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Optimal Portfolio Construction -- A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL-BHRP) Approach
Shaofeng Kang, Zeying Tian
https://arxiv.org/abs/2508.11856

Optimal Portfolio Construction -- A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL-BHRP) Approach
We propose a two-level, learning-based portfolio method (RL-BHRP) that spreads risk across sectors and stocks, and adjusts exposures as market conditions change. Using U.S. Equities from 2012 to mid-2025, we design the model using 2012 to 2019 data, and evaluate it out-of-sample from 2020 to 2025 against a sector index built from exchange-traded funds and a static risk-balanced portfolio. Over the test window, the adaptive portfolio compounds wealth by approximately 120 percent, compared with 1…

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
Qingliang Fan, Ruike Wu, Yanrong Yang
https://arxiv.org/abs/2507.16433 https://…

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization
Accurate transfer of information across multiple sectors to enhance model estimation is both significant and challenging in multi-sector portfolio optimization involving a large number of assets in different classes. Within the framework of factor modeling, we propose a novel data-adaptive multi-task learning methodology that quantifies and learns the relatedness among the principal temporal subspaces (spanned by factors) across multiple sectors under study. This approach not only improves the …

Quantum Leap in Finance: Economic Advantages, Security, and Post-Quantum Readiness
Gerhard Hellstern, Esra Yeniaras
https://arxiv.org/abs/2508.21548 https://

Quantum Leap in Finance: Economic Advantages, Security, and Post-Quantum Readiness
This paper provides an in-depth review of the evolving role of quantum computing in the financial sector, emphasizing both its computational potential and cybersecurity implications. Distinguishing itself from existing surveys, this work integrates classical quantum computing applications - such as portfolio optimization, risk analysis, derivative pricing, and Monte Carlo simulations with a thorough examination of blockchain technologies and post-quantum cryptography (PQC), which are crucial fo…

THEME : Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics
Hoyoung Lee, Wonbin Ahn, Suhwan Park, Jaehoon Lee, Minjae Kim, Sungdong Yoo, Taeyoon Lim, Woohyung Lim, Yongjae Lee
https://arxiv.org/abs/2508.16936

THEME : Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics
Thematic investing aims to construct portfolios aligned with structural trends, yet selecting relevant stocks remains challenging due to overlapping sector boundaries and evolving market dynamics. To address this challenge, we construct the Thematic Representation Set (TRS), an extended dataset that begins with real-world thematic ETFs and expands upon them by incorporating industry classifications and financial news to overcome their coverage limitations. The final dataset contains both the ex…