The Use Of Blockchain Technology To Solve Common Challenges In The Supply Chain, 2019 University of Southern Maine
The Use Of Blockchain Technology To Solve Common Challenges In The Supply Chain, Michael Cohen
Thinking Matters Symposium
When blockchain was first invented by Satoshi Nakamoto in 2008 it was thought to only be used for Bitcoin; a digital currency. It had been used to record transactions made without needing a third party authenticator. Blockchain showed the ability to reduce costs, speed up transactions, and reinvent the processes of how things are done. Once people fully understood what blockchain does, entrepreneurs and investors realized it could be used for much more than just a cryptocurrency. It could be applied to transportation, products sold, food, the medical industry, and much more.
With the help of blockchain, industries can operate ...
The Importance Of School District Quality In The Columbus Real Estate Market, 2019 Otterbein University
The Importance Of School District Quality In The Columbus Real Estate Market, Ryan Karapas
Honors Thesis Projects
Using a random sample of 100 houses sold between the months of October 2018 and January 2019 in the greater Columbus, Ohio area, this paper investigates the relationship between school district quality and the selling price of houses. The results show that school district quality has a positive and statistically significant influence on the Columbus real estate market. An increase of one letter grade in school district quality (calculated using output-related factors such as student performance) leads to a 13% increase in the selling price of a house. When using the average house price in my data set of $254 ...
Development And Protection Of Economic Competition In Kosovo: Case Study Gjilan Region, 2019 James Madison University
Development And Protection Of Economic Competition In Kosovo: Case Study Gjilan Region, Gani Asllani, Bedri Statovci, Gentiana Gega
International Journal on Responsibility
This paper investigates development and protection of economic competition in Kosovo focusing on the analysis of the level of competition in one region of Kosovo (the Gjilan region). The paper deals with the legislative aspects of competition, the sensitive sectors (banks, insurance, gas stations and pharmacies) where the competitions is damaged and finally are presented the measures on improvement based on the EU practices. Like other economies in transition, the economy in Kosovo the activity for protection of competition is faced with many challenges. Moreover, these challenges result from the fact that Kosovo was the last country in South Eastern ...
The Unfree Space Of Play: Emergence And Control In The Videogame And The Platform, 2019 Indiana University, Bloomington
The Unfree Space Of Play: Emergence And Control In The Videogame And The Platform, Logan Brown
Markets, Globalization & Development Review
This article attempts to understand the economic and informatic ramifications of the convergence between increasingly connective games and massive online platforms by considering recent trends in both that center around designing for emergence. Scholarship on emergence as a property of games overwhelmingly treats emergent design as a liberating force that privileges player agency in a virtual space. Yet, as games fuse with surrounding platform ecosystems like Steam, Facebook, and Google, those emergent behaviors are subject to vast systems of inscription that analyze user behavior in order to reshape the free space of emergence and extract greater social and financial capital ...
Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, 2019 Kennesaw State University
Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni
Jennifer L. Priestley
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and ...
Comparison Of Bankruptcy Prediction Models With Public Records And Firmographics, 2019 Kennesaw State University
Comparison Of Bankruptcy Prediction Models With Public Records And Firmographics, Lili Zhang, Jennifer Priestley, Xuelei Ni
Jennifer L. Priestley
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to study the impacts of public records and firmographics and predict the bankruptcy in a 12-month-ahead period with using different classification models and adding values to traditionally used financial ratios. Univariate analysis shows the statistical association and significance of public records and firmographics indicators with the bankruptcy. Further, seven statistical models and machine learning methods were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. The performance of models were evaluated and compared based on classification accuracy ...
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, 2019 Analytics and Data Science
A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.
Jennifer L. Priestley
Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.
Saving Lives With Effective Data Visualization: Evaluating The Effectiveness Of Indiana’S Driver Education Curriculum, 2019 Krannert School of Management
Saving Lives With Effective Data Visualization: Evaluating The Effectiveness Of Indiana’S Driver Education Curriculum, Aditi Vatse, Amratansh Sharma, Matthew A. Lanham Prof.
Engagement & Service-Learning Summit: Connecting Through Listening & Scholarship
No abstract provided.
An Investigation Of The Association Between Tourist Pre-Trip Planning Time And Length Of Trip, Lodging Choice, Tourist Psychographics And Demographics: An Application Of Correspondence Analysis And Cramér’S V Effect Size, 2019 Appalachian State University
An Investigation Of The Association Between Tourist Pre-Trip Planning Time And Length Of Trip, Lodging Choice, Tourist Psychographics And Demographics: An Application Of Correspondence Analysis And Cramér’S V Effect Size, James E. Stoddard, George D. Shows
Atlantic Marketing Association Proceedings
No abstract provided.
Making The Case For Global Outsourcing: Cases Of Business Complexities And Success, 2019 Robert Morris University
Making The Case For Global Outsourcing: Cases Of Business Complexities And Success, Alan D. Smith, Sara Krivacek
Atlantic Marketing Association Proceedings
No abstract provided.
Deskripsi Pekerjaan Seorang Pakar Seo, 2019 Bryant University
Deskripsi Pekerjaan Seorang Pakar Seo, Aan Web
Dos And Don'ts Of Data Science, 2019 Capital Services
Dos And Don'ts Of Data Science, Ryan Burton
SDSU Data Science Symposium
In an ideal world, we avoid all mistakes in our work. Some mistakes are preventable and others are unavoidable. A few common mistakes in data science that can be minimized include assuming correlation implies causation, modeling with an unrepresentative sample, and focusing on the mean without understanding the distribution. This talk will give an overview of some of the simple yet common mistakes in data science and guidance on how to avoid them.
Preventing Accounting Fraud, 2019 Singapore Management University
Preventing Accounting Fraud, Singapore Management University
Technology can help identify possible fraud but it might not overcome ‘capture theory’
Try, Try Again: Lessons Learned From Success And Failure In Participatory Modeling, 2019 American Museum of Natural History
Try, Try Again: Lessons Learned From Success And Failure In Participatory Modeling, Eleanor J. Sterling, Moira Zellner, Karen E. Jenni, Kirsten Leong, Pierre D. Glynn, Todd K. Bendor, Pierre Bommel, Klaus Hubacek, Antonie J. Jetter, Rebecca Jordan, Laura Schmitt Olabisi, Michael Paolisso, Steven Gray
Engineering and Technology Management Faculty Publications and Presentations
Participatory Modeling (PM) is becoming increasingly common in environmental planning and conservation, due in part to advances in cyberinfrastructure as well as to greater recognition of the importance of engaging a diverse array of stakeholders in decision making. We provide lessons learned, based on over 200 years of the authors’ cumulative and diverse experience, about PM processes. These include successful and, perhaps more importantly, not-so-successful trials. Our collective interdisciplinary background has supported the development, testing, and evaluation of a rich range of collaborative modeling approaches. We share here what we have learned as a community of participatory modelers, within three ...
Improving Vix Futures Forecasts Using Machine Learning Methods, 2019 Southern Methodist University
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
SMU Data Science Review
The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide ...
How Can California Best Promote Electric Vehicle Adoption? The Effect Of Public Charging Station Availability On Ev Adoption, Viraj Singh
Pomona Senior Theses
To promote higher air quality and reduce greenhouse gas emissions, the Californian government is investing heavily in developing public charging infrastructure to meet its electric vehicle adoption goal of five million zero-emission vehicles on the road by 2030. This thesis investigates the effect of public charging infrastructure availability on electric vehicle adoption at the zip code level in California. The analysis considers other factors that may influence electric vehicle adoption such as education level, income, commute time, gas prices, and public transportation rate. The findings suggest that public charging infrastructure availability does significantly positively correlate with electric vehicle registrations. Linear ...
Welfare Losses From First-Come-First-Serve Course Enrollment: Outcome Estimation And Non-Market Maximization, Rory Fontenot
CMC Senior Theses
College course enrollment operates as a market under supply cap. Because of the limited number of seats available for any given course some students who have a higher demand for a course are unable to enroll. The current registration system at the Claremont Colleges functions as a random draw system with added time costs. The lack of price signalling in the markets leads to a loss in overall welfare of the student body. By running data through simulated demand curves I am able to determine, on average, how much welfare is being lost by a random draw system. The percent ...
Are Women Executives Hurting Firm Performance? An Examination Of Gender Diversity On Firm Risk, Performance, And Executive Compensation, Krystal Diane Sung
CMC Senior Theses
In order to assess the continuing imbalance of top executives between genders, I examine the effects of gender diversity within top management teams on firm risk, performance, and executive compensation. Capitalizing on previous analysis, I apply three unique differentiators. First, I utilize current data from 2012 to 2017 from Compustat, CRSP, and ExecuComp. Second, I provide a unique subset view on a firm and individual performance of female CEOs to examine executive compensation. Third, my scope of analysis expands to S&P Composite 1500 companies. I use separate models to estimate the effect of gender diversity on firm risk by ...
Exploring Critical Success Factors For Data Integration And Decision-Making In Law Enforcement, 2019 Capitol Technology University
Exploring Critical Success Factors For Data Integration And Decision-Making In Law Enforcement, Marquay Edmondson, Walter R. Mccollum, Mary-Margaret Chantre, Gregory Campbell
International Journal of Applied Management and Technology
Agencies from various disciplines supporting law enforcement functions and processes have integrated, shared, and communicated data through ad hoc methods to address crime, terrorism, and many other threats in the United States. Data integration in law enforcement plays a critical role in the technical, business, and intelligence processes created by users to combine data from various sources and domains to transform them into valuable information. The purpose of this qualitative phenomenological study was to explore the current conditions of data integration frameworks through user and system interactions among law enforcement organizational processes. Further exploration of critical success factors used to ...
Social Justice, Numeracy, And Teaching Statistics At A Community College, 2019 Queensborough Community College/CUNY
Social Justice, Numeracy, And Teaching Statistics At A Community College, Edward Volchok
The author of this article reflects on the issues of justice, democracy, and numeracy. As one who has taught statistics in a community college for over 12 years, spent 28 years as a Marketing Consultant, and holds a PhD in political science, the author’s thesis is that while an advanced, democratic society can only be just with a numerate citizenry, fostering numeracy is not easy. In this article the author describes the daunting tasks of trying to define what justice is and reviews activities from his statistics class that help students develop their numeracy.