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Paper Research Latest Activity

Research from arXiv, SSRN, and large funds: prioritize papers that provide data, code, and out-of-sample tests.

124 related activitiesLast 30 days 97 itemsFeatured among them 1 items

Updated

Activity Archive · Page 2 页

20 OctTueIssue 21–40 items
  1. arXiv q-fin46

    Research on cooperative multi-agent reinforcement learning for market making in prediction market order books

    The study addresses market-making challenges in Polymarket's thin order books, using the JaxMARL-HFT framework to train cooperative multi-agent market-making models on 417 real markets. The market-making agents are split into quoters and risk managers, trained using recurrent independent proximal policy optimization (IPPO) combined with RNN policies. Testing showed that the improved single-agent market-making model quoted more frequently and had smaller out-of-sample losses, but none of the models achieved positive average portfolio value in out-of-sample evaluation, with only single-seed training completed.

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  2. arXiv q-fin64

    arXiv Paper: The Control Layer Behind Rule-Following Trading Agents Across Cross-Market Quotes

    This arXiv paper tests the degree to which 4 AI trading agents adhere to their own strategies and risk rules, based on 2627 closed trades from February 12 to August 27, 2026. 18% of the trades were live orders on Hyperliquid, while the rest were simulated at real-time prices, with a uniform round-trip fee of 0.09%.

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  3. arXiv q-fin40

    AuDQ: enabling auditable data quality through agentic rule discovery

    AuDQ, an auditable data quality layer for high-risk tabular data pipelines, proposes an ontology-driven, warehouse-native design vision that treats data quality rules as first-class artifacts, supporting version control, governance text traceability, and immutable execution trajectory replay. Its agentic rule discovery core includes both fully autonomous and guided rule acquisition modes, along with human-machine collaborative review interfaces, and has completed preliminary validation experiments on real-world datasets from the finance and retail industries.

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  4. arXiv q-fin46

    Probabilistic Order Book Forecasting with Conditional Diffusion Models

    This study introduces LOBGrad, a conditional DDPM framework for probabilistic order book forecasting. The framework combines an autoregressive RNN encoder with a diffusion-based generator, and establishes an excess-risk oracle inequality for the single-step DDPM noise prediction objective under exponential mixing and regularity assumptions.

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  5. arXiv q-fin37

    The Strategic Origins of Network Structure

    Researchers propose a game graph equilibrium framework that defines network structure as a solidified record of strategic interactions, proving that two types of games correspond to four network topologies, and K-class interactions correspond to K^K classes of topologies. This framework enables mechanism inference on low-dimensional game-theoretic manifolds through constrained block modeling, can reconstruct generative dynamics from observed networks, and performs well in real-world networks such as international trade.

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  6. arXiv q-fin57

    Conditional Weight of Evidence for Gradient Boosting Credit Models: Estimation Methods, Additivity, and Their Relationship with Adverse Behavior Causes

    This arXiv paper proposes a conditional weight of evidence method to measure the marginal impact of a single credit feature on the log odds of default, given the rest of an applicant's credit information. The same feature value can yield different evaluations for applicants with different credit backgrounds.

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  7. arXiv q-fin38

    A Dynamic Programming Approach for Value-at-Risk Markov Decision Processes in Continuous Spaces

    This study is the first to prove the validity of dynamic programming equations for Value-at-Risk Markov Decision Processes (VaR MDPs) in continuous spaces, covering both finite-horizon and infinite-horizon scenarios. Previously, related dynamic programming equations had only been derived and verified in finite state and action spaces, leaving the continuous-space problem unresolved for a long time. This framework can be used to characterize optimal dynamic strategies for optimal execution problems with VaR objectives, solving an open problem in the field.

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  8. arXiv q-fin55

    Probability Weighting Functions for Normal Tempered-Stable Distributions: Orthogonal Channels and Information-Theoretic Indices

    This arXiv paper proposes constructing probability weighting functions from changes in return distributions rather than parameter families, with the formula w(p)=F_post(F_prior^{-1}(p)). Both the prior and posterior are normal tempered-stable distributions, and the posterior differs from the prior along three independent channels—scale, skewness, and tail thickness—under the premise of fixed variance.

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  9. arXiv q-fin52

    RegimeRank: An Interpretable Regime-Aware Ranking Model for Industry Rotation

    The paper proposes RegimeRank, an interpretable industry ranking model that frames industry rotation decisions as a task of ranking industry actions by future risk-adjusted active returns, using the forward-looking industry benchmark Sharpe ratio as the measurement metric. The model represents fundamental and technical signals through expert-defined time-series features, uses cross-attention to condition on learned macro factors, then models cross-industry dependencies through self-attention, while using expected gradient attribution to identify the contribution of individual signal-transformation pairs.

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  10. arXiv q-fin40

    SHADE: A Pause-Alpha Stable Denoising-Based Shrinkage Method for Heavy-Tailed Probabilistic Time Series Forecasting

    The study proposes an Alpha-stable Lévy diffusion predictor called SHADE, which achieves distribution-level shrinkage through a residual stable kernel by reading outputs at non-zero depths, addressing the problem of overly narrow predictive distributions and overconfidence when Gaussian diffusion models forecast heavy-tailed time series.

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  11. arXiv q-fin58

    arXiv Paper: The Impact of Conflicting History on Return Prediction with Foundation Models

    A new arXiv paper studies the predictive performance of foundation models for return prediction when historical observations are inconsistent with recent return distributions. Authored by Levent Bulut, the 10-page paper includes 5 figures and belongs to the statistical finance (q-fin.ST) field.

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  12. arXiv q-fin55

    Amortized Neural Filtering for Calibrating Posteriors in Statistical Arbitrage

    The paper proposes a Neural Filter Estimator (NFE), trained on simulated paths of mean-reverting processes, that outputs the posterior distribution of the implied state for each spread in the cross-section in a single forward pass, incorporating estimation uncertainty into trading decisions. On simulated paths with known ground truth, the method achieves accuracy comparable to other practical estimators, with its advantage lying in correctly characterizing its own uncertainty.

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  13. arXiv q-fin35

    Topological Compression of Belief States in Prediction Markets for Event Forecasting

    This study introduces topological data analysis (TDA) and persistent homology (PH) into forecasting models in the field of prediction markets, exploring whether the underlying structure of market belief states has predictive value. The research findings show that single-parameter PH can improve the predictive power and calibration accuracy of linear forecasting models, and that two-parameter multi-persistent homology can produce similar gains for nonlinear models. The paper is 41 pages long with 19 figures, and belongs to the field of Trading and Market Microstructure (q-fin.TR).

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  14. arXiv q-fin45

    No-Arbitrage Implied Volatility Surface Reconstruction via Additive Martingales

    This paper proposes AddMart, a few-shot implied volatility surface reconstruction framework that embeds no-arbitrage structure into the architecture to achieve global static no-arbitrage, enabling continuous and valid surface generation from a small number of option quotes. Experiments on 10 real-world option datasets show that the framework delivers competitive reconstruction accuracy with no significant static arbitrage violations, and can be directly applied to VaR, CVaR and crash probability estimation.

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  15. arXiv q-fin63

    arXiv Paper: Diagnosing the Incremental Information of Reddit WSB Post Embeddings for Volatility Prediction

    This 9-page paper accepted by ICAIF '26 proposes a diagnostic protocol to examine the true source of incremental information from LLM embeddings of r/WallStreetBets posts for volatility prediction. The study covers panel data of 202,145 mentioned stock-days from 2015 to 2022, using a retrospective rolling out-of-sample test.

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  16. arXiv q-fin37

    Bias Correction of Transaction Cost Models for Characteristic Function-Based Estimators

    To address the need for transaction cost estimation in scenarios without quote data, researchers developed an empirical characteristic function method with first-order bias correction for spread estimation, and established the asymptotic distribution of the corrected estimator under appropriate conditions. Numerical experiments show that this method has a bias-variance trade-off compared to the Roll model, and offers computational efficiency advantages over the Hasbrouck model.

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  17. arXiv q-fin37

    The value of optimal trading filters and predictive signals

    This paper addresses the stationary quadratic trading problem with transient price impact, deriving optimal trading strategies using predictive theory tools, expressed as a combination of conditional predictions and explicit causal linear filters. The paper recovers the Gârleanu-Pedersen target portfolio under the exponential elasticity assumption, derives fractional trading filters under power-law impact decay, and clarifies the mechanisms determining the net realizable value of predictive signals under different trading cost scenarios.

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  18. arXiv q-fin54

    Evaluating the Substitution and Complementary Effects of TimeGAN Scenarios in Rachev Ratio Portfolio Optimization

    This arXiv paper evaluates the reliability and investment consequences of TimeGAN-generated scenarios in Rachev ratio portfolio allocation. Using 12 ETFs, it compares historical, substitution, and hybrid strategies under delayed execution and transaction costs. The study finds that generated scenarios underestimate volatility and tail losses and distort dependencies between assets; extending training only reduces reconstruction loss without improving cross-asset correlation accuracy. Substitution strategies increase turnover and are sensitive to the lower bound of the objective function denominator, and adjusting this lower bound does not consistently improve realized downside risk.

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  19. arXiv q-fin44

    Wealth-to-Wage Ratio: A Simple Indicator for Measuring Inequality

    Agent-based simulation research based on the capitalist economic model proposed by Ian Wright shows that the ratio R of average per capita wealth to average wage is the core parameter determining the degree of wealth and income distribution inequality; a higher R value corresponds to a higher level of inequality measured by the Gini coefficient.

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  20. arXiv q-fin31

    Pricing and Hedging Framework for Infinite-Dimensional Financial Markets

    This study extends the classical Black-Scholes market framework to infinite-dimensional scenarios, modeling asset prices with stochastic evolution equations in separable Hilbert spaces, deriving an infinite-dimensional Black-Scholes equation, and establishing exact replication results for strong solutions and asymptotic replication results for weak solutions. The study also introduces Hilbert lattice structures to characterize no-arbitrage conditions, provides sufficient conditions for the existence and uniqueness of equivalent risk-neutral measures, and explicit pricing formulas for some tractable cases; the model can be interpreted as a segmented market composed of a series of one-dimensional geometric Brownian motions.

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