Predictable forward utility processes under ambiguity

Jiale Du (), Shuo Li (), Fuguo Liu () and Yufeng Shi
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Jiale Du: Institute for Financial Studies and School of Mathematics, Shandong University, Jinan 250100, Shandong, China
Shuo Li: ��School of Mathematics and Data Sciences, Changji University, Changji 831100, Xinjiang, China
Fuguo Liu: ��School of Mathematics and Data Sciences, Changji University, Changji 831100, Xinjiang, China
Yufeng Shi: Institute for Financial Studies and School of Mathematics, Shandong University, Jinan 250100, Shandong, China‡National Center for Applied Mathematics of Shandong, Shandong University, Jinan 250100, Shandong, China

International Journal of Financial Engineering (IJFE), 2025, vol. 12, issue 02, 1-16

Abstract: Driven by the need for more effective decision-making tools amid market volatility and ambiguity, the authors aim to develop a robust and detailed forecasting model that enhances the understanding of market uncertainty and risk, providing investors with precise guidance for their decisions. To achieve this goal, we employ Bayesian functions to refine forward utility models, thereby improving market forecasting accuracy under uncertain conditions. This work contributes to the literature by introducing a more detailed method for estimating model parameters. The adaptability of this model allows for its application across various market scenarios, representing a significant advancement over traditional methods. In summary, this research deepens our understanding of market dynamics and equips investors with a more reliable tool for navigating uncertain financial landscapes.

Keywords: Portfolio selection; forward performance processes; binomial model; inverse investment problem; ambiguity model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S2424786325500148

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