# Bayesian Portfolio Optimization by Predictive Synthesis

arXiv: https://arxiv.org/abs/2510.07180 · HTML: https://mlsanity.com/paper/2510.07180/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [Dynamic Bayesian Predictive Synthesis in Time Series Forecasting](https://mlsanity.com/paper/1601.07463/) (2016)
- [Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting](https://mlsanity.com/paper/1711.01667/) (2017)
- [Bayesian Predictive Decision Synthesis](https://mlsanity.com/paper/2206.03815/) (2022)

## Built on by (most cited first, 1 of 1)

- [General Bayesian Policy Learning](https://mlsanity.com/paper/2602.23672/) (2026)

## Abstract (arXiv)

Portfolio optimization is a critical task in investment. Most existing portfolio optimization methods require information on the distribution of returns of the assets that make up the portfolio. However, such distribution information is usually unknown to investors. Various methods have been proposed to estimate distribution information, but their accuracy greatly depends on the uncertainty of the financial markets. Due to this uncertainty, a model that could well predict the distribution information at one point in time may perform less accurately compared to another model at a different time. To solve this problem, we investigate a method for portfolio optimization based on Bayesian predictive synthesis (BPS), one of the Bayesian ensemble methods for meta-learning. We assume that investors have access to multiple asset return prediction models. By using BPS with dynamic linear models to combine these predictions, we can obtain a Bayesian predictive posterior about the mean rewards of assets that accommodate the uncertainty of the financial markets. In this study, we examine how to construct mean-variance portfolios and quantile-based portfolios based on the predicted distribution information.