The frontier of quantitative finance, in one feed. The newest peer-review-bound research from arXiv’s q-fin archive — trading and market microstructure, portfolio management, risk, pricing, and machine learning in markets — with titles, authors, and abstracts, linked straight to source. Updated continuously.
Numerical methods, simulation, and machine learning in finance.
Òscar Burés, Rafael De Santiago
We propose a signature-based framework for the identification of stochastic volatility model classes from observed path data. By mapping volatility trajectories into a feature space via truncated path signatures and applying a gradient boosting classifier, we show that it is possible to distinguish between different classes of volatility …
Hao Qin, Ruozhong Yang, Charlie Che, Liming Feng
Many quantitative finance methods and applications are formulated in terms of option-implied risk-neutral marginals rather than directly in terms of option prices. Representative examples include martingale optimal transport, Bass local-volatility calibration, scenario analysis, and option-implied tail-risk measurement. The desired risk-n…
Yang Zhou, Jianwen Chen, Ruipeng Wei
We study a minimal agent-based market in which a single evolutionary-optimized institutional agent interacts with 20{,}000 herding retail traders. The agent spontaneously discovers a multi-cycle predatory strategy, producing 8--11 complete cycles over 2000 trading days with total portfolio return of $+51\%$ (best of 20 seeds; mean $+37.7\…
Useong Shin
This paper extends the cap-axis integral diagnostic to general characteristic axes, measuring factor-model pricing errors as bridge-alpha curves. A predetermined characteristic order generates prefix portfolios; subtracting equal-exposure aggregate portfolios yields zero-investment bridges indexed by cutoff p. The null is a zero-curve res…
Yang Zhou, Jianwen Chen, Ruipeng Wei
Three quantitative predictions have been advanced for the square-root law (SRL) of market impact, $I/σ_D = c\,(Q/V_D)^δ$ with $δ\approx 0.5$: GGPS ($δ=β-1$), FGLW ($δ=α-1$), and LOB walking ($δ=1/(1+γ)$). Using a minimal limit-order-book model populated by heterogeneous interacting agents and calibrated against the Tokyo Stock Exchange be…
Xianhua Peng, Wu Guo
We propose the first deep learning algorithm, the Certainty Equivalent Learning (CEL) algorithm, for solving high-dimensional discrete-time dynamic programming problems with recursive utility. Dynamic programming with recursive utility is numerically challenging because the recursive utility does not have an explicit representation and th…
Arati Uday Kamat
This paper reports a precision audit of a production filter stack against a 13-day window of post-rejection forward-market observations on Solana DEX trading (2026-04-10 to 2026-04-23, UTC). The audit yielded 99,510 follow-up samples across 2,402 unique rejection events spanning eight active filter rules. We classify each event under a fi…
Arati Uday Kamat
We present a Kaplan-Meier and Cox proportional-hazards survival analysis of 832,941 Solana pump.fun token launches with 24-hour graduation outcomes, observed continuously between 2026-05-08 and 2026-06-10. The pooled graduation rate is 0.198% (Wilson 95% CI [0.189%, 0.208%]), a 3.18x decline from the 0.63% rate reported by Marino et al. (…
Arati Uday Kamat
We study coordinated buyer behavior on the Solana pump.fun bonding-curve marketplace using 1,578,333 buyer observations from 166,098 token launches between June 12 and June 26, 2026. A two-stage detection pipeline - intra-launch first-buyer-window extraction followed by cross-launch persistent-cohort surfacing via union-find on co-occurre…
Useong Shin
I propose a cap-axis integral diagnostic for factor-model evaluation. Low-dimensional factor models can improve the maximum-Sharpe frontier while leaving zero-alpha violations on economically fixed subspaces. The diagnostic studies one such subspace by lifting pricing errors into a bridge-alpha curve along the market-capitalization rank a…
Dangxing Chen, Pengzhan Guo
In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications. At the same time, explainability remains critical in finance, a domain characterized by high stakes and strict regulatory requirements. Although numerous methods have been proposed to explain black box machine …
Bo Qu, Mingguang Chen
LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading. Yet most still rank agents by returns over a fixed window -- a weak proxy, since a period's return is dominated by the market path and apparent alpha can dissolve once look-ahead leakage is …
Hoyoung Lee, Suhwan Park, Seunghan Lee, Jun Seo +14
Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial source material, they can alter the investment judgment supported by the original source. We frame this problem as information fidelity: compression loses fidelity w…
Irene Aldridge
We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or optimization problem. An exact multi-period regret decomposition implies that the sign of this statistic classifies a linear strategy as a net liquidity consumer or provider, recovering the Ky…
Leif Andersen, Andrey Itkin, Rakhymzhan Kazbek
A flexible forward (FF) is a customized FX hedging instrument that guarantees a fixed exchange rate while letting the holder choose the delivery date within a pre-agreed window. It is therefore an American-style option on timing, and its valuation must respect the volatility skew of the underlying currency pair. We price FF contracts (and…
Tobias Lausser, Joao Eduardo Vuolo, Rudi Zagst
This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) archit…
Takayuki Sakuma
This paper develops a robust hedging valuation adjustment (HVA) measure for dynamic hedging. Simulated rebalancing and maturity-unwind trades generate a loss distribution for each no-trade-band rule, and we define robust HVA as the worst-case expected loss over a relative-entropy neighborhood of that distribution. Because band width affec…
Giacomo di Tollo, Massimiliano Kaucic, Filippo Piccotto
This paper proposes a two-stage decision support system for long-short portfolio optimization under environmental, social, and governance (ESG) considerations. In the first stage, assets are evaluated using a multi-criteria procedure based on TODIMSort, with criterion weights derived using the MEREC (Removal Effects of Criteria) method. T…
Isidro Moroso Varona, Jakub Michańków, Paweł Sakowski
This paper studies the use of randomized neural networks for the estimation of exposure profiles and unilateral CVA of American options within a Monte Carlo framework. The analysis is carried out separately under both Black-Scholes and Heston dynamics, combining American option valuation, expected exposure and potential future exposure es…
Andrey Itkin
The Fokker-Planck equation is fundamental to statistical mechanics, yet in settings with multiple state variables, anisotropic (cross-) diffusion, and jumps, conventional discretizations frequently produce non-physical negative probability densities. Building on the operator approach of "A. Itkin, Pricing derivatives under Levy models. Mo…
Emiliano Papa
In this paper, we consider pricing a Bermudan swaption with a small number of exercise dates. We begin with the case of two exercise dates. In this limit, we show that the Bermudan price decomposes into the sum of short-dated European swaptions, setting an upper bound, minus a correction term. This correction is expressed as an integral i…
Abdulrahman Alswaidan, Cade Jin, Jeffrey D. Varner
Synthetic generators of daily equity returns let practitioners stress test, backtest, and design scenarios that a single realized market history cannot supply, but only if the generator reproduces the stylized facts of real returns: heavy tails, negligible linear autocorrelation, and slow decay of the absolute-return autocorrelation. Hidd…
Debdoot Ghosh
Institutional rebalancing is a batched optimization workload with a hard operating deadline: hundreds of accounts need new weights under budget, turnover, exposure, exclusion, and tax-aware controls before trading can proceed. This paper evaluates Asymmetry PRISM, a CPU/GPU portfolio optimization engine, through a public evaluation bounda…
Mohammed Benseddik, Benjamin Kraner, Claudio J. Tessone
Ethereum's beacon chain hosts over 920,000 active validators, a number inflated by the legacy 32 ETH stake cap. The Pectra upgrade (May 2025) addresses this by introducing 0x02 compounding validators, raising the maximum stake per validator from 32 to 2,048 ETH and enabling automatic reward reinvestment. This paper examines how compoundin…
Bruno E. Holtz, Carlos A. Abanto-Valle, Ricardo S. Ehlers, Gabriel Rodríguez
This paper extends the approximate Bayesian estimation framework for Stochastic Volatility in Mean (SVM) models to accommodate heavy-tailed distributions from the Scale Mixture of Normals (SMN) family. To overcome the computational challenges arising from these models, we propose a numerically stable estimation procedure that exploits spe…
Ryan Parker, Mark Stedman, Luca Capriotti
Using the path-integral formalism, we develop an accurate and easy-to-compute semi-analytical approximation for a general class of {default intensity} models. We illustrate the accuracy of the method by presenting results for the Black-Karasinski model for which the proposed approximation provides remarkably accurate results, even in regi…
Philipp Mahler, Peter Ruckdeschel
The accurate calibration of interest rate models is central to market-consistent valuation and Economic Scenario Generators (ESGs). Traditional calibration methods for multi-factor models such as the G2++ model often rely on point estimates, neglecting the influence of specific market data and the quantification of estimation uncertainty.…
Marcel Muller, Arno Botha, Conrad Beyers
An integrated and extendable approach for stress-testing loan portfolios is presented, which includes both a loan production component and a credit risk component. In this approach, we simulate a completed portfolio using realistic loan parameters and distributional assumptions. Thereafter, we generate the uncertain cash flow history of t…
Cosmin Borsa, Michael Ludkovski
Simulation based solvers for optimal stopping problems must discretize the stopping decision. Under classical dynamic programming, a coarse exercise grid with only a few stopping opportunities can materially undervalue the optimal expected reward, whereas on a very fine grid, approximation errors accumulate through the backward recursion.…
Ziyao Wang
We introduce Martingale Doppelgänger-Eval, a public shadow-market benchmark for auditing whether vision-language models (VLMs) use candlestick evidence rather than extrapolate past trends. The central difficulty is identification: on real market histories, chart evidence and trend are strongly coupled, so an observational score cannot det…
Sadanand Singh, Allam Reddy, Manan Chopra
We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule. Trained on 6,034 fully-filled hourly Binance Options surfaces of BTC and ETH spanning May-October 2023 and pa…
Thijs van den Berg
Many models in quantitative finance have no closed-form option prices and rely on slow, noisy Monte Carlo simulation; neural surrogates restore speed but offer no error guarantees. We present a general recipe for surrogates that are fast, with bounded and verifiable error, applicable to any simulation-based density model. A Mixture Densit…
John R. Graham, Campbell R. Harvey, Manish Jha
Business sentiment is a closely watched economic signal, but measuring it is slow and costly: surveys reach only a few hundred firms, arrive periodically, and take time to compile. We show that large language models hold the potential to address these shortcomings. We prompt an LLM to role-play as the CFO of a specific company at a specif…
Thijs van den Berg
Given risk-neutral densities of a tradeable forward, fitted as $N$-component mixtures at a finite set of expiration pillars, we look for a continuous-time interpolation that is (i) \emph{mixture-preserving}, remaining a mixture of the same kernel (generically with more components than either pillar), and (ii) \emph{arbitrage-free} across …
Xin Guo, Yijie Huang, Xiang Yu
In this paper, we develop a continuous-time model-free reinforcement learning algorithm to learn deterministic equilibrium policies in general time-inconsistent control problems. Utilizing the extended Hamilton-Jacobi-Bellman system, we recast the original time-inconsistent problem into an equivalent two-stage problem. In the first stage,…
Swati Sachan, Dale Fickett, Richard Buchinger, Theo Miller
Recent advances in error-corrected qubits have accelerated the timeline for practical quantum computing. It poses a threat to cryptographic primitives used to secure financial systems, government infrastructure, communication networks, and DeFi (Decentralized Finance) ecosystems. This paper introduces a post-quantum secure federated DeFi …
Alper Hekimoglu, Ismail Hakki Gokgoz
We present a regime-split Black--Scholes implied volatility solver in which every initial seed is a fully closed-form analytical expression, derived from the asymptotic structure of the Black--Scholes price in its natural domain. At the money, series reversion of an exact Gaussian identity yields a fourth-order seed with error $\mathcal{O…
Xinyue Fang, Robert Ślepaczuk
This study investigates whether regime-dependent volatility forecasting and machine-learning-based return prediction can be jointly integrated to improve both statistical forecasting performance and economic strategy outcomes in equity markets. Using high-frequency CSI 300 Index data from 2005 to 2023, a sequential twostage framework is d…
Ilia Zaznov, Atta Badii, Julian Kunkel, Alfonso Dufour
This study addresses the optimal execution of large stock sell programs by introducing TT-DAC-PS (Twin-Target Deterministic Actor-Critic with Policy Smoothing), a deterministic actor-critic architecture that combines twin exponential-moving-average critic targets with pessimistic min backup, TD3-style target policy smoothing noise, delaye…
Junyi Yao, Zihao Zheng
Large language models (LLMs) and agentic systems are increasingly proposed for financial trading, yet their reported performance remains difficult to compare because studies vary in data provenance, temporal split discipline, execution timing, turnover treatment, and transaction-cost modeling. This article presents a targeted topical revi…
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