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.
Everything else at the frontier of quant finance.
Sahong Park, Suhwan Park, Hoyoung Lee, Gakyung Kwon +7
Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific investment preferences. We study whether a model's overall investment stance can be calibrated to a specified direction and strength. We introduce an investment-bias dial, an inference-time in…
Tim Gebbie
We consider reflexivity in hierarchical causal systems in which higher-level states constrain the lower-level dynamics that remain admissible [Wilcox and Gebbie (2014),Gebbie (2026)]. We ask how such state-dependent top-down constraints are realised when local activity is event driven while causal claims are made in calendar time. If an a…
Andreas G. F. Hoepner, Blerita Korca, Frank Schiemann, Fabiola I. Schneider
Investors interpret social disclosures from a risk perspective, yet relevant information can reach them through channels that differ sharply in regulatory enforcement and materiality: SEC filings, sustainability reports, or financial reports. We analyse how social disclosure via each channel relates to idiosyncratic risk. Studying S&P 1,5…
Jason Roderick Donaldson, Giorgia Piacentino, Xiaobo Yu
We develop a model of interbank networks with random liquidity shocks. Networks of dilutable debt---e.g., long-term, unsecured---facilitate efficient liquidity transfers: Shocked banks pledge interbank claims as collateral for new senior debt, diluting existing debt. Unlike with non-dilutable debt, indebtedness and connectedness are sourc…
Abdulrahman Qadi, Akash Sharma, Francesca Medda
Shariah-compliant equity screening provides a transparent setting in which institutional rules determine who may own a stock. A binary label identifies current eligibility but not whether the feasible investor base is fragmented across standards or close to changing. We define this instability as classification uncertainty and formalize i…
Kartik Dahake, Abhijit Chakraborty
Signed graphs provide an effective architecture for portraying a system in which cooperation and conflict coexist. Emerging from the concept of balance in psychological sciences, they have found applications across several domains. Financial markets are one such example that can be modeled using signed networks, where assets exhibit corre…
Jan Spörer
Open-weight language models from Chinese AI labs caught up on benchmarks relative to proprietary frontier models in recent months. Yet their reliability on real-world financial tasks remains largely untested. We updated the Financial Touchstone benchmark, which now has 2,967 question context-answer triplets across 495 international annual…
Girish G N, Dhanashekar Kandaswamy
Business users confronted with a moving metric need to know which part of their data moved and why. Existing data-explanation methods typically return predicates: conjunctions of attribute-value conditions that isolate responsible records. Predicates are exact and directly executable as filters, but they describe axis-aligned regions and …
Sasan Mansouri, Daniel Saad, Mark Wahrenburg, Manu Weissel +1
Financial question answering is typically evaluated by answer correctness, yet in SEC filings a plausible and even numerically correct answer can be grounded in the wrong evidence. Similar facts and disclosures recur across sections of a filing, across reporting periods of the same firm, and across comparable firms. FinRank targets this p…
Zonghao Yang
Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making. This paper investigates the application of large language models (LLMs), specifically the GPT-3.5 model, for counterfactual analysis. We focus on the online lending context, where the counterfactual retu…
Sidharth Mallik, Anastasios Megaritis, Waymond Rodgers
The impact of web datasets on market prices has suggested the development of new sources of information, such as social media and web portals, indicating the possibility of an emergent phenomenon. We propose a defining perspective, termed open information, that adds to the existing types of public and private information. We demonstrate t…
Rem Sadykhov, Geoffrey Goodell, Philip Treleaven
The objective of this paper is to provide a methodology for applying the DeTEcT framework to modelling token economies, to formalise the configuration of the simulation environment, and to introduce an event analysis framework. A token economy is an economic system that has a unique mechanism for controlling its monetary supply, and a med…
Taha Choukhmane, Tim de Silva, Weidong Lin, Matthew Akuzawa
We ask a representative sample to write prompts seeking spending and investing advice from LLMs, then simulate the lifetime effects of following the advice under realistic asset and labor market conditions. Applying this method to GPT-5.2, we find following the advice would move respondents toward life cycle theory: broader participation …
Yuki Sato, Shunta Fujiwara, Kiyoshi Kanazawa
We develop an exactly solvable nonlinear time-series model by incorporating the square-root price-impact law into the Lillo--Mike--Farmer (LMF) model to resolve the diffusive price-dynamics paradox under predictable market-order flow. In financial market microstructure, it is well established that the price dynamics are approximately desc…
Gbenga Ibikunle, Ben Moews, Dmitriy Muravyev, Khaladdin Rzayev
We introduce data-driven measures of high-frequency trading (HFT) that distinguish between liquidity-supplying and liquidity-demanding strategies. We train machine learning models on a proprietary dataset with observed HFT activity, then apply these models to public intraday data to generate HFT measures across all U.S. stocks during 2010…
Amin Izadyar
I revisit the exchange rate disconnect puzzle, first documented by Meese and Rogoff (1983), using generative artificial intelligence (AI) to forecast currency returns based on economic fundamentals. Using ChatGPT and DeepSeek, I analyze a comprehensive dataset of economic data releases for major currency pairs and measure the fundamental …
Shiqi Fang, Zexun Chen, Jake Ansell
Algorithmic credit scoring must satisfy fairness and explanation requirements, yet prevailing predictive-parity criteria assess only outcomes at the decision point. They can therefore overlook whether rejected applicants face unequal burdens in reaching future approval, a phenomenon we call masked inequality. We develop an effort-centric …
Girish G N, Ashutosh Sahoo, Ajay Bhat, Akshay SP +3
Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness. A compos…
Jacob Conway, Levi Boxell
We study the extent to which individuals' consumption decisions are influenced by firms' stances on controversial social issues and the implied incentives for firms to take such stances. We use transactions from a major payment card company to predict cardholders' likely social alignment with firm stances and to quantify effects on consum…
Karl T. Ulrich
When do outcome records carry enough signal to support reliable inferences about skill? When they do not, what should evaluators substitute? The framework answering the first question characterizes any decision domain with two parameters: the noise reflected in each outcome and the effective number of independent outcomes that are availab…
Michail Samawi
We construct the Settlement Modernisation Index, a panel dataset of 809 reform events across 24 advanced economies between 1993 and 2024, decomposed into three economic channels and three adoption phases. We document an S-curve in inside money elasticity with two interior turning points at SMI = 0.27 and 0.93, separating a liberation phas…
Jodi Dianetti, Giorgio Ferrari, Yunzhi Hu, Hao Xing
Using a mean-field game framework, we study a dynamic model of bank runs in which more withdrawals raise the risk of bank failure. Even though depositors receive gradual and idiosyncratic shocks, withdrawals occur in clusters. The main mechanism is latent fragility: run-prone depositors accumulate gradually over time and may prefer to wai…
Marco Bianchetti, Camilla Ricci, Marco Scaringi
The growth of peer-to-peer exchanges and the blockchain technology has led to a proliferation of cryptocurrencies and to a massive increase in the number of investors who actually negotiate digital money. Cryptocurrencies trade at prices mainly driven by investor sentiment, becoming a potential source of financial bubbles and instabilitie…
Igor Halperin
The Observable Matrix Dynamics (OMD) approach monitors the time development of complex non-linear systems through the trajectory of a fixed-size distance matrix and its spectrum. We apply it to the S\&P 500 cross section over three crisis decades, the 2001 dot-com bust, the 2007--2008 financial crisis, and the 2020 Covid crash, with three…
Karl T. Ulrich
Firms in non-contractual commerce face the challenge of knowing how many customers they actually have because customers can stop buying without ever saying they have left. Buy-Till-You-Die models address this by estimating each customer's probability of being alive, a quantity called P(alive) and used in every major software tool for dash…
Prashanth BS, Manoj Kumar, Ariful Hoque, Nasser Al Muraqab +3
The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank …
Maria Saveria Mavillonio, Stefano Borgioli, Caterina Giannetti, Chiara Ongari +1
Measuring sentiment from financial news is a central task in economics and finance, yet most existing indicators rely on dictionary-based approaches that infer sentiment from word counts and only partially capture context, negation, and semantic structure. This paper proposes a framework for constructing daily news mood indices using tran…
Bo Li
During the 1999-2019 U.S. housing cycle, three empirical facts present a puzzle: in the boom period, the correlation between income growth and mortgage growth is (1) negative across ZIP codes within a metropolitan area, but (2) positive across metropolitan areas, and (3) the metropolitan areas that experience the worst bust also show the …
Nicola Borri, Kirill Shakhnov
Gateways are trading venues where regulation can change the assets investors can trade. We study this margin using MiCA-EU's Markets in Crypto-Assets Regulation-which led several exchanges to delist USDT pairs for European Economic Area users, while USDC obtained MiCA authorization. First, aggregate market shares and trading volumes barel…
Pietro Saggese, Michael Sigmund, Burkhard Raunig, Esther Segalla +2
Cryptoassets are increasingly entangled with the traditional financial system, and how this activity integrates into national economies and behaves under stress bears on financial stability and the design of public digital money. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirec…
Rian Dolphin, Joe Dursun, Jarrett Blankenship, Katie Adams +1
Form 8-K filings are the primary channel through which U.S. public companies disclose material events, but the SEC item codes attached to them are coarse: a single item spans routine administrative changes and chief executive departures, and many of the most market-moving disclosures fall into a catch-all item. Large language models make …
Andrew Y. Chen, Ivo Welch
This paper examines about 200 published long-short anomaly equity portfolios (Chen and Zimmermann, 2022). Over the period through 2005 (December 2005 and earlier) and across all stocks, their median zero-investment return was an impressive 48 bp per month. Using only post-2005 years (January 2006 onward) reduces this to 19 bp. Using only …
Useong Shin
I propose a cap-axis zero-alpha diagnostic for factor-model evaluation. Whole-stock capitalization prefixes are paired with equal realized exposure to the aggregate market, producing a bridge-alpha curve that localizes pricing errors within the market. Finite-grid HAC-Gaussian inference and residual-block calibration provide size-controll…
Anders G Frøseth
Blandhol (2025) estimates that wealth-tax-induced emigration from Norway reduces long-run GDP by 1.3%. Dansk Industri scaled this figure to argue that a Danish wealth tax would cost billions - a claim central to the 2026 Danish election campaign. We develop a social contagion model in which the emigration rate depends on a visibility-weig…
Arati Uday Kamat
Kaplan-Meier and Cox proportional-hazards survival analysis of 832,941 Solana pump.fun token launches, observed 2026-05-08 to 2026-06-10. Pooled graduation rate 0.198% (Wilson 95% CI [0.189%, 0.208%]; steady-state 0.207%). A post-publication audit established that the collector's coverage was effectively limited to about six minutes after…
Useong Shin
I propose a cap-axis zero-alpha diagnostic for factor-model evaluation. Whole-stock capitalization prefixes are paired with equal realized exposure to the aggregate market, producing a bridge-alpha curve that localizes pricing errors within the market. Finite-grid HAC-Gaussian inference and residual-block calibration provide size-controll…
Hui Gong
Autonomous AI agents are beginning to occupy a position between analytical tools and transacting counterparties. They can interpret goals, call external tools, negotiate with other agents, access data and computation, and in some settings initiate payments or blockchain transactions. This development creates a distinct problem for financi…
David Dai, Ruizhe Jia, Shihao Yu
Prediction markets increasingly list contracts settling on an asset price that holders can move by trading the underlying. We build a model showing that such contracts transfer wealth from prediction-market liquidity traders to manipulators and harm price discovery in the underlying, even as it becomes more liquid. After the launch of Pol…
Praveen Kumar Ashok Kumar, Rafał Sieradzki
This paper investigates the Aggregate Confusion hypothesis (Berg, Kolbel, and Rigobon, 2022) at the firm level by measuring the Disclosure-Performance Gap (DPG), the standardised divergence between a firm's voluntary environmental disclosure ("Talk") and its realised emissions performance ("Walk"). The sample comprises 200 large European …
Nicola Borri, Yukun Liu, Aleh Tsyvinski
Using 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption, we analyze how AI affects firms, markets, and workers. Leveraging the unprecedented size, scope and granu…
Thank you to arXiv for use of its open-access interoperability. Paper metadata is sourced from the arXiv API; StockTools is not affiliated with or endorsed by arXiv. All rights to each paper remain with its authors. Educational only — not financial advice.