AIQF Weekly digest — 49 new papers

The week in numbers

Max drawdown -2.4%, -7.0% at market β · 21 strategies live · as of 2026-10-09

At market β restates the record at the market's beta — the same strategies held at 3.70× the exposure: the return is multiplied by that factor, the drawdown re-measured on the scaled line, and Sharpe is unchanged by it. Arithmetic on the record, not a second track record — no leverage was used and no financing cost is modelled.

Quant Paper Radar · 5 of 49

1. 1

   Conditional value-at-risk under reward-penalty mechanism with applications to robust portfolio management

   Jun Cai, Tiantian Mao, Zhiqiao Song · ARXIV · Robust portfolio optimization

   we backtested it — Sharpe 0.97 · quant relevance 0.98

   The paper proposes a distributionally robust portfolio-allocation method that minimizes worst-case CVaR while accounting for returns and losses beyond a threshold. In experiments on real market data, its portfolios outperform several comparator models.

   original paper

2. 2

   Enhancing Numerical Stability in Portfolio Optimization via Numerical Rank: The $$\delta $$-Stable Part of the Efficient Frontier

   Claudia Fassino, Pierpaolo Uberti · OPENALEX · Portfolio Optimization

   we backtested it — Sharpe 0.66 · quant relevance 0.96

   The paper limits mean-variance portfolio targets to a numerically stable portion of the efficient frontier. It compares out-of-sample risk-adjusted returns after transaction costs and reports evidence on allocation stability.

   original paper

3. 3

   Portfolio Optimization Under Heavy Tails and Asymmetric Volatility: Evidence from Taiwan-Exposed ETFs

   Ting-Jung Lee, Abootaleb Shirvani, Farzana Afroz et al. · OPENALEX · Tail-risk portfolio optimization

   we backtested it — Sharpe 0.37 · quant relevance 0.96

   The study compares mean–variance and CVaR allocations for U.S.-listed ETFs with Taiwan and semiconductor exposure, using daily returns to assess tail risk and asymmetric volatility. It reports historical performance rankings, though its concentrated CVaR allocations may depend on the sample.

   original paper

4. 4

   AlphaPADI: Formulaic Alpha Discovery via Pool-Aware Hierarchical Discrete Diffusion

   Yanzheng Jin, Pengyang Shao, Yunshan Ma et al. · ARXIV · Machine-Learned Alpha Discovery

   quant relevance 1.00

   AlphaPADI builds pools of complementary symbolic signals to predict cross-sectional stock returns. The authors report improved predictive and portfolio performance in Chinese and U.S. equities; the U.S. component can be tested with the available data.

   original paper

5. 5

   A Price-Based Framework for Stochastic Portfolio Theory

   Jongbong An, Donghan Kim · ARXIV · Functional portfolio generation

   we backtested it — Sharpe 0.38 · quant relevance 0.94

   The paper constructs price-weight-based equity portfolios that account explicitly for stock splits and compares their measured performance with capitalization-weight-based portfolios on NYSE data. The portfolio rules have a direct investing application, but the 2002–2021 sample cannot be reproduced in full.

   original paper

49 new papers written up in this period. Open the Quant Paper Radar →