Portfolio selection based on market states acquired via price and non-price data
Abstract Financial markets exhibit quasi-stationary patterns, known as market states, which previous studies have identified through the correlation structure of asset returns using clustering techniques. In this paper, we propose a novel approach to the portfolio selection problem by leveraging mar...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-04-01
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| Series: | Discover Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44248-025-00035-5 |
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| Summary: | Abstract Financial markets exhibit quasi-stationary patterns, known as market states, which previous studies have identified through the correlation structure of asset returns using clustering techniques. In this paper, we propose a novel approach to the portfolio selection problem by leveraging market states through an unsupervised method that extends beyond conventional correlation analysis. We introduce market distance metrics for both price and non-price data to estimate market states. Our proposed model allocates assets based on portfolio choices optimized within specified market states. In addition to using asset return correlations, we experiment with two non-price data sources: (1) a text-based market distance metric derived from Federal Open Market Committee (FOMC) policy statements, and (2) the correlation of multivariate time series search volumes from Google Trends. Using a multi-asset portfolio encompassing major U.S. asset classes, we trained our model from 2016 to 2019 and evaluated its out-of-sample performance from 2020 to 2023. Our findings demonstrate the superiority of our ensemble solution, incorporating multiple market distance metrics with price and non-price data, over established baselines with statistically significant results. |
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| ISSN: | 2731-6955 |