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Full-Text Articles in Business
A Hybrid Deep Learning Approach For Crude Oil Price Prediction, Hind Aldabagh, Xianrong Zheng, Ravi Mukkamala
A Hybrid Deep Learning Approach For Crude Oil Price Prediction, Hind Aldabagh, Xianrong Zheng, Ravi Mukkamala
Computer Science Faculty Publications
Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model …
Combining Machine-Based And Econometrics Methods For Policy Analytics Insights, Robert J. Kauffman, Kwansoo Kim, Sang-Yong Tom Lee, Ai Phuong Hoang, Jing Ren
Combining Machine-Based And Econometrics Methods For Policy Analytics Insights, Robert J. Kauffman, Kwansoo Kim, Sang-Yong Tom Lee, Ai Phuong Hoang, Jing Ren
Research Collection School Of Computing and Information Systems
Computational Social Science (CSS) has become a mainstream approach in the empirical study of policy analytics issues in various domains of e-commerce research. This article is intended to represent recent advances that have been made for the discovery of new policy-related insights in business, consumer, and social settings. The approach discussed is fusion analytics, which combines machine-based methods from Computer Science (CS) and explanatory empiricism involving advanced Econometrics and Statistics. It explores several efforts to conduct research inquiry in different functional areas of Electronic Commerce and Information Systems (IS), with applications that represent different functional areas of business, as well …
Accounting For Locational, Temporal, And Physical Similarity Of Residential Sales In Mass Appraisal Modeling: The Development And Application Of Geographically, Temporally, And Characteristically Weighted Regression, Paul E. Bidanset, Michael Mccord, John R. Lombard, Peadar Davis, William J. Mccluskey
Accounting For Locational, Temporal, And Physical Similarity Of Residential Sales In Mass Appraisal Modeling: The Development And Application Of Geographically, Temporally, And Characteristically Weighted Regression, Paul E. Bidanset, Michael Mccord, John R. Lombard, Peadar Davis, William J. Mccluskey
School of Public Service Faculty Publications
Geographically weighted regression (GWR) has been recognized in the assessment community as a viable automated valuation model (AVM) to help overcome, at least in part, modeling hurdles associated with location, such as spatial heterogeneity and spatial autocorrelation of error terms. Although previous researchers have adjusted the GWR weights matrix to also weight by time of sale or by structural similarity of properties in AVMs, the research described in this paper is the first that has done so by all three dimensions (i.e., location, structural similarity, and time of sale) simultaneously. Using 24 years of single-family residential sales in Fairfax, Virginia, …
Valuation Of Participation In Social Gaming, Kwansoo Kim, Byungjoon Yoo, Robert J. Kauffman
Valuation Of Participation In Social Gaming, Kwansoo Kim, Byungjoon Yoo, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
This study examines the value of the time that a user spends to participate in a social game. We focus on how a massive multiplayer online role-playing game (MMORPG) vendor can establish prices to encourage participation and retain its players. We estimate value through an application of the hedonic pricing model and analyze a data set for an MMORPG in Korea. The results permit us to estimate the value of game-playing time in monetary terms. Based on our empirical results, we propose an economic model and conduct numerical simulation to show how a game vendor can apply differential pricing in …
Firm Strategy And The Internet In U.S. Commercial Banking, K. H. Goh, Robert J. Kauffman
Firm Strategy And The Internet In U.S. Commercial Banking, K. H. Goh, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
As information technology (IT) becomes more accessible, sustaining any competitive advantage from it becomes challenging. This has caused some critics to dismiss IT as a less valuable resource. We argue that, in addition to being able to generate strategic advantage, IT should also be viewed as a strategic necessity that prevents competitive disadvantage in rapidly changing business environments. We test a set of hypotheses on strategic advantage and strategic necessity in the context of Internet banking investments among the entire population of the United States Federal Deposit Insurance Corporation (FDIC) banks from 2003 to 2005. We seek to understand whether …
Empirical Methods-A Review: With An Introduction To Data Mining And Machine Learning, Matt Bogard
Empirical Methods-A Review: With An Introduction To Data Mining And Machine Learning, Matt Bogard
Economics Faculty Publications
This presentation was part of a staff workshop focused on empirical methods and applied research. This includes a basic overview of regression with matrix algebra, maximum likelihood, inference, and model assumptions. Distinctions are made between paradigms related to classical statistical methods and algorithmic approaches. The presentation concludes with a brief discussion of generalization error, data partitioning, decision trees, and neural networks.