3 papers in the archive (2025–2026), average empirical rigor 7.2/10, average math complexity 7.5/10, 1 with linked code. Quadrant mix: Holy Grail ×3.
Works mostly in Portfolio Optimization, Factor Investing & Asset Pricing, Machine Learning for Market Forecasting, Reinforcement Learning for Trading.
Frequent co-authors: Chi-Sheng Chen (2), Kexin Wang (1), Xiaomeng Zhang (1), En-Jui Kuo (1), Rong Fu (1), Qiuzhe Xie (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Hybrid quantumclassical models are increasingly proposed for nancial prediction, but reported gains are rarely separated from the classical components around them. We study a hybrid quantum temporal convolutional network for daily cross-sectional ranking of 2000 Tokyo Stock Exchange equities under a
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, QRW
Recognizing that asset markets generally exhibit shared informational characteristics, we develop a portfolio strategy based on transfer learning that leverages cross-market information to enhance the investment performance in the market of interest by forward validation. Our strategy asymptotically