This week the pipeline scored 14 new papers. Here are the 10 strongest by our rigor-weighted score (how scoring works).
1. Insights on Time-consistent Deep Hedging under Elicitable Dynamic Risk Measures
Holy Grail · Math 8.5/10 · Rigor 7.5/10 · Risk Management, Options & Derivatives
We study deep hedging in the context of dynamics risk measures, where sequential decisions are time-consistent. Whereas the literature in such context mainly considers low-dimensional problems with simple environment dynamics, we tackle the high-dimensional problem of basket option hedging; we show…
2. Global Multi-Maturity SPX-VIX Calibration Beyond Markovian Stitching
Holy Grail · Math 9.0/10 · Rigor 7.0/10 · Volatility
We develop a global framework for joint S&P 500 (SPX)-VIX smile calibration across multiple maturities without the conditional-independence restriction induced by Markovian stitching. Exact local and global feasibility are equivalent: every globally feasible law has a block-preserving SPX-Markovizat…
3. An Entropic Factor Model for Robust Portfolio Replication
Holy Grail · Math 8.0/10 · Rigor 7.0/10 · Factor Investing
Portfolio replication, or the construction of a tradable basket of assets to match the risk-return profile of a target benchmark, is fundamentally an ill-posed inverse problem. When restricted to a subset of available assets, classical variance-minimizing models often yield unstable, over-leveraged…
4. Scaling Laws, Tabular Data and Actuarial Ratemaking Models
Holy Grail · Math 6.5/10 · Rigor 8.0/10 · Machine Learning
Scaling laws in modern deep learning describe how held-out loss improves as model capacity, training data, and compute increase, often following power-law trends. We investigate whether analogous scaling regularities arise in actuarial ratemaking, where data are tabular, heterogeneous, and noisy, an…
5. Uniform Inference and Certified Capacity at a Reflexive Stability Boundary
Holy Grail · Math 9.0/10 · Rigor 6.0/10
This paper develops uniform inference and certified capacity decisions for an estimated financial stability boundary. Conditional risk, temporary cross-impact, and effective risk-bearing capacity are jointly estimated from dependent observations. Conventional pointwise inference is reliable at a sep…
6. Switching Frictions, Heterogeneous Trading Horizons, and Long-Memory Order Flow
Holy Grail · Math 8.5/10 · Rigor 6.0/10 · Market Microstructure, Volatility
This paper develops a mechanism through which costly changes in the representations used for portfolio choice can contribute to persistent signed order flow. Heterogeneous switching thresholds and opportunity volatility generate heterogeneous residence times, and renewal aggregation maps their execu…
7. Modeling Trade Durations under Temporal Granularity Effects in Forex Markets
Holy Grail · Math 7.0/10 · Rigor 7.0/10 · HFT & Execution, Fixed Income
Trade durations in high-frequency foreign exchange data exhibit increased occurrence near integer values. To address this empirical phenomenon, we propose the granularity-adjusted autoregressive conditional duration (GA-ACD) model. It is based on a novel two-component mixture distribution consisting…
8. The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis
Street Traders · Math 4.0/10 · Rigor 8.0/10 · NLP & LLMs
Large Language Models (LLMs) increasingly use user context such as memory, profiles, and role prompts to personalize their responses. This personalization can affect evidence-based judgment: the same evidence may lead to different conclusions under different user contexts. Finance provides a high-st…
9. Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization
Holy Grail · Math 7.0/10 · Rigor 6.0/10 · Reinforcement Learning, Portfolio Optimization
Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates. Current Reinforcement Learning (RL) approaches typically optimize for a single ESG provider, neglecting the significant divergence in…
10. Bayesian Confidence Recalibration and Research-Equilibrium Criticality: Temporal Support in Robust Portfolios
Lab Rats · Math 9.0/10 · Rigor 2.0/10
Robust portfolio rules that reconstruct confidence sets after learning need not preserve the evaluator obtained by prior-by-prior Bayesian transport. In the Gaussian model, this discrepancy is summarized by natural-coordinate displacement: inherited transport preserves it whereas fresh reconstructio…
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