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- All Subjects: Finance
- All Subjects: Machine Learning
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
- Status: Published
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Company X has developed minicomputing products that can change the way people think about minicomputer. The Product A (PRODUCT A) and Product B are relatively new products on the market that have the ability to change the way some industries use technology and increase end-user convenience. The key issue for Company X is finding targeted use cases to which Company X can market these products and increase sales. This thesis reports how our team has researched, calculated, and financially forecasted use cases for both the PRODUCT A and Product B. The Education and Healthcare industries were identified as those providing significant potential value propositions and an array of potential use cases from which we could choose to evaluate. Key competitors, market dynamics, and information obtained through interviews with a Product Line Analyst were used to size the available, obtainable, and attainable market numbers for Company X. The models built for this research provided insight into the PRODUCT A and Product B's potential growth in the education and healthcare industries. This led to the selection of education and healthcare use cases for the Product B and the PRODUCT A use cases for healthcare. This report concludes with recommendations for success in education and healthcare with the PRODUCT A and Product B.
ContributorsHoward, James (Co-author) / Kazmi, Abbas (Co-author) / Ralston, Nicholas (Co-author) / Salamatin, Mikkaela Alexis (Co-author) / Simonson, Mark (Thesis director) / Hopkins, David (Committee member) / W.P. Carey School of Business (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis aims to promote financial literacy in the community. It was driven by the realization that there was a lack of basic financial knowledge among people at ASU and beyond. The people involved in the reason for the guide had all heard of bonds and understood the basic concepts, but lacked the knowledge of the finite details. The research starts with an overview of the United States bond market and focuses on the creation of a short simple guide. The goal is that anyone can read the guide and have a basic understanding of bonds, talk to financial managers, and do some basic investing. The easy guide is basically a two-page crash course on investing in bonds. Anyone can take a class or watch a video on bonds, but how do they actually start investing in them? This thesis works to answer this question by providing knowledge of real world application. The goal is to take knowledge beyond a book or video and learn from actively investing in a safe and clear way. Bonds are a very useful tool in investing and provide safe returns. The investing proposed is one that would be an alternative to putting money into a savings account. The guide recommends a good starting point of a way to invest in bonds (Specifically the US Treasury). At the same time does some analysis on other investing options for more advanced investors. The work includes an analysis of five bond portfolios and the calculations of finding their actual returns after loads and other fees.
ContributorsIrwin, Carter E. (Author) / Pruitt, Seth (Thesis director) / Schreindorfer, David (Committee member) / W.P. Carey School of Business (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Abstract: The Ultimate Fighting Championship or UFC as it is commonly known, was founded in 1993 and has quickly built itself into the world's foremost authority on all things MMA (mixed martial arts) related. With pay-per-view and cable television deals in hand, the UFC has become a huge competitor in the sports market, rivaling the popularity of boxing for almost a decade. As with most other sports, the UFC has seen an influx of various analytics and data science over the past five to seven years. We see this revolution in football with the broadcast first down markers, basketball with tracking player movement, and baseball with locating pitches for strikes and balls, and now the UFC has partnered with statistics company Fightmetric, to provide in-depth statistical analysis of its fights. ESPN has their win probability metrics, and statistical predictive modeling has begun to spread throughout sports. All these stats were made to showcase the information about a fighter that one wouldn't typically know, giving insight into how the fight might go. But, can these fights be predicted? Based off of the research of prior individuals and combining the thought processes of relevant research into other sports leagues, I sought to use the arsenal of statistical analyses done by Fightmetric, along with the official UFC fighter database to answer the question of whether UFC fights could be predicted. Specifically, by using only data that would be known about a fighter prior to stepping into the cage, could I predict with any degree of certainty who was going to win the fight?
ContributorsMoorman, Taylor D. (Author) / Simon, Alan (Thesis director) / Simon, Phil (Committee member) / W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05