用贝叶斯融合多模型预测，动态优化投资组合
Bayesian Portfolio Optimization by Predictive Synthesis
- 通过贝叶斯预测合成融合多个资产收益预测模型
- 在真实市场波动下，显著提升收益分布预测准确性
- 适合追求稳健投资策略的量化交易与资管机构
投资组合优化是金融领域关键任务。现有方法大多依赖资产收益分布信息，但投资者通常无法获得。虽已有多种估计方法，但其精度受金融市场不确定性影响显著：某一时点表现优异的模型，另一时点可能失效。为此，本文研究基于贝叶斯预测合成（BPS）的组合优化方法，属于贝叶斯集成学习中的元学习技术。假设投资者拥有多个资产收益预测模型，通过将BPS与动态线性模型结合，可得到包含市场不确定性的资产均值收益贝叶斯后验分布。本研究探讨如何基于该分布构建均值-方差组合与分位数组合，实现更鲁棒的投资决策。
原文摘要 · Abstract (English)
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.
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