Alpha Architect interprets a 2026 paper by Andreasen and Bro published in the Review of Finance. The study combines traditional valuation data with investor expectation surveys to propose a new method for distinguishing between fundamental-driven price increases and speculative bubbles.
Read originalThe 2026 Marsh CFA Institute Global Pension Index is released by Marsh in partnership with the CFA Institute and the Monash Centre for Financial Studies, covering 55 retirement income systems worldwide. It uses adequacy (40% weight), sustainability (35% weight), and integrity (25% weight) as core evaluation dimensions.
Read originalThis paper proposes PALM (Point-in-time Adaptation for financial Language Models) as an alternative to annual pre-training of financial language models. It achieves adaptation by fitting low-rank adapters on permitted financial text without modifying any pre-trained weights.
Read originalThis study focuses on the allocation of bank default losses without external capital injection, proving that for all non-decreasing concave terminal payoffs, the "tax the richest" truncation rule is optimal. The study constructs a continuous-time optimal solution through time discretization, characterizes the optimal flow as a novel reflected free boundary problem, and proves the positive time smoothness of the free boundary and the density of surviving banks.
Read originalBased on 4.57 million news articles covering approximately 3,000 US stocks from 2023 to 2026, the paper uses a compact classifier distilled from a large model teacher to label news with 17 event categories and 5 attributes, clustering them into 1.68 million stock-day events, of which 364,405 are neutral sentiment events serving as a placebo.
Read originalJuergen Renn of the Max Planck Institute for the Science of Human History constructed a game-theoretic model of climate coalition formation with dual registration channels, finding that the model exhibits bistability when the channels are sufficiently strong, with a critical mass separating a residual club from a near-universal coalition.
Read originalA study based on 2.8 million EU ETS trades and compliance records from 2005 to 2022 shows that 37% of regulated operators in the world’s largest carbon market do not participate in trading each year, and this phenomenon persists. Trading concentrates in the April compliance month, when returns are systematically higher, operator order flow can predict returns, and this effect is concentrated among frequent traders. A model incorporating market entry costs, limited intermediation, and heterogeneous information can explain these features. Staggering compliance deadlines into four groups can reduce the compliance-month premium by 58% without changing the overall cap.
Read originalThe study proposes the Identifiable Variational Dynamic Factor Model (iVDFM) for multivariate time series modeling. The model combines variational inference to provide partial identifiability guarantees within a known fuzzy class range. The model's innovation terms are conditioned on observed auxiliary context and mechanism embeddings, follow a conditional exponential family prior, and are trained by maximizing the ELBO to estimate uncertainty in latent trajectories and predictions. This research has been published in the ProbML 2026 conference proceedings, and the code is open source.
Read originalThe paper proposes a branching signature model that generalizes the classical geometric rough path based on the branching rough path framework, proving its universal approximation theorem, which can approximate high-level signatures through iterative combinations of low-level signature mappings. The model can explicitly extend the original path to a high-dimensional space via the mapping Ψ, implementing it as the classical geometric signature of the extended path, and its effectiveness is verified in two applications: rough volatility calibration and optimal execution.
Read originalThis arXiv paper defines the recovery of tick-level queue history from aggregated L2 limit order book snapshots as a conditional partial identification problem. It constructs a path-preserving compiler that only varies among three cancellation allocation rules—front-of-queue, quantity-weighted random, and back-of-queue—while fixing the aggregated path, reconciled market removals, latent order partitioning, and new order arrivals.
Read originalThe study proposes a conditional latent diffusion framework that jointly generates 30-step trajectories of the implied volatility surface and underlying returns. It learns surface representations through an arbitrage-aware autoencoder, with the diffusion process capturing the evolution dynamics. The framework is evaluated on the SPX surface dataset and can generate realistic probabilistic multi-step scenarios, with its forecast mean outperforming the persistence benchmark for horizons beyond the shortest maturity.
Read originalThe paper proposes using the half-width of conformal prediction intervals as a scaling measure for fractional Kelly position sizing with a leverage cap, and tests this rule under a pre-registered protocol. During the development phase, an autonomous LLM agent searched approximately 200 configurations on a 2016-2021 sample of 8 U.S. equity ETFs, with subsequent data sealed before the search, and the final configuration, 9 comparison portfolios, and conclusion mapping all registered before unsealing.
Read originalThis paper proposes an arbitrage-aware latent flow matching framework for unconditional generation of implied volatility surfaces. The framework first compresses high-dimensional surfaces into a low-dimensional latent space using a variational autoencoder with differentiable penalty regularization for calendar spreads, call spreads, and butterfly arbitrage. A flow matching model then learns to map a Gaussian prior to the empirical latent distribution, which is finally decoded back to a volatility surface. This method can generate 90.8% of surfaces that satisfy all tested static no-arbitrage conditions, performs best in the extreme Q99 range, and balances distributional similarity, tail preservation, and financial consistency without post-sampling reweighting.
Read originalThis study formulates and solves a stochastic control problem for Ethena protocol's core yield strategy, which generates returns through a delta-neutral combination of long stETH positions and short ETH perpetual contracts of equal size. The model takes the rate of simultaneously buying stETH and shorting perpetual contracts as the control variable, incorporating both permanent price impact and temporary execution slippage. The study solves both the infinite-horizon discounted problem and the finite-horizon problem with terminal liquidation costs, obtaining explicit optimal control solutions in both cases.
Read originalThis research in quantitative financial risk management proves that when estimates remain unchanged after replicating all scenarios, binary estimators can accurately describe the same population risk and recover the population dual set from finite models. The study derives Wasserstein, Kolmogorov, and total variation norms under bounded support based on Bernoulli loss risk, characterizes uniform estimability, provides impossibility bounds for arbitrary stochastic estimators, and further derives uniform regret bounds for data-driven risk-averse optimization, with applications in stop-loss retention scenarios.
Read originalThis arXiv paper applies 7 sets of Islamic finance screening rules to a sample of 1.34 million monthly observations of U.S. securities from 1999 to 2024, and backtests and compares them on a unified threshold-anchored scale. The results show that the disagreement across the sets of rules reaches 35.9% across all compliant industry observations, and the disagreement in financial ratio screening alone reaches 47.3%. Under AAOIFI's market value denominator rule, changes in market value alone are sufficient to explain 65.6% of non-compliance events, and half of the denominator-driven non-compliances reverse within 3 months.
Read originalFor a discrete-time financial market model with trades having instantaneous price impact, the existence of a solution to the corresponding utility maximization problem is proven. This study removes the unnatural restrictions on market depth and resilience processes present in earlier work, with the non-standard feature of this problem being that the set of attainable portfolio values may not satisfy convexity.
Read originalThis study proposes a general theoretical framework for identifying economically relevant latent variables such as willingness to pay and preference intensity through response time data, unifying and extending existing literature conclusions. The framework can be applied to three empirical scenarios: identifying optimal nudges, testing diminishing marginal happiness of income, and predicting processing heterogeneity.
Read originalThis study proposes a unified theoretical model that can identify and test a full set of probability updating biases within a single framework, focusing on belief distribution updates rather than point beliefs. Estimates from laboratory experiments show that the hot-hand fallacy is the most common bias and a key driver of biased reasoning. Most biases cancel out at the aggregate level, but base-rate neglect persists.
Read originalA new arXiv paper uses Gaussian surrogate models to precisely calculate the finite-sample risk of candidate scoring decision models, analyzing the boundaries of the role of three training elements: teacher soft targets, frozen pre-trained backbones, and temperature calibration. The paper points out that after a student model inherits teacher bias, more student data cannot eliminate that bias; fixed representations only reduce risk below an explicit sample size threshold; under the probit model, exact recalibration maps to temperature scaling, and can only eliminate all excess risk when the direction of the teacher's coefficients is correct.
Read originalThe 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.
Read originalThis 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.
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