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.

翻译：我们讨论并发展了用于投资组合分析的贝叶斯动态建模与预测决策综合。该研究背景涉及模型不确定性，针对金融时间序列构建候选模型集合，重点关注序列学习、预测以及投资组合再投资的递归决策。贝叶斯预测决策综合（BPDS）的基础视角定义了新颖的操作性分析及其产生的预测与决策结果。本文通过国际汇率时间序列的金融预测与投资组合再平衡的详细案例研究，将基于BPDS的决策结果与传统贝叶斯分析进行对比，实例化并凸显了BPDS所定义的扩展主观贝叶斯方法所能实现的实践性进展。