cs.AI · cs.CL · cs.LG · cs.CV · cs.CR · cs.SE · stat.ML · q-fin.TR · q-fin.ST · econ.GN · q-bio.QM · physics.soc-ph · math.OC · cs.RO · cs.HC
Ahmed Mahrous, Roberto Di Pietro · 2026-10-21
Using a near-census of 802,863 resolved markets listed on Polymarket between January 2023 and May 2026, and only information available at listing (the market's title and description, named resolution source, and contract metadata), we predict lifetime trading volume and whether s…
Malthe Rauh Johansen, Nina Lange, David Skovmand · 2026-10-20
We develop an arithmetic multi-factor stochastic volatility model for commodity futures and options. The arithmetic specification yields a low-dimensional Markovian representation of the futures curve and semi-closed-form pricing formulas for vanilla and basket options, including…
Akash Deep, Bhathiya Divelgama, A. Alexandre Trindade, W. Brent Lindquist, Svetlozar T. Rachev and others · 2026-10-19
A probability-weighting function (PWF) maps objective probabilities into decision weights. We construct PWFs from changes in the distribution of returns rather than from a parametric family: with prior and posterior return distributions $F_{prior}$ and $F_{post}$, the PWF is $w(p…
Levent Bulut · 2026-10-19
Older observations can help a forecaster learn a predictive relationship while becoming misleading when that relationship changes. We study this tension using paired return histories that differ in older observations but share recent returns and the conditional distribution of th…
Minkey Chang · 2026-10-19
Statistical arbitrage trades on an estimate of the state of a residual spread, and conventional practice discards the uncertainty of that estimate. We keep the uncertainty and use it in the trading decision. A Neural Filter Estimator (NFE) is trained on simulated paths of a mean-…
Zhiyu Zheng · 2026-10-18
Since the 2021 GameStop short squeeze, r/WallStreetBets (WSB) has been associated with episodes of extreme volatility, and its text is increasingly used to forecast volatility. A model using only large language model (LLM) embeddings of WSB posts attains an out-of-sample $R^2$ of…
Antonis Ballis, Christos Ioannidis · 2026-10-16
Weak-form efficiency implies symmetry restrictions on the Markov chain governing movement across the cryptocurrency capitalisation distribution. We test them on the full listed population: 8,970 assets, 690 weekly cross-sections, 2013 to 2026, with entry, exit and survival observ…
Kevin Foley, Jonathan Hartadi, Shivesh Prakash, Charishma Takkallapalli, Swapnil Vatsal · 2026-10-15
Language models that read financial news are usually trained on text from the same years used to test them, so a backtest can reward hindsight. We build news topics from NoLBERT, an encoder pretrained only on text published between 1976 and 1995, adapted into a sentence encoder b…
Alex Vidovich, Zivana Zerjal · 2026-10-15
Backtests of intraday strategies can drift away from what the live system actually does; in our case, the drift never showed in summary statistics. We report six defects found while running one intraday US equity system live and comparing its live days with their backtest replay.…
Karl Kristian Engelund · 2026-10-14
Regime-switching multi-factor models are widely used in quantitative finance, but filtering does not by itself determine when an agent should act on inferred regime changes. We formulate online regime detection with latent factors and hidden regimes as a Bayesian decision problem…
Jiwon Jung, Kiseop Lee · 2026-10-14
Multivariate Hawkes models are widely used to measure endogenous interactions in event data, but latent common signals can generate apparent excitation even without structural interaction. We characterize the resulting linear projection and construct stable, nonnegative Hawkes pr…
Hongyu Yao, Zhengyang Chi, Junbin Gao · 2026-10-13
We propose a structured continuous-time stochastic volatility model for one-day-ahead integrated-variance forecasting. The model introduces a jointly evolving realized-measure state that feeds directly into latent variance dynamics, while correlated Brownian innovations capture r…
Pawel Robert Smolinski, Siddharth Saridevi · 2026-10-12
We study whether stock disclosures by recognizable investors generate abnormal market reactions beyond the reactions produced by comparable but non-recognizable managers. We define the Buffett effect as the incremental abnormal return to a disclosed new position that is attributa…
Magnus Wiese, Hans Buehler, Blanka Horvath · 2026-10-12
We present a statically and dynamically arbitrage-free European option market model with smooth interpolation between option prices, based on the SANOS framework, and prove that its dynamics admit a unique strong solution. In spirit, it is a stochastic model for local volatility …
Francesco Cesarone, Massimiliano Corradini, Nicolò Giunta, Serena Rocci · 2026-10-12
Within the framework of non-extensive statistical mechanics, $q$-Gaussian distributions arise as maximum-entropy solutions of Tsallis entropy under suitable escort moment constraints. Although widely used in physics, biology, geology, finance, and related fields, standard $q$-Gau…
Yunqi Liang, Stefan Zohren, Mihai Cucuringu · 2026-10-10
To extend conventional model-level feature importance to economically meaningful portfolio outcomes and decisions, we propose a local first-order attribution-propagation framework that maps allocation-function feature exposures to local sensitivities of sequential portfolio perfo…
Sayak Dutta · 2026-10-10
Temporal point processes are used both to measure market mechanisms, for example how fast prices absorb news and how much of the reaction is the market responding to itself, and to predict the next event. We show that one algebraic object, the phase-type (Erlang-chain) state spac…
Cameron Mouangue, Tristan Cazenave · 2026-10-09
Feature selection is a central problem in high-dimensional financial regression, where large numbers of engineered variables and strong correlations among predictors reduce model stability and interpretability. $L_1$ regularization (Lasso) is widely used for sparse regression, bu…
Shivesh Prakash, Frank Ferstler, Nicholas Pezolano · 2026-10-09
FLEX options are customized exchange-traded contracts allowing participants to choose nonstandard strikes, expirations, exercise styles, and settlement terms while retaining central clearing. We investigate whether single-name FLEX activity leaves measurable footprints in regular…
Siqi Shao, R. A. Serota · 2026-10-07
We introduce a mean-reverting stochastic differential equation with a three-component stochastic term and show that it generates a hierarchy of steady-state (stationary) distributions. At the top level, the hierarchy is described by a modified-Beta distribution, while one- and tw…
Aashish Bohra, Lokendra Vishwakarm · 2026-10-07
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 ret…
Dexin Peng, Xiaoyu Wang · 2026-10-07
Shallow models are special cases of deep models, and deep models theoretically have the potential to outperform the shallow ones. However, the existing empirical asset pricing literature provides strong benchmarks for shallow models. Residual learning allows neural network models…
Anjali Thawait · 2026-10-05
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 …
Yizhi Luo, Jiahe Yi, Jianhui Zhang, Shuo Sun · 2026-10-04
Learning effective representations helps characterize the structure and dynamics of equity markets from financial data with a low signal-to-noise ratio. Black-box deep models can capture complex patterns but may overfit sample noise and lack explicit economic structure. Meanwhile…
Andrey Itkin · 2026-09-29
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 eq…
Davide Graziano · 2026-09-28
This paper examines whether a cointegration-based pairs trading strategy between PepsiCo and The Coca-Cola Company is statistically robust and economically exploitable. We first test for cointegration and estimate the spread's mean-reversion dynamics over 2013-2018, then hold the…
Kevin Foley, Jonathan Hartadi, Shivesh Prakash, Swapnil Vatsal · 2026-09-28
News may reveal systematic risk, but whether its context enhances the construction of systematic risk factors is still unclear. We seek to test whether utilizing a sentence transformer represents an improvement over techniques such as Latent Dirichlet Allocation (LDA) in the cohe…
Siyuan Li, Jiangfeng Zhang, Rui Yao, Weihua Qiu, Mingyang Xu and others · 2026-09-27
Self-evolving agents aim to turn research feedback into reusable skills, tools, and research rules. Whether these accumulated capabilities continue to improve later research requires controlled evaluation. Long-horizon alpha discovery provides a state-dependent setting: once a ne…
Vincent Maciejewski · 2026-09-26
HFT systems are conventionally built as a single-threaded event loop, on the rule that every thread hop adds latency. We test that rule against a measurement study of more than a year of CME market data for the NQ front-month contract, following every packet and matching-engine t…
Sultan Amed, Tanmay Sen, Sayantan Banerjee · 2026-09-23
Verified income is often unavailable in digital loan applications, forcing lenders to rely on reported income and potentially leading to over-lending, overly conservative offers, or rejection of creditworthy applicants. Cross-institutional data-sharing constraints make this probl…
Bram Wouters, Cees Diks · 2026-09-23
We develop a model-agnostic framework for noise reduction in high-dimensional time series that explicitly targets optimal recovery of a low-dimensional latent dynamic component contaminated by observational white noise. Under the assumption that the latent dynamics live in a low-…
Bo Qu, Mingguang Chen, Licheng Wang · 2026-09-22
Language-model agents now run the whole of quantitative factor research: they propose investment factors, backtest them, select the survivors and retire them. We ask which of those jobs an agent should keep. Our answer is governed self-evolution: the agent may propose, and a froz…
Masoud Soleimani · 2026-09-22
Forecasters often score the same units per date against one standardized realized outcome. We show that every standardized forecast splits exactly into a component aligned with this common target and a component uncorrelated with it. Three consequences follow: forecast-error corr…
Michał Balcerek, Michał Wronka · 2026-09-22
We examine the conditional volatility dynamics of the USD 1Yx10Y forward swap rate using GARCH(1,1), GJR-GARCH(1,1), and a two-regime Markov-switching GARCH (MSGARCH) model. The analysis uses daily data from 2007 to 2023 and incorporates market-implied measures (ATM swaption vola…
Arkadiusz Lipiecki, Nikolaos Kourentzes, Rafal Weron · 2026-09-19
Day-ahead electricity price forecasts support trading and storage decisions, but for battery arbitrage predicting intraday price spreads is more relevant than predicting individual hourly prices. Here we show that a temporal hierarchy forecasting (THieF) framework that jointly re…
Haoyu Liu, Len Thomas, Benjamin Baer, Carl Donovan · 2026-09-18
Pairs trading exploits mean reversion in the relationship between related assets. We adapt this idea to political betting markets by modelling the combined implied probability of the two major-party nominees with a latent Ornstein-Uhlenbeck process whose mean-reversion level vari…
Alex Bernstein, Lisa R. Goldberg, Nicholas Gunther, Alec N. Kercheval, Tian Lan and others · 2026-09-17
In a statistical factor model, principal components (or eigenvectors) of a sample covariance matrix serve as estimates of principal directions, the true drivers of co-movement of a collection of observed variables. We write the often substantial error in these estimates as a sum …
Aashish Bohra, Vivek Vijay · 2026-09-13
Hybrid Deep Learning for equity index forecasting is limited by three problems: propagation of OHLCV noise into derived technical indicators (TIs), channel-indiscriminate multi-scale decomposition that conflates heterogeneous frequency signatures, and static multi-branch fusion t…
David Reinhardt · 2026-09-12
Special Markowitz (SM) regularises returns and covariance jointly, relative to a reference state (mu_ref, Sigma_ref). Each eigendirection of the whitened relative operator carries a signed spectral potential Phi_k, with persistence factor psi_k = exp(-Phi_k) > 0. Positive pote…
Aashish Bohra, Vivek Vijay · 2026-09-11
Stock price prediction remains challenging due to the non-stationary and noisy nature of financial time series. Existing deep learning models often rely on rigid decision-level fusion, ad hoc hyperparameter tuning, and compressed final-layer outputs, causing information loss, ove…
GET https://store.agentexchange.work/gw/papers/latest?cat=q-fin.ST&limit=25 (0.003 USD per call). Catalogue