This week the pipeline scored 58 new papers. Here are the 10 strongest by our rigor-weighted score (how scoring works).
1. Latent Continuum of Regimes in Limit Order Book Dynamics
Holy Grail · Math 8.5/10 · Rigor 9.0/10 · Market Microstructure, HFT & Execution
Market-regime models typically assume a finite set of discrete latent states. We examine whether high frequency limit-order-book dynamics exhibit distinct regime separation or apparent regimes result from discretising an underlying continuum, analysing deep limit-order book data for EURO STOXX 50 in…
2. Packets, Transactions and Queues: Design Principles for HFT Systems from a Measurement Study of CME Market Data
Holy Grail · Math 7.5/10 · Rigor 9.0/10 · HFT & Execution
HFT systems are conventionally built as a single-threaded event loop, on the rule that every thread hop adds latency. We test that rule against a measurement study of more than a year of CME market data for the NQ front-month contract, following every packet and matching-engine transaction through t…
3. Multi-period Mean-Expectile Portfolio Optimization under Wasserstein Ambiguity: Reformulation, Degeneracy and the Role of the Ground Metric
Holy Grail · Math 9.0/10 · Rigor 8.0/10 · Risk Management, Portfolio Optimization
Expectiles are the only law-invariant risk measures that are both coherent and elicitable. Unlike Conditional Value-at-Risk (CVaR), however, they do not admit a Rockafellar–Uryasev representation that admits tractable Wasserstein reformulations. We address this difficulty by developing an envelope…
4. Learned Monotone Recurrent Features in Governed Credit Scoring: The Price of the Frame and the Necessity of Macro Conditioning
Holy Grail · Math 7.0/10 · Rigor 9.0/10 · Machine Learning
Regulated credit scoring requires scores monotone non-decreasing in every exposure input. Deployed pipelines – hand-crafted monotone aggregates feeding sign-constrained gradient boosting – already meet this by composition; the open question is what learned temporal aggregation is worth inside one.…
5. Unbiased Monte Carlo Greeks for Discontinuous Payoffs
Holy Grail · Math 8.5/10 · Rigor 8.0/10 · Options & Derivatives
Pathwise differentiation of Monte Carlo estimators fails at payoff discontinuities, producing zero or biased sensitivities for barriers, autocallables, and digital options. The industry workaround — smoothing the indicator functions — introduces bias and requires per-product calibration. We deri…
6. A Dirichlet Mixed-Membership Model for Exact Multivariate Distributional Credibility
Holy Grail · Math 8.5/10 · Rigor 7.5/10 · Insurance & Actuarial
Credibility theory combines individual experience with portfolio information for insurance pricing, but classical formulations focus primarily on conditional means and expected premiums. We propose a Dirichlet mixed-membership model (DMMM) for multivariate distributional credibility. Policyholder ri…
7. Robust enhanced index tracking portfolio selection under distributional uncertainty
Holy Grail · Math 7.5/10 · Rigor 8.0/10 · Portfolio Optimization
The enhanced index tracking (EIT) portfolio selection problem aims to construct a portfolio that is expected to outperform a benchmark index. In practice, investors face uncertainty in the joint distribution of asset and index losses, as the true distribution is typically unknown and only partial in…
8. Modelling Regime Shifts in Continuous Intraday Electricity Markets with State-dependent Hawkes Processes
Holy Grail · Math 7.5/10 · Rigor 8.0/10 · Commodities & Energy, Volatility
The growing importance of intraday trading in Europe, driven by the increasing penetration of renewable energy sources, has led to higher volatility and periods of market stress. Understanding how order flow behaves under varying liquidity conditions requires models that adapt to the state of the ma…
9. Landscape-Dependent Performance of Photonic Quantum Solvers in QUBO Feature Selection for Financial Risk Detection
Holy Grail · Math 7.5/10 · Rigor 8.0/10 · Risk Management
Feature selection for imbalanced classification tasks such as credit card fraud and consumer default detection requires balancing predictive relevance, inter-feature redundancy, and computational feasibility. We benchmark three computing paradigms, classical branch-and-bound optimization (Gurobi), p…
10. Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark
Holy Grail · Math 6.0/10 · Rigor 9.0/10 · Machine Learning, Volatility
Forecasting resale prices of used electronics is critical for subscription-based platforms where pricing errors translate directly into risk. Unlike structured financial markets, second-hand electronics exhibit high volatility, sparse listing histories, and non-normal price dynamics - yet no systema…
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