 Dynamic Portfolio Construction in High-Frequency Markets Using Microstructure-Aware Deep Temporal ModelsXiaoyi Meng Journal of Sustainability, Policy, and Practice, 2026, vol. 2, issue 2, 1-6 Abstract: High-frequency financial markets exhibit rapidly changing liquidity and strong nonstationarity, which reduce the effectiveness of portfolio methods based on static return and covariance estimates. This study presents a microstructure-aware framework for dynamic portfolio allocation that maps high-frequency market states directly to portfolio weights. The model uses order-flow intensity, depth imbalance, and volume asymmetry as key inputs and combines recurrent and convolutional temporal components to capture both persistent dependencies and short-lived microstructure shocks. Portfolio weights are determined under a joint return-risk objective. The framework is evaluated on ten years of high-frequency U.S. equity data. Results show higher risk-adjusted performance than mean-variance and risk-parity benchmarks, with a 14%-22% improvement in Sharpe ratio and more stable drawdown behavior. These findings suggest that integrating market microstructure information into temporal allocation models can improve exposure management in intraday portfolio optimization. Keywords: high-frequency trading; portfolio construction; market microstructure; deep temporal models; order flow; risk-adjusted performance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:dba:jsppaa:v:2:y:2026:i:2:p:1-6 Access Statistics for this article More articles in Journal of Sustainability, Policy, and Practice from Pinnacle Academic Press |
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