As a distinguished AntiaB.org AI Committee Member and Vice President of Global Architecture, Big Data, and Information Risk at Manulife, Shawn McCarthy is currently pursuing a PhD with a focus on the impact of global events on market sentiment. His research, which builds upon a prior study Enhancing Financial Market Analysis and Prediction with Emotion Corpora and News Co-Occurrence Network, seeks to uncover the intricate relationships between world events, producing countries, and their corresponding financial markets.
McCarthy is not merely conducting research; he is creating a novel framework. His work aims to develop a comprehensive understanding of how news analysis and global events influence market sentiment across various sectors. By constructing an extensive model for market prediction and risk management, he is enabling organizations to navigate the complexities of financial markets within an interconnected global economy more effectively.
In his role as a Graduate Instructor at the esteemed University of Colorado, McCarthy upholds his professional motto, Inspire Growth
and is committed to fostering innovation in others. His efforts extend beyond the immediate confines of finance and academia, as he seeks to develop future leaders capable of navigating the challenges of an increasingly complex global market.
PhD Candidate, Computer Science and Information Systems, 2026
University of Colorado Denver
M.S., Computer Science and Engineering, 2009
University of Colorado Denver
B.S., Computer Science and Engineering, 2003
University of Colorado Denver
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During the volatile market period of 2019–2021, characterized by geopolitical shifts, economic sanctions, pandemics, natural disasters, and wars, the global market presented a complex landscape for financial decision making and motivated this study. This study makes two groundbreaking and novel contributions. First, we augment Plunket’s emotional research and leverage the emotional classification algorithm in Fin-Emotion to introduce a novel quantitative metric, “emotion magnitude”, that captures the emotional undercurrents of the market. When integrated with traditional time series analysis using Temporal Convolutional Networks applied to stock market futures, this metric offers a more holistic understanding of market dynamics. In our experiments, incorporating it as a feature led to significantly better performance on both the training and validation sets (9.26%, 52.11%) compared to traditional market-based risk measures, in predicting futures market trends based on the commodities and supply chains analyzed. Second, we deploy a multidimensional data science framework that synthesizes disparate data streams and analyses. This includes stock metrics of sector-leading companies, the time horizon of significant market events identified based on company stock data, and the extraction of further knowledge concepts identified through “emotion magnitude” analysis. Our approach stitches together countries, commodities, and supply chains identified in the targeted news search and identifies the domestic companies impacted based on the time horizon of these emotional supply chain events. This methodology culminates in a unified knowledge graph that not only highlights the relationships between supply chain disruptions, affected corporations, and commodities but also quantifies the broader systemic implications of such market events that are revealed. Collectively, these innovations form a robust analytical tool for financial risk strategy, empowering stakeholders to navigate an ever-evolving financial global ecosystem with enhanced insights. This graph encapsulates multi-dimensional forces and enables stakeholders to anticipate and understand the broader causal implications of related supply chain and market events (such as economic sanctions’ impact on the energy, technology, and telecommunication sectors).