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%.
Read originalAuDQ, 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.
Read originalThis 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.
Read originalResearchers 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.
Read originalThis 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.
Read originalThis 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.
Read originalThis 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.
Read originalThe 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.
Read originalThe 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.
Read originalA 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.
Read originalThe 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.
Read originalThis 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).
Read originalThis 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.
Read originalThis 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.
Read originalTo 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.
Read originalThis 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.
Read originalThis 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.
Read originalAgent-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.
Read originalThis 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.
Read originalResearchers have distilled five principles from established practices in software engineering and security research to form the TRACE framework for governing AI-assisted research, covering five dimensions: record tracking, source verification, independent acceptance, process control, and correction evaluation. The framework is accompanied by the open-source operating system TRACE-OS, which can adapt to researchers' existing workflows, helping teams maintain control over their research work when adopting AI and promoting the establishment of trust standards for AI-assisted research.
Read originalResearchers used proprietary panel data of commercial mortgages from 1998 to 2021 and adopted deep neural networks (DNN) to distinguish liquidity-constrained default from strategic default. The study found that, under delinquency conditions, property net operating income (NOI) has a more significant impact on default rates than loan-to-value ratio (LTV), and the threshold for dividing the two types of defaults remained stable during the 2008 financial crisis and the 2020-2021 COVID-19 commercial real estate shock.
Read originalThis follow-up study builds on 'Eclipse of Rent-Sharing' by Acemoglu, He and le Maire, analyzing a sample of 7,000 firm-year observations across 695 companies from 2015 to 2025.
Read originalHKEX has launched the voluntary HKEX Green Equity certification scheme to help investors identify Hong Kong-listed companies with substantial green business activities. Aligned with globally recognized peer frameworks, the scheme is assessed by independent accredited reviewers against the HKEX Green Equity framework; certified companies may use the certification mark and are subject to annual reassessment. Eligible companies may submit applications starting today, with rules and application procedures available on the HKEX website.
Read originalStockStory published an analysis of internet stocks, mentioning 2 internet stocks worth watching and 1 internet stock facing headwinds.
Read originalOpenAI is improving ChatGPT’s code generation performance for low-resource programming languages, adding three evaluation dimensions: software engineering, SQLite, and COBOL migration. These capability improvements are centered on code generation scenarios for low-resource programming languages.
Read originalCboe Options Exchange has announced extended trading hours for select stock and ETF options. This adjustment applies to designated stock and ETF options, and the specific extended schedule was not disclosed in this announcement.
Read originalQuantConnect Lean commit number 18197 adds the ShareClassMarketCap field, with MarketCap still retained as the company's total market capitalization. This commit is verified by GitHub signature and co-authored by Claude Fable 5.1.
Read originalvectorbt v1.2.0 has been officially released. This version introduces the ulcer index and Martin ratio indicators, adds the nbformat>=4.2.0 dependency, and fixes multiple issues including OBV calculation, EWM standard deviation ddof handling, portfolio P&L expectation statistics, and drawdown logging.
Read originalGuangzhou Financial Holdings Futures released the precious metals daily report for the period 20261019, published via Sina Finance. The publicly available content currently only includes the report title and publishing channel information; core content such as specific market analysis and variety judgments within the report has not yet been disclosed.
Read originalThe number of 10-billion-yuan private equity firms hits a new high, with obvious internal divergence in the industry: some institutions are growing rapidly in scale, while others are falling behind. Related information comes from Tonghuashun.
Read originalSHFE crude oil futures closed 2.34% higher in the night session at 735.6 yuan per barrel. This market data comes from East Money's futures market coverage.
Read originalBinance provides live price, chart and market data query services for the Nasdaq Index (Derivatives) IXIC. Users can obtain the latest market trends of this derivative through the platform.
Read originalNYSE is following Cboe in expanding to 24/7 options trading. The publicly disclosed information currently covers only the two exchanges’ moves toward this trading schedule; specific launch dates, contract scope, and supporting rule details have not yet been announced.
Read originalThe SEC announced it will host a roundtable on machine-readable data in the age of superintelligence on November 9, 2026, at its Washington, D.C. headquarters. The event will discuss the applications of machine-readable data and superintelligence, as well as related risks and costs. The roundtable is open to the public; in-person attendance is limited, and it will be streamed live on the SEC’s website, with a recording to be made available afterward.
Read originalPublished at ICAIF 2026, this study finds that accurate option prices do not necessarily mean accurate recovery of the underlying risk-neutral density. By comparing synthetic simulation benchmarks with out-of-sample NIFTY option benchmarks, the research reveals that the two-component lognormal mixture model demonstrates overall pricing performance on synthetic benchmarks.
Read originalThis arXiv paper proposes proximal equilibrium operators (PEOs), constructed via finite-dimensional deep equilibrium layers, that can solve a family of convex optimization problems on separable Hilbert spaces. The paper proves that when input loss functions lie in a suitable infinite-dimensional set, PEOs can uniformly approximate corresponding solutions to arbitrary precision, with rank and width growing only logarithmically with the inverse of approximation error, and depth growing linearly. The related theory and trainability have been validated across three application domains: nonlinear PDEs, stochastic optimal control, and financial portfolio optimization and hedging.
Read originalThis arXiv paper reproduces Calvano et al.'s 2020 simulated Bertrand duopoly Q-learning pricing experiment and challenges the conventional punishment-mechanism explanation for why independently trained tabular Q-learning agents converge to supracompetitive prices.
Read originalThe TradingView platform provides EBITDA margin data for Inflection Point Acquisition Corp. VIII (NASDAQ:IPHX), which can be used to view indicators related to the company's profitability.
Read originalTradingView has released EBITDA margin data for HengHong Technology Inc. (NASDAQ:HCPC). This data can be used as a reference for the company's profitability performance.
Read originalTradingView provides EBITDA margin data for TCGX Acquisition Corp. II (NASDAQ:TCXB), which can be used to analyze the company's profitability.
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