Paper Type
Complete
Paper Number
PACIS2026-2095
Description
Stock market Decision Support Systems often lose predictive reliability during crises characterized by structural breaks and extreme volatility. Although prior reviews examine Artificial Intelligence (AI) based forecasting, crisis regimes, and financial applications separately, an integrated synthesis explaining how multimodal data fusion supports forecasting across diverse geographies remains limited. Addressing this gap, this study conducts a design-oriented systematic literature review of 72 peer-reviewed studies to examine how fusion-driven AI can achieve resilience across the data divide in developed and emerging markets. Findings reveal a strong focus on predictive artifacts that integrate market, news, and sentiment signals through hybrid architectures. The study applies a design-oriented framework and introduces three design principles that synthesize insights across the data divide, using fusion to guide resilient forecasting systems for both developed and emerging markets. The paper concludes with a research agenda prioritizing data infrastructure innovation, explainable fusion mechanisms, and governance frameworks for resilient stock forecasting.
Recommended Citation
Tiwary, Aaditya Chandra and Dixit, Gaurav, "Resilience Across the Data Divide: A Design-Oriented Systematic Review of Multimodal Fusion for Developed and Emerging Stock Market Forecasting under Crisis" (2026). PACIS 2026 Proceedings. 19.
https://aisel.aisnet.org/pacis2026/ai_ml/ai_ml/19
Resilience Across the Data Divide: A Design-Oriented Systematic Review of Multimodal Fusion for Developed and Emerging Stock Market Forecasting under Crisis
Stock market Decision Support Systems often lose predictive reliability during crises characterized by structural breaks and extreme volatility. Although prior reviews examine Artificial Intelligence (AI) based forecasting, crisis regimes, and financial applications separately, an integrated synthesis explaining how multimodal data fusion supports forecasting across diverse geographies remains limited. Addressing this gap, this study conducts a design-oriented systematic literature review of 72 peer-reviewed studies to examine how fusion-driven AI can achieve resilience across the data divide in developed and emerging markets. Findings reveal a strong focus on predictive artifacts that integrate market, news, and sentiment signals through hybrid architectures. The study applies a design-oriented framework and introduces three design principles that synthesize insights across the data divide, using fusion to guide resilient forecasting systems for both developed and emerging markets. The paper concludes with a research agenda prioritizing data infrastructure innovation, explainable fusion mechanisms, and governance frameworks for resilient stock forecasting.
Comments
01-AIML