Business Intelligence Commons^™

Does Green Pay Less? Global Corporate Bond Evidence On Primary And Secondary Yields, Youssef El Kenawy

Theses and Dissertations

The greenium, or green premium, refers to the lower yield that arises from a bond’s green label, conditional on otherwise identical contractual features and credit risk. In our study, we estimate the greenium by combining causal matching techniques with a neural network–based propensity score approach to construct a closely comparable set of green and conventional bonds. Our empirical framework incorporates issuer fixed effects and currency × issuance-year fixed effects, ensuring that our estimates reflect the impact of the green label itself rather than differences in macro-financial conditions or issuer composition.

Our findings indicate that, once currency-specific issuance-year conditions are absorbed, …

Multi-Theoretic Perspective Of Backer Decision-Making In Crowdfunding: Interplay Of Backer, Founder And Ai, Maliha Alam

Theses and Dissertations

Crowdfunding has become a transformative mechanism for financing innovation, connecting creators directly with backers while bypassing traditional funding barriers. Despite its rapid global growth, only about 33.7% of campaigns succeed, largely due to ineffective communication of value propositions and lack of backer engagement. This dissertation investigates the complex interplay between project founders, backers, and emerging AI technologies to understand the factors influencing donation intention and to enhance crowdfunding success prediction.

Adopting the behavioral decision-making framework, I draw on multiple theoretical perspectives- signaling theory, the dual-process model of persuasion, and the cognitive-affective trust framework to examine founder- backer interactions. Using a …

Two Essays In Finance: Can Machines Better Predict Insider Trading? And Csr And Firm Value Following Dividend Cuts: International Evidence, Solmaz Batebi

Theses and Dissertations

This dissertation consists of two essays. In the first paper, I find that machine learning (ML) models predict the likelihood and magnitude of insider trading significantly better than linear models such as OLS and logistic regression. I use ML models, including LASSO, Random Forest, and eXtreme Gradient Boosting, optimizing model parameters through Bayesian hyperparameter tuning to identify the best configuration. Additionally, I apply SHAP values to better understand the determinants of insider trading. I also use Gaussian Thompson Sampling (GTS) to explore the sources of insiders’ market-timing capabilities. I find that ML models can boost the R² for models predicting …

Using Hybrid Machine Learning Models For Stock Price Forecasting And Trading., Ahmed Khalil

Theses and Dissertations

Trading stocks of publicly traded companies in stock markets is a challenging topic since investors are researching what tools can be used to maximize their profits while minimizing risks, which encouraged all researchers to research and test different methods to reach such a goal. As a result, the use of both fundamental analysis and technical analysis started to evolve to support traders in buying and selling stocks. Recently, the focus increased on using Machine learning models to predict stock prices and algorithmic trading as currently there is a huge amount of data that can be processed and used to forecast …

Enhancing Marketing Education Through Gamification: Learners’ Characteristics And Motivation In Gamification Strategies, Jagannath Kharel

Theses and Dissertations

The study investigates how the personality traits of marketing students influence their engagement with gamification elements and the resulting learning outcomes. Additionally, it explores how gamification, guided by Self-Determination Theory, enhances these outcomes, and motivates university marketing students. One hundred eleven respondents participated, testing eight hypotheses: seven examined the Big Five traits' influence on attitudes and engagement. At the same time, the final used the self-determination framework to analyze motivation in gamification. Respondents with openness and extraversion favored using gamified learning materials, with no gender differences observed. The study found that gamified learning elements, such as rewards, feedback, levels, challenges, …

Developing A Model-Based Approach To Forecast A Competitor's System, Christopher A. Del Vecchio

Theses and Dissertations

The purpose of this research is to develop a model-based approach to intelligence forecasting of a competitor’s system. This analysis currently uses a document-based practice to capture all knowledge of the forecast and its development. A framework of antithesis processes, or Anti-Processes, were derived from the systems engineering technical processes. This was then combined with analytical tradecraft from the field of competitive technical intelligence to build a SysML reference model, which was then applied to a small case study to enhance and refine the model. The Anti-Process framework and SysML reference model provide a rigorous, model-based approach to intelligence forecasts …

Stock Market Manipulation Detection Using Continuous Wavelet Transform & Machine Learning Classification, Sarah Youssef

Theses and Dissertations

Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the confidence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market efficiency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it's applications in this domain. The benefit of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock price …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Effective Use Of Data Analytics And Its Impact On Business Performance Within Small-To-Medium-Sized Businesses, Alfonso Berumen

Theses and Dissertations

Business use of data analytics and its potential impact on firm performance have become topics of deep interest within both the business practitioner and academic communities. While previous research has demonstrated relationships between data analytics and firm performance in larger firms, there is limited research on whether and how data analytics is used within and impacts Small-to-Medium-sized Business (SMB) settings. Given the preponderance of SMBs within the US economy, and their contribution to employment and economic activity, it is important for SMB owners to understand what management practices lead to effective use of data analytics that in turn impacts SMB …

A Novel Framework For Social Internet Of Things: Leveraging The Friendships And The Services Exchanged Between Smart Devices, Javad Abed

Theses and Dissertations

As humans, we tackle many problems in complex societies and manage the complexities of networked social systems. Cognition and sociability are two vital human capabilities that improve social life and complex social interactions. Adding these features to smart devices makes them capable of managing complex and networked Internet of Things (IoT) settings.

Cognitive and social devices can improve their relationships and connections with other devices and people to better serve human needs. Nowadays, researchers are investigating two future generations of IoT: social IoT (SIoT) and cognitive IoT (CIoT). This study develops a new framework for IoT, called CSIoT, by using …

Towards Developing A Goal-Driven Data Integration Framework For Counter-Terrorism Analytics, Dapeng Liu

Theses and Dissertations

Terrorist attacks can cause massive casualties and severe property damage, resulting in terrorism crises surging across the world; accordingly, counter-terrorism analytics that take advantage of big data have been attracting increasing attention. The knowledge and clues essential for analyzing terrorist activities are often spread across heterogeneous data sources, which calls for an effective data integration solution. In this study, employing the goal definition template in the Goal-Question-Metric approach, we design and implement an automated goal-driven data integration framework for counter-terrorism analytics. The proposed design elicits and ontologizes an input user goal of counter-terrorism analytics; recognizes goal-relevant datasets; and addresses semantic …

Good Game, Greyory Blake

Theses and Dissertations

This thesis and its corresponding art installation, Lessons from Ziggy, attempts to deconstruct the variables prevalent within several complex systems, analyze their transformations, and propose a methodology for reasserting the soap box within the display pedestal. In this text, there are several key and specific examples of the transformation of various signifiers (i.e. media-bred fear’s transformation into a political tactic of surveillance, contemporary freneticism’s transformation into complacency, and community’s transformation into nationalism as a state weapon). In this essay, all of these concepts are contextualized within the exponential growth of new technologies. That is to say, all of these semiotic …

A Web Personalization Artifact For Utility-Sensitive Review Analysis, Long Flory Mrs.

Theses and Dissertations

Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual assessment of review utility is not only time consuming …