3 papers in the archive (2023–2025), average empirical rigor 5.7/10, average math complexity 5.3/10. Quadrant mix: Holy Grail ×2, Philosophers ×1.
Works mostly in Machine Learning for Market Forecasting, Crypto Markets & DeFi, Risk Management & Tail Risk.
Frequent co-authors: Zhuohang Zhu (2), Haodong Chen (2), Vera Chung (2), Xiaoming Chen (1), Seyed Mojtaba Hosseini Bamakan (1), Nasim Nezhadsistani (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%).
Financial time-series forecasting is critical for maintaining economic stability, guiding informed policymaking, and promoting sustainable investment practices. However, it remains challenging due to various underlying pattern shifts. These shifts arise primarily from three sources: temporal non-sta
Effective stock price forecasting (estimating future prices) and prediction (estimating future price changes) are pivotal for investors, regulatory agencies, and policymakers. These tasks enable informed decision-making, risk management, strategic planning, and superior portfolio returns. Despite th
With the explosive development of decentralized finance, we witness a phenomenal growth in tokenization of all kinds of assets, including equity, funds, debt, and real estate. By taking advantage of blockchain technology, digital assets are broadly grouped into fungible and non-fungible tokens (NFT)