2024/04/30 by Emily Tallman, Tallman, Emily, Mike West +1 · 1 citation
Computer Science · #62C05 #62F15 #Applications (stat.AP) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #FOS: Economics and business #Methodology (stat.ME) #Portfolio Management (q-fin.PM) #Semantic Web and Ontologies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2405.01598
openalex publication_date 2024/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We discuss and develop Bayesian dynamic modelling and predictive decision synthesis for portfolio analysis. The context involves model uncertainty with a set of candidate models for financial time series with main foci in sequential learning, forecasting, and recursive decisions for portfolio reinvestments. The foundational perspective of Bayesian predictive decision synthesis (BPDS) defines novel, operational analysis and resulting predictive and decision outcomes. A detailed case study of BPDS in financial forecasting of international exchange rate time series and portfolio rebalancing, with resulting BPDS-based decision outcomes compared to traditional Bayesian analysis, exemplifies and highlights the practical advances achievable under the expanded, subjective Bayesian approach that BPDS defines.