15 papers in the last 30 days, 1 in the 30 days before (+1400%).
Circumventing the Computational Hardness of Computational Arbitrage (opens in a new tab)
arXiv - AI / ML / NLP
AI model marketplaces host providers with heterogeneous costs and capabilities. These differences create an opportunity for computational arbitrage (Olmedo et al., 2026), in which an intermediary purchases inference from a portfolio of models and resells it to users at a profit. Beyond creating profit opportunities for the arbitrageur, computational arbitrage offers financial value to both consume
BridgeRouter: Gain-Aware Instance Routing over Cross-Modal Bridge Portfolios (opens in a new tab)
arXiv - AI / ML / NLP
Lightweight representation bridges make pretrained, task-adapted EEG foundation models executable on other biosignal modalities without retraining their backbones. However, selecting a single bridge pathway globally assigns every instance to the same transfer route and ignores complementary errors across routes. We therefore formulate multi-bridge transfer as an instance-level algorithm-selection
arXiv - AI / ML / NLP
Factor models are central to asset pricing and portfolio construction, but neural factors are often difficult to interpret, and their effect on portfolio risk is unclear. A sparse autoencoder (SAE) may address both concerns by describing daily returns across many assets with only a few active features. We ask whether this sparse representation produces stable financial factors and better-diversifi
Simple Portfolios that Deliver Consistent Decent Returns (opens in a new tab)
arXiv - Quantitative finance + Economics
Portfolio construction has been studied extensively in both academia and industry. Its applications range from pension funds and hedge funds to private savings. The academic literature centers on methodology, commonly evaluated in settings that allow short selling, leverage, and frequent trading. Industry, on the other hand, operates under real constraints, but its data and methods are proprietary
Does Ecosystem Activity Validate TVL? (opens in a new tab)
arXiv - Quantitative finance + Economics
Total value locked (TVL) is widely used to assess decentralized finance ecosystems. Prior evidence finds that portfolios formed on TVL alone are spanned by the crypto market portfolio (Brigida 2025). This paper asks whether TVL becomes more informative when combined with measures of adoption, usage, development, protocol revenue, or social attention. I construct weekly value-weighted 2x3 portfolio
FIDES-Live: Evaluating Behavioral Concordance in Sequential Trading Agents (opens in a new tab)
arXiv - AI / ML / NLPalso LLM agents
Sequential trading agents produce decisions, orders, execution receipts, account states, and reports that need not agree, even when the final portfolio value is correctly computed. We present FIDES-Live, an operational extension of FIDES from generated strategy concordance to the audit of sequential agent traces. The protocol separates structured decision validity, optional numerical commitment ad
arXiv - AI / ML / NLP
arXiv:2512.07867v2 Announce Type: replace-cross Abstract: We develop a transparent, auditable large language model (LLM) pipeline for macro-financial stress testing. Structured prompting, with optional retrieval of country fundamentals and news, emits G7 macroeconomic scenarios (GDP growth, inflation, policy rate), which are converted into deviations from an IMF World Economic Outlook baseline and
arXiv - Quantitative finance + Economics
arXiv:2610.27751v3 Announce Type: replace Abstract: We investigate the interaction between market regime detection and Nested Clustered Optimization (NCO) for tactical asset allocation across heterogeneous multi asset portfolios. The central empirical question is whether conditioning the NCO pipeline, encompassing covariance estimation, hierarchical clustering, and weight allocation, on latent mar
arXiv - Quantitative finance + Economics
arXiv:2610.33538v1 Announce Type: cross Abstract: Security-token standards such as ERC-3643 decide who may hold a token, not what stands behind it. For a sukuk this gap matters: whether a certificate may trade at a negotiated price depends on what its portfolio contains, and a token that checks only investor identity keeps trading after every non-debt asset has been sold. We ask whether this trada
arXiv - Quantitative finance + Economics
arXiv:2610.18795v2 Announce Type: replace-cross Abstract: Mortgage credit risk may exhibit dependence across economically connected areas, even when they are geographically distant. However, current models typically represent cross-area dependence through fixed geographical neighbourhoods, potentially overlooking economic connections that extend beyond physical proximity and evolve over time. Thes
Bayesian Parametric Portfolio Policies (opens in a new tab)
arXiv - Quantitative finance + Economics
arXiv:2602.21173v2 Announce Type: replace Abstract: Parametric portfolio policies map predictors into portfolio weights without modelling returns, making them a natural tool for factor timing. With many predictors, if the policy has more coefficients than observations the data cannot pin it down. We develop Bayesian Parametric Portfolio Policies (BPPP), which discipline the policy with a prior, up
Multiscaling in the Rough Bergomi Model: A Tale of Tails (opens in a new tab)
arXiv - Quantitative finance + Economics
arXiv:2601.11305v2 Announce Type: replace Abstract: The rough Bergomi (rBergomi) model, characterised by its roughness parameter $H$, has been shown to exhibit multiscaling behaviour as $H$ approaches zero. Multiscaling has profound implications for financial modelling: it affects extreme risk estimation, influences optimal portfolio allocation across different time horizons, and challenges tradit
Combinatorial Arbitrage in Prediction Markets (opens in a new tab)
arXiv - Quantitative finance + Economics
arXiv:2610.23517v4 Announce Type: replace Abstract: We develop a framework to identify and quantify arbitrage across logically related prediction-market contracts. We prove that prices outside the convex hull of admissible payoff vectors admit arbitrage and identify optimal arbitrage portfolios through integer programming. Using venue metadata and semantic analysis of resolution criteria, we recov
Certifying Reward Alignment in Portfolio Reinforcement Learning (opens in a new tab)
arXiv - Quantitative finance + Economicsalso LLM agents
arXiv:2610.34153v2 Announce Type: new Abstract: Financial reinforcement learning often translates an investment objective into an additive risk-adjusted reward. An agent can optimise that reward accurately yet implement a different financial preference. We develop a decision audit for terminal mean-variance equilibrium that separates reward misspecification from learning error. In a finite exogeno
Optimal portfolio choice under green transition timing risk (opens in a new tab)
arXiv - Quantitative finance + Economics
arXiv:2610.33597v1 Announce Type: new Abstract: We study an investor's optimal portfolio choice problem when they account for the impact of a possible future green transition, whose timing is uncertain. We model the green transition as a discontinuity in the stock price processes, driven by a single jump process with constant intensity, in which 'green' stocks increase in value and 'brown' stocks
arXiv - Quantitative finance + Economics
Mean--variance portfolio choice takes the conditioning information as given, so that two errors about it, using variables not available at the decision time and treating common variation as idiosyncratic, pass into the portfolio unseen. This paper turns what that practice assumes into a condition declared before estimation, certified from data and priced when it fails. The conditioning information
Climate aware lending allocation under NGFS scenarios - A Monte Carlo approach (opens in a new tab)
arXiv - Quantitative finance + Economics
This paper examines climate-sensitive credit risk and lending allocation over a five-year horizon by mapping NGFS short-term transition and physical-risk scenarios into obligor default probabilities. NGFS shocks enter mean-reverting log-production dynamics, yielding analytical terminal default probabilities. Monte Carlo simulation with covariance-based PCA estimates joint losses, while constrained
arXiv - AI / ML / NLP
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 rating methodologies across the industry and the unintuitive nature of manually weighting conflictin
arXiv - Quantitative finance + Economics
We study the problem of optimal portfolio selection under stochastic volatility within a continuous time reinforcement learning framework with portfolio constraints. Exploration is modeled through entropy-regularized relaxed controls, where the investor selects probability distributions over admissible portfolio allocations rather than deterministic strategies. Using dynamic programming arguments,
An Analysis of Artificial Intelligence Adoption in NIH-Funded Research (opens in a new tab)
arXiv - AI / ML / NLP
Understanding the landscape of artificial intelligence (AI) and machine learning (ML) adoption across the National Institutes of Health (NIH) portfolio is critical for research funding strategy, institutional planning, and health policy. The advent of large language models (LLMs) has fundamentally transformed research landscape analysis, enabling researchers to perform large-scale semantic extract