Economics & Finance · Interdisciplinary & Society
Statistical Finance
The newest papers in Statistical Finance that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
72 papers, from 6 September 2026 to 5 November 2026: 72 arXiv preprints.
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Newest papers, page 2
FinLLM-KG: Knowledge Graph-Enhanced LLMs for Financial Sentiment Analysis in Algorithmic Trading
Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).Event definition and the branching ratio: Hawkes estimates of price-change clustering in the consolidated quote feed
Hawkes models of price changes in the consolidated US quote feed find a sub-millisecond kernel component and a branching ratio near criticality.Compiling and forecasting implied volatility surfaces via survey sampling, tempered inverse-probability sequence learning, and causal activity clocks
Compiling implied volatility (IV) surfaces and forecasting derivative risk from high-frequency equity and options feeds requires addressing contract heterogeneity, schema shifts, volatility…Intraday Forecasting Ability and Liquidity Provision of Institutional Traders
This study uses a high-frequency dataset from a stock exchange to examine how forecasting ability and liquidity provision relate to institutional traders' proprietary intraday profitability.What Prediction-Market Contracts Reveal at Launch: Trading Volume and Dispute Risk
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…Arithmetic Models for Commodity Futures, Vanilla, and Calendar Spread Options
We develop an arithmetic multi-factor stochastic volatility model for commodity futures and options.Amortized Neural Filtering for Calibrated Posteriors in Statistical Arbitrage
Statistical arbitrage trades on an estimate of the state of a residual spread, and conventional practice discards the uncertainty of that estimate.Conflicting History in Foundation Model Return Forecasts
Older observations can help a forecaster learn a predictive relationship while becoming misleading when that relationship changes.Probability-Weighting Functions for Normal Tempered-Stable Laws: Orthogonal Channels and Information-Theoretic Indices
A probability-weighting function (PWF) maps objective probabilities into decision weights.More Mentions, Less Information? Diagnosing Identity and Alignment in Reddit Embeddings for Volatility Forecasting
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.Market Efficiency in a Changing Asset Population: Mobility, Entry, and Survival over Cryptocurrency's Full History
Weak-form efficiency implies symmetry restrictions on the Markov chain governing movement across the cryptocurrency capitalisation distribution.Where Backtests and Live Trading Part Ways: Six Defects from One Intraday System
Backtests of intraday strategies can drift away from what the live system actually does; in our case, the drift never showed in summary statistics.Reading the News Without Hindsight: NoLBERT Topics and the Cross-Section of Stock Returns
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.Common Signals, Spurious Cross-Excitation, and Identification in Multivariate Hawkes Processes
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.Regime Detection in Latent Factor Models -- A Bayesian Decision-Theoretic Approach
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.Structured SDEs for Realized GARCH: Continuous-Time Volatility Modelling and Component-Level Analysis
We propose a structured continuous-time stochastic volatility model for one-day-ahead integrated-variance forecasting.Asymmetric $q$-Gaussian Distributions from Tsallis Maximum Entropy
Within the framework of non-extensive statistical mechanics, $q$-Gaussian distributions arise as maximum-entropy solutions of Tsallis entropy under suitable escort moment constraints.GROMM: Generative Risk-neutral Option Market Models
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…The Buffett Effect
We study whether stock disclosures by recognizable investors generate abnormal market reactions beyond the reactions produced by comparable but non-recognizable managers.Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams
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…