3 papers in the archive (2025), average empirical rigor 8.3/10, average math complexity 6.7/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Machine Learning for Market Forecasting, Factor Investing & Asset Pricing, Risk Management & Tail Risk, Portfolio Optimization.
Frequent co-authors: Zihan Lin (2), Haojie Liu (2), Peilin Rao (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%).
This paper provides robust, new evidence on the causal drivers of market troughs. We demonstrate that conclusions about these triggers are critically sensitive to model specification, moving beyond restrictive linear models with a flexible DML average partial effect causal machine learning framework
This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management. Using daily stock data from the S&P 500 (2020-2024), we construct dependency networks via Vector Autoregression (VAR) and Forecast Error
This study integrates real-time sentiment analysis from financial news, GPT-2 and FinBERT, with technical indicators and time-series models like ARIMA and ETS to optimize S&P 500 trading strategies. By merging sentiment data with momentum and trend-based metrics, including a benchmark buy-and-hold a