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The Emotion Magnitude Effect: Navigating Market Dynamics Amidst Supply Chain Events

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  • Shawn McCarthy

    (Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO 80204, USA
    These authors contributed equally to this work.)

  • Gita Alaghband

    (Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO 80204, USA
    These authors contributed equally to this work.)

Abstract

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).

Suggested Citation

  • Shawn McCarthy & Gita Alaghband, 2023. "The Emotion Magnitude Effect: Navigating Market Dynamics Amidst Supply Chain Events," JRFM, MDPI, vol. 16(12), pages 1-21, November.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:12:p:490-:d:1284611
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    References listed on IDEAS

    as
    1. Patton, Andrew J. & Ziegel, Johanna F. & Chen, Rui, 2019. "Dynamic semiparametric models for expected shortfall (and Value-at-Risk)," Journal of Econometrics, Elsevier, vol. 211(2), pages 388-413.
    2. Guy Burstein & Inon Zuckerman, 2023. "Deconstructing Risk Factors for Predicting Risk Assessment in Supply Chains Using Machine Learning," JRFM, MDPI, vol. 16(2), pages 1-16, February.
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