to explore how natural language processing (NLP) advances and machine learning can transform unstructured text data into actionable conclusions. Essay 1 provides a broad framework. Essay 2 strengthens the sentiment analysis with the most recent artificial intelligence methodologies for capturing nuanced sentiment in complex texts. Essay 3 applies those insights to forecast recessions using topics that can be readily interpreted and applied.

The research demonstrates how these methodologies can be applied to enhance understanding of the same dataset, Beige Books. Published by …

Comparative Analysis Of Regression And Random Forest Models For Player Performance Prediction In The Mls, Joshua Clement Madeti

Senior Honors Theses

Advanced technology and analytics have transformed the world and have benefited several industries throughout, the sport industry being one of them. Data is constantly generated during sports and requires post-game or post-season analysis which is crucial to team and player success. In this paper, the researcher will focus on the impact of analytics on soccer and soccer players. With over three billion active fans, soccer is the most famous sport in the world yet, when it comes to analytics, it is lagging. The thesis includes a comparative study of multiple linear regression and random forest regression to explore whether these …

Understanding Household Financial Stability: Examining The Effects Of Inflation And Income, Cyndy E. Gutierrez-Fierros

Electronic Theses, Projects, and Dissertations

This study aims to investigate the impact of inflation on US household financial stability from 2013 to 2023. As inflation rises, individuals’ purchasing power erodes leading to influence economic behaviors. Behaviors such as spending, saving, and borrowing should be a priority concern in the US economy. Results of this study reveal that inflation does not affect financial well-being and its direct impact on household stability is statistically weak. Regression testing shows that income emerges as the most significant factor. Most noticeably income influences savings and debt management in a positive way. As per these results, economic policies should be focused …

The Electric Revolution: A Quantitative Analysis Of Tesla's Sales Growth, Mason Bravo

Accounting Undergraduate Honors Theses

This thesis explores the key factors influencing Tesla's sales growth within the evolving electric vehicle (EV) market. It uses regression analysis to quantify the impact of average sales price, charging infrastructure, EV demand, and macroeconomic performance on Tesla's vehicle sales. The analysis is based on data from Tesla's annual reports, the Alternative Fuels Data Center, the International Energy Agency, and the Bureau of Labor Statistics. Key findings include a significant negative correlation between average sales price and vehicle sales, and a strong positive correlation between charging infrastructure and sales. These insights offer valuable implications for Tesla’s strategic decision-making, investor analysis, …

Strategic Audit: Hilton Worldwide Holdings, Inc., Jenna Derowitsch, Tram Ngo, Tenley Katt, Makenna Henning, Lexi Soukup

Honors Program: Senior Projects (Public)

This comprehensive strategic audit analyzes the internal and external environment in which Hilton Worldwide Holdings Inc. operates, in addition to auditing the strategic drivers that have drove the iconic hospitality firm to success. Hilton Worldwide Holdings, Inc. (Hilton) is a valued hotel chain that has been operating as a global leader in the hospitality industry for more than 100 years. The firm sets an industry standard to provide high-quality hotel services and a reliable stay for more than 200 million guests annually and continues to develop its portfolio of destinations. Technological advancements and shifting customer preferences challenge Hilton to remain …

Telehealth And Medical Consumerism In The Digital Age, Yibing Hu

Finance Undergraduate Honors Theses

This paper discusses the role of telehealth in medical consumerism. As a cheap and convenient model of healthcare, telehealth enables patients to access healthcare regardless of location, as long as they have the necessary technology.

In the current healthcare landscape, insurance policies and high costs push patients to act as consumers of medical services. With the rise of telehealth as another healthcare option, the medical sector further becomes consumer-oriented. This ultimately changes the way patients interact with healthcare.

Mortgage Default Classification Modeling For Variable Analysis, Brendan R. Goggins

Honors College Theses

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables …

Kennesaw State University Student Managed Investment Fund Sector Sensitivity Analysis, John Kiersznowski, Joe Johnson, Kyler Howell, Geranger Lewis

Senior Design Project For Engineers

The Kennesaw State University Student Managed Investment Fund (SMIF) Sector Sensitivity Analysis focuses on improving the fund’s decision-making and performance through data science. The SMIF is a diversified index fund designed to outperform indices like the S&P 500. This project investigates how macroeconomic variables—such as GDP growth, inflation, interest rates, and commodity prices—impact sector performance. By structuring data, developing a sustainable data pipeline, and leveraging advanced statistical techniques and predictive modeling, our team was able to provide the framework and proof of actionable insights that enhance the fund's ability to manage risks and optimize returns.

Measuring Exposure Through Targeted Ads, Sarah Bazell

Honors Projects

This study investigates the effectiveness of targeted digital advertising strategies for Company X, a performance motorsports fuel brand. With the goal of optimizing engagement and improving budget allocation, four distinct ad campaigns were developed and tested on Facebook and Instagram: brand awareness, product education, dealer locator promotion, and lifestyle merchandise promotion. Each campaign was evaluated based on reach, impressions, cost per result, and conversion rates. Descriptive statistics revealed significant differences in engagement rates across campaigns, with video-based, emotionally driven ads outperforming static formats. A one-way ANOVA indicated no significant difference in reach across campaigns but confirmed significant differences in spending …

Ai Meets Economics: Can Deep Learning Surpass Machine Learning And Traditional Statistical Models In Inflation Time Series Forecasting?, Ezekiel N.N. Nortey, Edmund F. Agyemang, Enoch Sakyi-Yeboah, Obu-Amoah Ampomah, Louis Agyekum

School of Mathematical & Statistical Sciences Faculty Publications

This study examined the forecasting ability of deep learning (DL) and machine learning (ML) models against benchmark traditional statistical models for the monthly inflation rates in the USA. The study compared various DL and ML models like transformers, linear regression, gradient boosting (GB), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost) with traditional baseline time-series models like autoregressive integrated moving averages (ARIMA) and exponential smoothing (ETS) with Holt-Winters seasonal method utilizing data sourced from the Federal Reserve Bank of St. Louis. The study consistently showed that all DL and ML models outperformed the traditional approaches. In particular, the Transformer (RMSE …

Effect Of Street-Pricing Deregulation In U.S. Airports On Customer Satisfaction, Thorsten Merkle, Satheesh Seenivasan, Sushanta Das

ICHRIE Research Reports

This study investigates the impact of pricing policies and food and beverage (F&B) strategies on traveler satisfaction in the dynamic airport environment, with a focus on Phoenix airport in comparison with two other (undisclosed) major U.S. airports. Using a mixed-methods approach that integrates unstructured observations and social media analytics, the research provides a comprehensive understanding of passenger behavior, satisfaction, and spending patterns in airside F&B outlets.

Key findings reveal that traveler satisfaction is driven by factors such as service quality, timeliness, perceived value, and emotional engagement, with price sensitivity playing a relatively minor role. Phoenix International Airport exemplifies a successful …

Discovering Prompt Engineering: A Qualitative Study Of Nonexpert Teachers' Interactions With Chatgpt, Katherine Carl, Christopher A. Dignam

Research Days

Discussions of the use of artificial intelligence (AI) have become ubiquitous in research, industry, and education. Prompt engineering has emerged as a valuable skill desired by employers, and students have begun to express interest in learning effective ways of interacting with Generative AI. While much research has been done to formalize and optimize the process of prompt engineering, few studies have investigated the use of AI by nonexperts, including nonexpert teachers. In this study, we use qualitative analysis to characterize the process by which nonexpert teachers engage with and learn from ChatGPT as they engage with it as co-creators to …

Enhancing Animal Shelter Operations With Time Series And Machine Learning, Sakava L. Kiv, Donald L. Anderson, Shivam Negi, Jacquelyn Cheun

SMU Data Science Review

Enhancing animal shelter operations through machine learning involves employing a variety of advanced techniques aimed at increasing efficiency, promoting animal welfare, and optimizing resource allocation. This paper explores predictive analytics for adoption rates using regression models to estimate the likelihood of adoption based on historical data, encompassing variables such as breed, health status, and previous adoption trends. Additionally, classification algorithms are utilized to categorize animals by adoption probability, facilitating better resources and marketing prioritization. Clustering algorithms are employed to group animals according to behavior patterns and/or physical health, enabling tailored medical care and enrichment activities that improve their mental and …