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.
Computational Finance2d ago
Rupendra Yadav, Aparna Mehra
Expectiles are the only law-invariant risk measures that are both coherent and elicitable. Unlike Conditional Value-at-Risk (CVaR), however, they do not admit a Rockafellar--Uryasev representation that admits tractable Wasserstein reformulations. We address this difficulty by developing an envelope theorem for worst-case expectiles that c…
Computational Finance3d ago
Mateusz Buczyński, Michał Woźniak, Konrad Kaczyński, Anna Wróblewska +1
Forecasting resale prices of used electronics is critical for subscription-based platforms where pricing errors translate directly into risk. Unlike structured financial markets, second-hand electronics exhibit high volatility, sparse listing histories, and non-normal price dynamics - yet no systematic time-series benchmark exists for thi…
cs.LGq-fin.CPq-fin.ST3d ago
Aashish Bohra, Lokendra Vishwakarm
Wavelet-based financial forecasters typically use the transform only to denoise, or reduce it to a single spectral snapshot at the forecast origin, and the convolution that produces the coefficients is usually bilateral, so it can read past the forecast origin. DSTNet instead retains the recent evolution of filter-bank magnitudes as a cau…
cs.LGq-fin.CP5d ago
Philipp J. Schneider, Lukas Looser, Antoine Garin, Shuhan Liu +1
Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions. We propose WRAP (Wasserstein-Reweighting Adversarial Perturbation), a drift-aware adversarial training framework derived from a two-budget distributionally robust…
math.OCq-fin.CP5d ago
Lokman A Abbas-Turki, Chassagneux Jean-Fran{\c c}ois, Jean-Philippe Lemor, Gr{é}goire Loeper +1
We develop a posteriori primal-dual bounds for numerical approximations of European option prices in the Uncertain Volatility Model. Given a smooth candidate approximation of the value function, a feedback control induced by its Hessian yields a primal lower bound. On the dual side, we derive a representation based on a matrix-valued Gamm…
cs.CEq-fin.CP5d ago
Emre Atay Tümer, Rischan Mafrur
On-chain transfer volume is often used to describe activity in tokenized real-world assets (RWAs), although recorded transfers may reflect very different economic functions, including token creation, destruction, issuer-related operations, and movements between other addresses. Ownership measures face a similar problem because a large add…
Trading & Market Microstructureq-fin.CPq-fin.MF6d ago
Anjali Thawait
Market-regime models typically assume a finite set of discrete latent states. We examine whether high frequency limit-order-book dynamics exhibit distinct regime separation or apparent regimes result from discretising an underlying continuum, analysing deep limit-order book data for EURO STOXX 50 index futures across 987 clean trading day…
cs.CEq-fin.CP6d ago
Yanzheng Jin, Pengyang Shao, Yunshan Ma, Haowen Pan +4
Formulaic alpha discovery seeks symbolic expressions that predict cross-sectional asset returns. In deployment, multiple formulas are combined into an alpha pool, where each formula is valued through the complementary information it contributes to joint predictive performance. While Reinforcement Learning and Generative Flow Networks have…
cs.MAq-fin.CP8d ago
Xiangyu Li, Fengbin Zhu, Xuan Yao, Siyu Liu +8
Deep Research (DR) agents have demonstrated strong capabilities in complex, research-oriented tasks through autonomous planning, iterative retrieval, multi-step reasoning, and structured reporting. However, adapting DR agents to finance introduces unique challenges: financial analysis demands the joint completion of heterogeneous sub-task…
Computational Finance8d ago
Shaïn Afzali, Serena Della Corte, Antonis Papapantoleon
Neural network-based approaches have emerged as efficient alternatives to traditional optimization-based procedures for the calibration of stochastic volatility models. However, existing work has focused primarily on predictive accuracy, with comparatively little attention devoted to understanding the structure of the learned inverse cali…
Computational Finance8d ago
David Schaurecker, Lasse B. Strand, Kevin O'Sullivan, Robert Jakob
Short-horizon price-trend prediction from limit order books in equity and intraday electricity markets requires models that combine predictive quality with low single-sample latency and a small serialized model size to keep pace with rapid and continuous market updates. We introduce MBOFormer, a 7,203-parameter causal transformer, and MBO…
Computational Finance9d ago
Long Teng
In this work, we study the pricing of American options under stochastic local volatility (SLV) models extended by including stochastic correlation driven by an additional stochastic process. We generalize the class of SLV models by incorporating a flexible stochastic correlation structure. To price options within these extended models, we…
Pricing of Securitiesq-fin.CP10d ago
Mathias Beiglböck, Manuel Hasenbichler, Gudmund Pammer
European option smiles determine the risk-neutral marginal laws of an asset, but not their intertemporal coupling, which is decisive for many applications. The Bass martingale construction selects, among all calibrated martingales, the one closest to Bachelier dynamics; it permits fast calibration at discrete maturities and recovers the D…
Computational Financeq-fin.MFq-fin.PM11d ago
Balaji Ramachandran, Srikanth Iyer, Shashi Jain
Bank treasury portfolios must balance yield, liquidity, and interest-rate risk across bonds of different maturities. Static allocation rules are ill-suited to this task: portfolios concentrated in long-duration securities with no dynamic adjust- ment mechanism can accumulate large mark-to-market losses and liquidity stress under rising in…
Computational Finance11d ago
Runyao Yu, Jochen L. Cremer, Pierre Pinson, Jalal Kazempour +3
Power systems with increasing variable renewable generation face greater uncertainty in scheduling and balancing. Intraday and balancing electricity markets facilitate position adjustments and real-time balancing close to delivery. As delivery approaches, continuous intraday market participants exposed to imbalance settlement adjust their…
Trading & Market Microstructureq-fin.CPq-fin.MF11d ago
Andrey Itkin
We model market impact as the response to submitted order flow net of counterflow from latent traders, activated when price displacements from the level that would prevail without the order exceed individual thresholds. Order flow depletes this pool, and a generalized Langevin equation governs its recovery over several time scales. Its me…
Computational Finance11d ago
Marcus Gawronsky, Chun-Sung Huang
Firms follow changing economic opportunities, but rivalry changes their response. We develop ESCAPE, a rational-share game of distribution-valued positioning with heterogeneous capability costs, establish a unique equilibrium, and derive an exact reallocation restriction separating opportunity and crowding contributions. Competition need …
Mathematical Financeq-fin.CPq-fin.PR11d ago
Frédéric Pauquay
We develop a non-perturbative framework for stochastic-volatility option pricing built on the two-particle-irreducible (2PI) effective action and the Dyson-Schwinger gap equations of quantum field theory. In log-price, log-volatility or Lamperti coordinates, the joint law of the state variables is approximated by a self-consistent Gaussia…
Computational Finance11d ago
Thomas Schmelzer
In a recent paper, Schmelzer and Hastie argue that Markowitz's Critical Line Algorithm and the LASSO path trace the same curve. Here we use that identity to compute efficient frontiers with a stock LASSO solver, \texttt{lars\_path} from \texttt{scikit-learn}. It handles long--short portfolios under a leverage cap, fixed leverage with vary…
Computational Financeq-fin.MF12d ago
Hyoeun Lee, Kiseop Lee
We study the joint dynamics of the best bid and ask prices with a spread-gated Hawkes-flocking model. The model tracks four types of best-quote movements: spread-narrowing movements are switched off when the spread is at its one-tick minimum, and a cross-side excitation term, whose activation depends on the prevailing spread, links the tw…
cs.LGq-fin.CPq-fin.MF12d ago
Joel Pfeffer, J. M. Diederik Kruijssen, Florian Stecker, Steven N. Longmore
In quantitative finance, standard regression losses are misaligned with the economics of return prediction. As the conditional mean of financial log-returns is close to zero, symmetric losses such as the mean squared and mean absolute errors make the constant zero forecast a near-optimal solution, penalizing models with genuine but noisy …
cs.AIq-fin.CP12d ago
Hoyoung Lee, Suyeol Yun, Jack Haverty, Yunju Cho +16
Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on fixed, per-item rubrics, which are costly to extend and cannot encode each institution's own standard. In FinAutoRubric, experts specify reusable evaluation gu…
cs.LGq-fin.CPq-fin.PM12d ago
Kelvin J. L. Koa, Xinyang Li, Ke-Wei Huang
In this work, we study portfolio optimization under the stochastic discount factor (SDF) framework by learning market state representations that capture the underlying risk structures of financial data. This is challenging due to several factors: financial markets exhibit non-stationary dynamics with shifting regimes, multimodal inputs su…
cs.LGq-fin.CPq-fin.RM12d ago
Jean-Loup Dupret, Donatien Hainaut, Edouard Motte
We introduce a deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods. The hedging functional is restricted to a reproducing kernel Hilbert space whose kernel is parameterized through a neural network embedding of the input features. The framework minimizes a regul…
Portfolio Managementq-fin.CPq-fin.TR13d ago
Wee Ling Tan, Stephen Roberts, Stefan Zohren
We present an end-to-end deep learning framework for systematic options trading that directly embeds hedging behavior through explicit control of portfolio-level risk exposures. While neural networks trained to optimize risk-adjusted performance have been shown to outperform traditional rules-based strategies, such approaches remain agnos…
cs.AIq-fin.CP13d ago
Haochen Luo, Yifan Li, Binh Minh An, Xiaolong Luo +3
Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess directional prediction, overlooking the structural complexity of derivative markets. Option trading introduces fundamentally different challenges, including nonli…
cs.AIq-fin.CP14d ago
Maya Kodeih, Aliaa Alnaggar, Mucahit Cevik
Monetary-policy announcements and central-bank communications play a central role in foreign exchange markets, yet their qualitative, unstructured form makes their forecasting value difficult to quantify. While prior research has largely focused on sentiment extracted from financial news, comparatively little is known about the relative c…
cs.MAq-fin.CP14d ago
Kelvin J. L. Koa, Filip Orestav, Shengqiong Wu, Michael J. Wooldridge +1
While symbolic regression (SR) has been successfully used in science to discover new equations, its use in financial valuation is hindered by several limitations. Whereas the natural sciences provide objectively correct relationships, financial valuation constitutes a distinct class of symbolic discovery problems, as it admits multiple va…
Computational Finance15d ago
Alexander Walter, Lukas Zimmer, Maxim Ulrich
Option-implied factors are largely, but not entirely, spanned by the equity factor zoo. We construct 137 option-implied characteristics for optionable U.S. stocks from 2004 to 2023, screen them to 20 representative long-short factors, and test each against 160 equity factors with the double-selection LASSO of Feng, Giglio, and Xiu (2020).…
Trading & Market Microstructureq-fin.CP15d ago
Jan Rosenzweig
We investigate the systemic macroscopic dynamics emerging from Limit Order Books (LOBs) populated exclusively by autonomous reinforcement-learning agentic traders. By formalizing agent interactions within a microscopic order-matching engine, we examine two fundamental quantitative phenomena: equilibrium phase transitions in order flow reg…
Computational Financeq-fin.RM18d ago
Andrzej Tokajuk, Jarosław A. Chudziak
Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed. Yet losses emphasise different errors and can target different properties of future volatility, so raw compar…
cs.CEq-fin.CP19d ago
Hengyi Yang, Sida Lin, Yiyan Qi, Yankai Chen +3
Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns. While recent deep-learning models have raised forecasting accuracy by jointly modeling inter-stock and temporal price dynamics, they conflate inter-stock relationships with intra-stock …
Computational Finance20d ago
Runyao Yu, Derek W. Bunn
Intraday electricity markets enable participants to adjust energy positions close to delivery, with price forecasts necessary to support trading and the scheduling of flexible electricity resources as well as increasingly responsive consumers. Forecasting model specifications have progressed from using macro-features, such as renewable ge…
cs.LGq-fin.CPq-fin.GN20d ago
Nabeel Ahmad Saidd
Financial time series evolve across multiple temporal resolutions, challenging forecasting systems to incorporate newly available information without repeatedly recomputing unchanged representations. We introduce HARN, a Hierarchical Associative Resonance Network for event-driven multi-timeframe forecasting. HARN maintains persistent repr…
cs.AIq-fin.CP20d ago
Zijiang Yang
Recursive differenced forecasting, the standard remedy for non-stationarity, predicts one-step changes and integrates them by cumulative summation. We show that this reconstruction is a discrete integrator with a pole on the unit circle, so the biased increment errors of a learned nonlinear model are summed without bound and the rollout d…
Computational Finance21d ago
Mingzhi Yang, Sheng Wang, Chao Zhang, Ruikun Li
Modeling the dynamics of option implied volatility surface (IVS) is crucial for pricing, hedging, and risk-managing option portfolios. We develop a universal conditional diffusion model that learns to jointly generate next-day IVS increments and the underlying stock's returns. The model is trained on pooled data from 50 stocks and evaluat…
Computational Finance23d ago
Lilian Hu, Congxin He, Yue Kuen Kwok, Gongqiu Zhang
The standard Euler discretization schemes for numerical option pricing under stochastic volatility models are known to exhibit high biases and potential unreliability. The alternative use of the exact (unbiased) simulation approach invariably involves numerical evaluation of integrated variance (and / or volatility) conditional on termina…
Computational Finance23d ago
Congxin He, Yue Kuen Kwok
We develop an efficient Monte Carlo simulation scheme for pricing options under the Ornstein-Uhlenbeck driven stochastic volatility model via the operator splitting approach. With an ingenious splitting of the governing stochastic differential equations, our operator splitting scheme admits analytic solutions in all sub-steps, so its impl…
Trading & Market Microstructureq-fin.CP25d ago
Vincent Maciejewski
Automated execution algorithms are organized into schedule-based and liquidity-seeking families. This paper concerns the first, whose members -- Time-Weighted Average Price (TWAP), Volume-Weighted Average Price (VWAP), Percentage of Volume (POV) and Implementation Shortfall -- are all model-based: each derives its decisions from an explic…
Trading & Market Microstructureq-fin.CPq-fin.MF25d ago
Georgios Chionas, Charalampos Kleitsikas, Stefanos Leonardos, Leandro Sánchez-Betancourt +1
We present SAiFE_gym, a Python module that provides a collection of simulation environments for studying trading problems in Constant Product Markets (CPMs) with Concentrated Liquidity (CL). These markets give Liquidity Providers (LPs) granular control over how their capital is allocated and enable them to adjust their range of liquidity …
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