Daily digest — 3 new papers

Quant Paper Radar · 3 new

1. 1

   An interpretable machine learning framework for predicting next day firm level market stress in the Dhaka Stock Exchange

   Md Sadman Haque, Mohammad Sameer Ahmed, Md Tasfikur Rahman et al. · OPENALEX

   we backtested it — Sharpe 0.47 · quant relevance 0.86

   The paper builds leakage-aware classifiers to predict next-day firm-level stress and crash risk using technical price, volatility, and volume features. It does not present a tradable portfolio backtest, but the predictions can inform equity exposure reduction, position sizing, and risk-aware trading decisions.

   original paper

2. 2

   The impact of climate risk attention on excess returns of actively managed funds

   Kaisheng Yang, Ximan An · OPENALEX

   quant relevance 0.90

   The paper links a Word2Vec-derived measure of climate-risk attention in Chinese fund portfolios to excess returns and Brinson allocation and selection attribution for actively managed Chinese equity funds. The analysis is relevant to investment research but cannot be reproduced or backtested using the platform's US-focused prices, fundamentals, news, and available fund-related data.

   original paper

3. 3

   Do fossil energy prices and climate policy uncertainty affect clean energy markets? Insights for financial sustainable development

   Ali Nawaz, Chi‐Wei Su, Ran Tao et al. · OPENALEX

   we backtested it — Sharpe 0.07 · quant relevance 0.78

   The study tests whether fossil-energy prices and climate-policy uncertainty predict or condition clean-energy equity performance. Its findings could support a tactical allocation or risk-overlay signal for clean-energy ETFs, although the paper establishes statistical dependence rather than testing a trading strategy directly.

   original paper