Sector Rotation

Analysis of Contagion in China’s Stock Market: A Hawkes Process Perspective ArXiv ID: 2512.08000 “View on arXiv” Authors: Junwei Yang Abstract This study explores contagion in the Chinese stock market using Hawkes processes to analyze autocorrelation and cross-correlation in multivariate time series data. We examine whether market indices exhibit trending behavior and whether sector indices influence one another. By fitting self-exciting and inhibitory Hawkes processes to daily returns of indices like the Shanghai Composite, Shenzhen Component, and ChiNext, as well as sector indices (CSI Consumer, Healthcare, and Financial), we identify long-term dependencies and trending patterns, including upward, downward, and oversold rebound trends. Results show that during high trading activity, sector indices tend to sustain their trends, while low activity periods exhibit strong sector rotation. This research models stock price movements using spatiotemporal Hawkes processes, leveraging conditional intensity functions to explain sector rotation, advancing the understanding of financial contagion. ...

Optimal Portfolio Construction – A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL-BHRP) Approach ArXiv ID: 2508.11856 “View on arXiv” Authors: Shaofeng Kang, Zeying Tian Abstract 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 101 percent for the static comparator and 91 percent for the sector benchmark. The average annual growth is roughly 15 percent, compared to 13 percent and 12 percent, respectively. Gains are achieved without significant deviations from the benchmark and with peak-to-trough losses comparable to those of the alternatives, indicating that the method adds value while remaining diversified and investable. Weight charts show gradual shifts rather than abrupt swings, reflecting disciplined rebalancing and the cost-aware design. Overall, the results support risk-balanced, adaptive allocation as a practical approach to achieving stronger and more stable long-term performance. ...

Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market ArXiv ID: 2506.20930 “View on arXiv” Authors: Chi-Sheng Chen, Xinyu Zhang, Ya-Chuan Chen Abstract We propose a hybrid quantum-classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both classical architectures (LSTM, Transformer) and quantum-enhanced models (QNN, QRWKV, QASA) as policy and value networks. An automated feature engineering pipeline extracts financial indicators from capital share data to ensure consistent model input across all configurations. Empirical backtesting reveals a key finding: although quantum-enhanced models consistently achieve higher training rewards, they underperform classical models in real-world investment metrics such as cumulative return and Sharpe ratio. This discrepancy highlights a core challenge in applying reinforcement learning to financial domains – namely, the mismatch between proxy reward signals and true investment objectives. Our analysis suggests that current reward designs may incentivize overfitting to short-term volatility rather than optimizing risk-adjusted returns. This issue is compounded by the inherent expressiveness and optimization instability of quantum circuits under Noisy Intermediate-Scale Quantum (NISQ) constraints. We discuss the implications of this reward-performance gap and propose directions for future improvement, including reward shaping, model regularization, and validation-based early stopping. Our work offers a reproducible benchmark and critical insights into the practical challenges of deploying quantum reinforcement learning in real-world finance. ...

Sector Rotation by Factor Model and Fundamental Analysis ArXiv ID: 2401.00001 “View on arXiv” Authors: Unknown Abstract This study presents an analytical approach to sector rotation, leveraging both factor models and fundamental metrics. We initiate with a systematic classification of sectors, followed by an empirical investigation into their returns. Through factor analysis, the paper underscores the significance of momentum and short-term reversion in dictating sectoral shifts. A subsequent in-depth fundamental analysis evaluates metrics such as PE, PB, EV-to-EBITDA, Dividend Yield, among others. Our primary contribution lies in developing a predictive framework based on these fundamental indicators. The constructed models, post rigorous training, exhibit noteworthy predictive capabilities. The findings furnish a nuanced understanding of sector rotation strategies, with implications for asset management and portfolio construction in the financial domain. ...