Business Commons^™

Harnessing Technology To Solve Social Problems, Ian Hassell

SMU Human Trafficking Data Conference

No abstract provided.

Leveraging Aggregate Data For The Anti-Trafficking Movement: A National Strategy For Curating, Analyzing, And Visualizing Multiple Data Sources For The Field, John Nehme

SMU Human Trafficking Data Conference

No abstract provided.

Unified In Christ, David Morris

Doctor of Ministry Projects and Theses

Statement of the Question/Problem

The United Methodist Church is facing a season of incredible struggle. The modern American United Methodist Church is facing a crisis of Division during this season of disaffiliation and restructuring. Division within church is not a new phenomenon. In fact, it is not new in the Methodist tradition. This repeating cycle of denominational fracture begs the question, “why does division in the church happen, and how can we avoid it in the future?” Currently the United Methodist Church is dividing supposedly over the issue of human sexuality. I would argue these divisions do not solely rely …

Accounting For The Gift: Theology And Ethics In Accounting, Daniel Sebastian

Religious Studies Theses and Dissertations

Accounting is often assumed to be a neutral presentation of the facts of economic activities and actions. Its double-entry system means that it is always in balance and comports to the rigor of mathematical formulas, and it is taken to be a matter of empirical counting that lends it certainty as well. The dissertation argues that this description of accounting is inadequate. Accounting is better seen as a political tool and technology for producing trust that can help resolve social conflicts. As such, accounting is not value-neutral but carries within it a particular sociality that has moral implications. These moral …

Investigation Into A Practical Application Of Reinforcement Learning For The Stock Market, Philip Traxler, Sadik Aman, Will Rogers, Allyn Okun

SMU Data Science Review

A major problem of the financial industry is the ability to adapt their trading strategies at the same rate the market evolves. This paper proposes a solution using existing Reinforcement Learning libraries to help find new strategies at a practical scale. Using a wide domain of ticker symbols, an algorithm is trained in an environment that better represents reality. The supplied decision-making algorithm is tested using recorded data from the U.S stock market from 2000 through 2022. The results of this research show that existing techniques are statistically better than making decisions at random. With this result, this research shows …

Forecasting Accessory Demand In The Automotive Industry, Eric Cadena, Kevin Albright, Harry Wang, Satvik Ajmera

SMU Data Science Review

The automotive industry seeks effective ways to forecast consumer demand to avoid overstocking, waste, underproduction, and employee underperformance. Modeling future demand for vehicles is standard, however parts & accessories are a significant subset of overall automotive revenue. There is no industry standard for predicting the quantity of accessories sold or revenue. This paper seeks to use the best industry forecasting methods and research practices to build a predictive model that forecasts vehicle accessory sales. The time-series forecasting model utilizes Toyota Motor Corporation data in a first attempt to predict accessory sales.

Bridging The Chasm Between Fundamental, Momentum, And Quantitative Investing, Allen Hoskins, Jeff Reed, Robert Slater

SMU Data Science Review

A chasm exists between the active public equity investment management industry's fundamental, momentum, and quantitative styles. In this study, the researchers explore ways to bridge this gap by leveraging domain knowledge, fundamental analysis, momentum, crowdsourcing, and data science methods. This research also seeks to test the developed tools and strategies during the volatile time period of 2020 and 2021.

Following The Crowd: Beginners Investors Guide To The Options Market, Jeremy Dawkins, Alexy Morris, Jacob Gipson, Masoud Valizadeh

SMU Data Science Review

While the options market may be intimidating for a beginner, having the right tools can help improve the outcome of their investments. This project aims to develop a tool that uses time-series analysis and forecasting to model the future demand of S&P 500 and AAPL options contracts. The open interest of these contracts will be analyzed using various models such as AR, ARIMA, Neural Networks, and VAR, along with the put-call ratio. The goal is not to make buy or sell recommendations, but alert the user when money is flowing into a security or index. Of all the models, the …

Profiting From Dow Jones Industrial Index And Hang Seng Index Using Moving Average And Macd Optimization Model, Anthony Yeung, Joe Wailun Chung, Nibhrat Lohia, Onyeka Emmanuel

SMU Data Science Review

Before the internet, high-speed laptop computers, and big data became accessible and popular, academia on stock market trading concentrated on Efficient Market Hypothesis (EMH). EMH hinges on the idea that the market is efficient and there is no extra return that could be generated. With the dynamic development of the internet, big-data and computing technology, many researchers started to pay attention to Technical Analysis and its usage. Numerous academic papers claimed that technical analysis can enhance returns by using various technical tools. This paper explores in-depth the simulation model of Moving Average and Moving Average Convergence/Divergence (MACD) to come up …

Fraud Pattern Detection For Nft Markets, Andrew Leppla, Jorge Olmos, Jaideep Lamba

SMU Data Science Review

Non-Fungible Tokens (NFTs) enable ownership and transfer of digital assets using blockchain technology. As a relatively new financial asset class, NFTs lack robust oversight and regulations. These conditions create an environment that is susceptible to fraudulent activity and market manipulation schemes. This study examines the buyer-seller network transactional data from some of the most popular NFT marketplaces (e.g., AtomicHub, OpenSea) to identify and predict fraudulent activity. To accomplish this goal multiple features such as price, volume, and network metrics were extracted from NFT transactional data. These were fed into a Multiple-Scale Convolutional Neural Network that predicts suspected fraudulent activity based …

Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn

SMU Data Science Review

Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …

Analysis Of First-Time Completion In The Field Service Environment, Gavin Rick, Scott Englerth, Marc Carter, Hayley Horn

SMU Data Science Review

First-time completion is an important measure of service quality and efficiency in the field service industry. Customers call upon field service providers to repair their equipment in a timely manner so it can be put back into service for their business demands. Responsiveness can be measured through first-time completion and is defined as completing the repair on the first visit of a service call. This research is exploring the first-time completion in the forklift service industry. This research found the primary factors that impact first-time completion percentage in this industry include parts on hand, parts backorder process, technician experience, and …

Retail Landscape Changes May Affect Kroger-Albertsons Deal, Ed Fox, Emily Cotton, Laura O'Laughlin

Marketing Research

No abstract provided.

Study Of Stochastic Market Clearing Problems In Power Systems With High Renewable Integration, Saumya Sakitha Sashrika Ariyarathne

Operations Research and Engineering Management Theses and Dissertations

Integrating large-scale renewable energy resources into the power grid poses several operational and economic problems due to their inherently stochastic nature. The lack of predictability of renewable outputs deteriorates the power grid’s reliability. The power system operators have recognized this need to account for uncertainty in making operational decisions and forming electricity pricing. In this regard, this dissertation studies three aspects that aid large-scale renewable integration into power systems. 1. We develop a nonparametric change point-based statistical model to generate scenarios that accurately capture the renewable generation stochastic processes; 2. We design new pricing mechanisms derived from alternative stochastic programming …

A Machine Learning Approach To Revenue Generation Within The Professional Hair Care Industry, Alexander K. Sepenu, Linda Eliasen

SMU Data Science Review

The cosmetic and beauty industry continues to grow and evolve to satisfy its patrons. In the United States, the industry is heavily science-driven, innovative, and fast-paced, suggesting that to remain productive and profitable, companies must seek smart alternatives to their current modus operandi or risk losing out on this multi-billion-dollar industry to fierce competition. In this paper, the authors seek to utilize machine learning models such as clustering and regression to improve the efficiency of current sales and customer segmentation models to help HairCo (pseudonym for confidentiality), a professional hair products manufacturer, strategize their marketing and sales efforts for revenue …

Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun

SMU Data Science Review

This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …

Spotify: You Have A Hit!, Christopher E. Dawson Jr., Steve Mann, Edward Roske, Gauthier Vasseur

SMU Data Science Review

Abstract. Over 87% of the streaming music is owned by four major record labels (Jones, 2018). Yet, the songs owned by those labels account for <1% of the total amount of music created each year. These labels are historically better at identifying talent (though this talent identification is becoming more difficult). Even though Spotify has 36% of the streaming marketing share (T4, 2021), Spotify has not been profitable because of the large licensing costs paid to the large music labels. If Spotify could identify hit songs & artists before the large labels, they would sign those artists and dramatically reduce their licensing costs. Using the Spotify API, this paper will use Spotify data on over 400K songs over the last three years for exploratory data analysis, provide descriptive statistics, perform feature selection, and develop models using LASSO and XGBOOST Classification. The research determined multiple key features and predicted with over 60% accuracy songs which were going to be a hit (defined as >90% popularity).

Collaborative Filtering Based Generative Networks, Raghuram Srinivas

Computer Science and Engineering Theses and Dissertations

Collaborative Filtering, a popular method for recommendation engines, models its predictions using past interactions between the entities in question (aka users/movies or customers/products etc). The method does not rely on the explicit properties of the entities, the identification of which may be intractable. In this work, we leverage this advantage rendered by Collaborative Filtering where the explicit features need not be defined apriori by evaluating its application to the domain of Ligand based Virtual Screening. We further attempt to address the drawback of Collaborative Filtering , ie the lack of interpret ability of the factors discovered through collaborative filtering by …

Enhanced Data Science Methods For Freight Optimization At Kelly-Moore Paints, Lance Dacy, Reannan Mcdaniel, Shawn Jung

SMU Data Science Review

Kelly-Moore Paints is a paint manufacturing company founded in San Carlos, California in 1946 by William Kelly and William Moore. It has stores located in California, Texas, Oklahoma, and Nevada. They currently own 11 42’ trailers, contract 4 distinct drivers, and service 44 stores Monday-Thursday from its Texas Distribution and Manufacturing Center in Hurst, TX. Given that transportation costs are typically the highest in the supply chain costs, this study will employ data science techniques to ensure the transportation routing, store ordering mechanism, and trailer utilization are at the best efficiency possible given the current ordering patters of the stores. …

Modeling And Application Of Neural Networks For Automotive Damage Appraisals, Fred Poon, Yang Zhang, Jonathon Roach, David Josephs, John Santerre

SMU Data Science Review

The automotive damage appraisal process is one of the areas in property and casualty insurance that can benefit from applying deep learning technology and computer vision. It is commercially beneficial to introduce a fast and efficient claim process that can shorten the entire process. Technologies adopted include advanced neural network algorithm and Mask R-CNN to solve tasks such as image classification, object detection, and segmentation in combination with statistical analysis and model construction of the appraisal metadata to approximate final claim cost. With a database of over 3 million records as the data source, a workflow is constructed via a …

Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman

SMU Data Science Review

Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U.S. Government …

Analysis Of The Commercial Real Estate Market In A Post Covid-19 World, Brandon Croom, Sean Kennedy, Sandesh Ojha, Justin Sparks

SMU Data Science Review

The volatility in the commercial real estate market has been greatly influenced by the new societal practices brought about by the COVID-19 pandemic. The COVID-19 pandemic has added additional factors to already complex modeling to value and predict commercial real estate prices. Although multiple methodologies have been applied to commercial real estate valuation, these methods have not yet taken the COVID-19 pandemic factor into account. The main contribution of this article lies in developing an application for commercial real estate valuation which includes the COVID-19 pandemic factor. Thought this article a Hedonic model was developed to compare the impacts of …

Price Optimization For Revenue Maximization At Scale, Nikhil Gupta, Massimiliano Moro, Kailey A. Ayala, Bivin Sadler

SMU Data Science Review

This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy …

Bert For Question Answering On Bioasq, Eric R. Fu, Rikel Djoko, Maysam Mansor, Robert Slater

SMU Data Science Review

Machine reading comprehension and question answering are topics of considerable focus in the field of Natural Language Processing (NLP). In recent years, language models like Bidirectional Encoder Representations from Transformers (BERT) [3] have been very successful in language related tasks like question answering. The difficulty of the question answering task lies in developing accurate representations of language and being able to produce answers for questions. In this study, the focus is to investigate how to train and fine tune a BERT model to improve its performance on BioASQ, a challenge on large scale biomedical question answering. Our most accurate BERT …

Automated Interactive 3d Geospatial Data Assimilation, Formatting And Visualization System For Development Of Subsurface Conceptual Site Models, Aaron Cattley, Gavin Hudgeons, Bruce Lee

SMU Data Science Review

The natural evolution of the collection and storage of sub- surface data in Texas has resulted in the current state where data for certain resources, such as water resources, have not been assimilated with state oil and gas and injection data in a meaningful way that allows for rapid understanding and data analysis for a physical land site. The consequences that result due to data from different spheres not being in sync are often duplication of work being performed but not in a consistent manner. However, the reality is that the infrastructure and impacts of these sectors are deeply intertwined. …

Predicting Attrition - A Driver For Creating Value, Realizing Strategy, And Refining Key Hr Processes, Kevin Mendonsa, Maureen Stolberg, Vivek Viswanathan, Scott Crum

SMU Data Science Review

Talent is the most important asset for every organization's success. While attrition (or churn) and turnover can refer to both employees and customers, this paper will focus on employee attrition only. Many organizations accept attrition as an inevitable cost of doing business and do nothing to adopt or implement mitigating strategies to combat it. World class companies on the other hand take deliberate measures to understand, control and mitigate attrition (turnover) at every stage. Unmitigated attrition can have a devastating effect on an organization's bottom line and market value. In addition, the “invisible" costs of low employee morale, reduced employee …

Blockchain And Its Transformational Impact To Global Business, Mohammed Qaudeer

Operations Research and Engineering Management Theses and Dissertations

The advent of internet to the public back in 1994 resulted in the 4th industrial revolution disrupting and transforming business and communication models. As much as the transformation changed our lives and experiences, it has resulted in centralized models like Amazon and Facebook. It also resulted in exponential growth of Fraud, Identity theft, and lack of trust. Blockchain is considered an emerging technology of this era, which will trigger the 5th industrial revolution enabling another massive storm of disruptive transformation completely changing the current business models based on trust, security, collaboration and crypto currency. As the evolution of blockchain technology …

Developing And Sustaining Organizational Systems That Honor The Dignity Needs Of Stakeholders, Robyn Short

Graduate Liberal Studies Theses and Dissertations

At the time of the study, organizations were missing opportunities for innovation and bottom-line growth by failing to align performance management systems with basic human needs and dignity needs. Employees were missing opportunities for accessing their potential, and were failing to thrive. Organizational leaders needed to understand the importance of integrating basic human needs and the essentials of dignity as attributes of organizational systems design in order to maximize employee engagement, attract and retain top talent, increase innovation, and optimize bottom-line growth.

This research explored the role of dignity in organizational systems design. A qualitative research approach, specifically case study …

Toxic Comment Classification, Sara Zaheri, Jeff Leath, David Stroud

SMU Data Science Review

This paper presents a novel application of Natural Language Processing techniques to classify unstructured text into toxic and non- toxic categories. In the current century, social media has created many job opportunities and, at the same time, it has become a unique place for people to freely express their opinions. Meanwhile, among these users, there are some groups that are taking advantage of this framework and misuse this freedom to implement their toxic mindset (i.e. insulting, verbal sexual harassment, threads, Obscene, etc.). The 2017 Youth Risk Behavior Surveillance System (Centers for Disease Control and Prevention) estimated that 14.9% of high …

Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater

SMU Data Science Review

In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied …