AIQF Daily digest — 6 new papers

Quant Paper Radar · 4 of 6

1. 1

   Entropic Value-at-Risk parity for tempered stable returns

   Jaehyung Choi · ARXIV

   we backtested it — Sharpe 0.37 · quant relevance 0.98

   The paper constructs long-only inverse-risk-parity and equal-risk-contribution portfolios using Entropic Value-at-Risk under tempered-stable return models. Across ETF and portfolio universes, its EVaR-ERC portfolios produced positive Sharpe-ratio differences relative to equal-weight and Gaussian-EVaR benchmarks.

   original paper

2. 2

   A hybrid deep learning framework for volatility prediction in financial markets

   Sanjay Agal · OPENALEX

   we backtested it — Sharpe 0.64 · quant relevance 0.96

   The paper forecasts market volatility for risk management, VaR, options applications, and a reported trading strategy. A closely related volatility-targeting implementation using daily and one-minute US equity and ETF data is backtestable, but the full multi-asset sample cannot be reproduced because the required FX data and long history are unavailable.

   original paper

3. 3

   When Richer Information Does Not Improve Allocation: Machine Learning and Portfolio Optimisation in NFT-Ecosystem Tokens

   David Toscano, Juan C. Roca, Francisco Jareño · OPENALEX

   quant relevance 0.98

   The paper tests whether machine-learning return forecasts and OHLC-informed risk estimates improve portfolio allocation for NFT-ecosystem tokens. Its out-of-sample comparisons find that machine-learning forecasts add value, while intraday-informed volatility estimates do not systematically improve risk-adjusted returns.

   original paper

The rest of the batch

• Incredulous creditors: ESG momentum, credibility barriers, and state-contingent pricing in corporate bonds

  Karim Henide · OPENALEX · relevance 0.93

  The paper presents a corporate-bond return signal based on ESG momentum and conditioned on issuer credibility, bond seniority, credit quality, and the SFDR regulatory regime.

and 2 more · see the full radar