Masahiro Kato, Kentaro Baba, Hibiki Kaibuchi, Ryo Inokuchi
arXiv 8 Oct 2025 · Econometrics
arXiv:2510.07180 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kenichiro McAlinn, Knut Are Aastveit, Jouchi Nakajima, and Mike West (2020) Multivariate bayesian predictive synthesis in macroeconomic forecasting | 0.874 | 5 | 2 | 100% |
| 2 | Kenichiro McAlinn and Mike West (2019) Dynamic bayesian predictive synthesis in time series forecasting | 0.737 | 3 | 2 | 100% |
| 3 | Emily Tallman and Mike West (2023) Bayesian predictive decision synthesis, 2023 | 0.737 | 3 | 2 | 100% |
| 4 | Taras Bodnar, Mathias Lindholm, Vilhelm Niklasson, and Erik Thors^^c… (2020) Bayesian quantile-based portfolio selection, 2020 | 0.644 | 4 | 1 | 100% |
| 5 | Taras Bodnar, Mathias Lindholm, Erik Thorsén, and Joanna Tyrcha (2021) Quantile-based optimal portfolio selection | 0.511 | 2 | 1 | 100% |
| 6 | R. Prado and M. West (2010) Time Series: Modelling, Computation & Inference | 0.511 | 2 | 1 | 100% |
| 7 | M. West and P. J. Harrison (1997) Bayesian Forecasting & Dynamic Models | 0.511 | 2 | 1 | 100% |
| 8 | Nestor Parolya David Bauder, Taras Bodnar and Wolfgang Schmid (2021) Bayesian mean^^e2^^80^^93variance analysis: optimal portfolio selection under parameter uncertainty | 0.405 | 1 | 1 | 100% |
| 9 | Vijay Chopra and William Ziemba (1993) The effect of errors in means, variances, and covariances on optimal portfolio choice | 0.405 | 1 | 1 | 100% |
| 10 | Harry Markowitz (1952) Portfolio selection | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | General Bayesian Policy Learning | 0.511 | 2 | 2 |
| 2 | Conformal Predictive Portfolio Selection | 0.405 | 1 | 1 |