Tootfinder

From fair price to fair volatility: Towards an Efficiency-Consistent Definition of Financial Risk
Sergio Bianchi, Daniele Angelini, Massimiliano Frezza, Augusto Pianese
https://arxiv.org/abs/2508.11649

From fair price to fair volatility: Towards an Efficiency-Consistent Definition of Financial Risk
Volatility, as a primary indicator of financial risk, forms the foundation of classical frameworks such as Markowitz's Portfolio Theory and the Efficient Market Hypothesis (EMH). However, its conventional use rests on assumptions-most notably, the Markovian nature of price dynamics-that often fail to reflect key empirical characteristics of financial markets. Fractional stochastic volatility models expose these limitations by demonstrating that volatility alone is insufficient to capture the fu…

Examining the Relationship between Scientific Publishing Activity and Hype-Driven Financial Bubbles: A Comparison of the Dot-Com and AI Eras
Aksheytha Chelikavada, Casey C. Bennett
https://arxiv.org/abs/2509.11982

Examining the Relationship between Scientific Publishing Activity and Hype-Driven Financial Bubbles: A Comparison of the Dot-Com and AI Eras
Financial bubbles often arrive without much warning, but create long-lasting economic effects. For example, during the dot-com bubble, innovative technologies created market disruptions through excitement for a promised bright future. Such technologies originated from research where scientists had developed them for years prior to their entry into the markets. That raises a question on the possibility of analyzing scientific publishing data (e.g. citation networks) leading up to a bubble for si…

Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News
Ross Koval, Nicholas Andrews, Xifeng Yan
https://arxiv.org/abs/2509.12519 https://…

Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News
Financial news plays a critical role in the information diffusion process in financial markets and is a known driver of stock prices. However, the information in each news article is not necessarily self-contained, often requiring a broader understanding of the historical news coverage for accurate interpretation. Further, identifying and incorporating the most relevant contextual information presents significant challenges. In this work, we explore the value of historical context in the abilit…

JobPulse: A Big Data Approach to Real-Time Engineering Workforce Analysis and National Industrial Policy
Karen S. Markel, Mihir Tale, Andrea Belz
https://arxiv.org/abs/2508.11014

JobPulse: A Big Data Approach to Real-Time Engineering Workforce Analysis and National Industrial Policy
Employment on a societal scale contributes heavily to national and global affairs; consequently, job openings and unemployment estimates provide important information to financial markets and governments alike. However, such reports often describe only the supply (employee job seeker) side of the job market, and skill mismatches are poorly understood. Job postings aggregated on recruiting platforms illuminate marketplace demand, but to date have primarily focused on candidate skills described i…

ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets
Andr\'es L. Su\'arez-Cetrulo, Alejandro Cervantes, David Quintana
https://arxiv.org/abs/2509.11844

ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets
Financial markets are complex, non-stationary systems where the underlying data distributions can shift over time, a phenomenon known as regime changes, as well as concept drift in the machine learning literature. These shifts, often triggered by major economic events, pose a significant challenge for traditional statistical and machine learning models. A fundamental problem in developing and validating adaptive algorithms is the lack of a ground truth in real-world financial data, making it di…

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Yijia Xiao, Edward Sun, Tong Chen, Fang Wu, Di Luo, Wei Wang
https://arxiv.org/abs/2509.11420 https:/…

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language analysis into disciplined, executable trades. Although reasoning LLMs have advanced in step-by-step planning and verification, their application to risk-sensitive financial decisions is underexplored. …

Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets
Avishek Bhandari
https://arxiv.org/abs/2507.08065 https://

Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets
This paper introduces a novel framework for analyzing systemic risk in financial markets through multi-scale network dynamics using Model Context Protocol (MCP) for agent communication. We develop an integrated approach that combines transfer entropy networks, agent-based modeling, and wavelet decomposition to capture information flows across temporal scales implemented in the MCPFM (Model Context Protocol Financial Markets) R package. Our methodology enables heterogeneous financial agents incl…

The US SEC launches Project Crypto, an initiative to "modernize" securities regulations to allow for crypto-based trading (Tanaya Macheel/CNBC)
https://www.cnbc.com/2025/07/31/sec-debuts-project-crypto-to-bring-us-financial-mark…

SEC debuts 'Project Crypto' to bring U.S. financial markets 'on chain'
The Securities and Exchange Commission on Thursday debuted "Project Crypto," a plan to modernize securities regulations to allow crypto-based trading.

Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection
Adriana Watson
https://arxiv.org/abs/2506.14933 https://

Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection
The decentralized finance (DeFi) community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of the technology makes the task of catching and prosecuting offenders particularly challenging. Thus, it is necessary to implement automated detection tools related to policies to address the growing cri…

Data Synchronization at High Frequencies
Xinbing Kong, Cheng Liu, Bin Wu
https://arxiv.org/abs/2507.12220 https://arxiv.org/pdf/2507.…

Data Synchronization at High Frequencies
Asynchronous trading in high-frequency financial markets introduces significant biases into econometric analysis, distorting risk estimates and leading to suboptimal portfolio decisions. Existing synchronization methods, such as the previous-tick approach, suffer from information loss and create artificial price staleness. We introduce a novel framework that recasts the data synchronization challenge as a constrained matrix completion problem. Our approach recovers the potential matrix of high-…

Toward Quantum Enabled Solutions for Real-Time Currency Arbitrage in Financial Markets
Suman Kumar Roy, Rahul Rana, M Girish Chandra, Nishant Kumar, Manoj Nambiar
https://arxiv.org/abs/2509.09289

Toward Quantum Enabled Solutions for Real-Time Currency Arbitrage in Financial Markets
Currency arbitrage leverages price discrepancies in currency exchange rates across different currency pairs to gain risk-free profits. It involves multiple trading, where short-lived price discrepancies require real-time, high-speed processing of vast solution space, posing challenges for classical computing. In this work, we formulate an enhanced mathematical model for the currency arbitrage problem by adding simple cycle preservation constraints, which guarantee trading cycle validity and eli…

Network Contagion in Financial Labor Markets: Predicting Turnover in Hong Kong
Abdulla AlKetbi, Patrick Yam, Gautier Marti, Raed Jaradat
https://arxiv.org/abs/2509.08001 https:/…

Network Contagion in Financial Labor Markets: Predicting Turnover in Hong Kong
Employee turnover is a critical challenge in financial markets, yet little is known about the role of professional networks in shaping career moves. Using the Hong Kong Securities and Futures Commission (SFC) public register (2007-2024), we construct temporal networks of 121,883 professionals and 4,979 firms to analyze and predict employee departures. We introduce a graph-based feature propagation framework that captures peer influence and organizational stability. Our analysis shows a contagio…

DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization
Feliks Ba\'nka (Warsaw University of Technology, Faculty of Electronics,Information Technology), Jaros{\l}aw A. Chudziak (Warsaw University of Technology)
https://arxiv.org/abs/2509.12753

DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization
In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with an ensembled options-based hedging strategy, DeltaHedge enhances risk-adjusted…

To What Extent Can Public Equity Indices Statistically Hedge Real Purchasing Power Loss in Compounded Structural Emerging-Market Crises? An Explainable ML-Based Assessment
Artem Alkhamov, Boris Kriuk
https://arxiv.org/abs/2507.13055

To What Extent Can Public Equity Indices Statistically Hedge Real Purchasing Power Loss in Compounded Structural Emerging-Market Crises? An Explainable ML-Based Assessment
This study investigates the extent to which local public equity indices can statistically hedge real purchasing power loss during compounded structural macro-financial collapses in emerging markets. We employ a non-linear multiplicative real return calculations consistent with Fisher-parity logics for both domestic and foreign investors with a principled quantile regression, tail dependence copula analysis, and Shapley Additive Explanations (SHAP) to assess the explanatory power of macro variab…

Tokenize Everything, But Can You Sell It? RWA Liquidity Challenges and the Road Ahead
Rischan Mafrur
https://arxiv.org/abs/2508.11651 https://arxiv.org/pdf…

Tokenize Everything, But Can You Sell It? RWA Liquidity Challenges and the Road Ahead
The tokenization of real-world assets (RWAs) promises to transform financial markets by enabling fractional ownership, global accessibility, and programmable settlement of traditionally illiquid assets such as real estate, private credit, and government bonds. While technical progress has been rapid, with over \$25 billion in tokenized RWAs brought on-chain as of 2025, liquidity remains a critical bottleneck. This paper investigates the gap between tokenization and tradability, drawing on recen…

💸 Under shadow of Trump warning, Africa pioneers non-dollar payment systems
https://www.japantimes.co.jp/business/2025/06/24/markets/trump-africa-non-dollar-payments/
🆓

Under shadow of Trump warning, Africa pioneers non-dollar payment systems
The move by Africa to create payments systems that do not rely on the greenback mirrors a push by China to develop financial systems independent of Western institutions.

How Kalshi is shaking up US sports betting, as its board member and CFTC Chair nominee Brian Quintenz takes a permissive stance on sports prediction markets (Financial Times)

The Financial Connectome: A Brain-Inspired Framework for Modeling Latent Market Dynamics
Yuda Bi, Vince D Calhoun
https://arxiv.org/abs/2508.02012 https://…

From Static to Dynamic: A Streaming RAG Approach to Real-time Knowledge Base
Yuzhou Zhu
https://arxiv.org/abs/2508.05662 https://arxiv.org/pdf/2508.05662…

From Static to Dynamic: A Streaming RAG Approach to Real-time Knowledge Base
Dynamic streams from news feeds, social media, sensor networks, and financial markets challenge static RAG frameworks. Full-scale indices incur high memory costs; periodic rebuilds introduce latency that undermines data freshness; naive sampling sacrifices semantic coverage. We present Streaming RAG, a unified pipeline that combines multi-vector cosine screening, mini-batch clustering, and a counter-based heavy-hitter filter to maintain a compact prototype set. We further prove an approximation…

Modified Cubic B-spline Based Differential Quadrature Methods for Time-fractional Black-Scholes Equation
Nizamudheen V, Riyasudheen TK, Noufal Asharaf, Shefeeq T
https://arxiv.org/abs/2508.06780

Modified Cubic B-spline Based Differential Quadrature Methods for Time-fractional Black-Scholes Equation
The time-fractional Black-Scholes equation (TFBSE) is intended to price the options for which the underlying price fluctuates within a correlated fractal transmission system. Although the TFBSE is an influential approach for grasping the long-term memory traits of financial markets, the non-local nature of fractional derivatives makes significant challenges in finding an accurate solution. We perform an efficient use of the differential quadrature method (DQM) based on modified cubic B-splines …

Ultrafast Extreme Events: Empirical Analysis of Mechanisms and Recovery in a Historical Perspective
Luca Henrichs, Anton J. Heckens, Thomas Guhr
https://arxiv.org/abs/2509.10376

Ultrafast Extreme Events: Empirical Analysis of Mechanisms and Recovery in a Historical Perspective
To understand the emergence of Ultrafast Extreme Events (UEEs), the influence of algorithmic trading or high-frequency traders is of major interest as they make it extremely difficult to intervene and to stabilize financial markets. In an empirical analysis, we compare various characteristics of UEEs over different years for the US stock market to assess the possible non-stationarity of the effects. We show that liquidity plays a dominant role in the emergence of UEEs and find a general pattern…

Structured Agentic Workflows for Financial Time-Series Modeling with LLMs and Reflective Feedback
Yihao Ang, Yifan Bao, Lei Jiang, Jiajie Tao, Anthony K. H. Tung, Lukasz Szpruch, Hao Ni
https://arxiv.org/abs/2508.13915

Structured Agentic Workflows for Financial Time-Series Modeling with LLMs and Reflective Feedback
Time-series data is central to decision-making in financial markets, yet building high-performing, interpretable, and auditable models remains a major challenge. While Automated Machine Learning (AutoML) frameworks streamline model development, they often lack adaptability and responsiveness to domain-specific needs and evolving objectives. Concurrently, Large Language Models (LLMs) have enabled agentic systems capable of reasoning, memory management, and dynamic code generation, offering a pat…

The UK CMA says it has provisionally found Google meets the legal tests to designate it with "strategic market status" in general search and search advertising (Financial Times)
https://t.co/B2evUJQFta

Google faces UK push to loosen its grip on search
Big Tech giant slams Competition and Markets Authority’s plan as ‘punitive’ and risk to economic growth

Proactive Market Making and Liquidity Analysis for Everlasting Options in DeFi Ecosystems
Hardhik Mohanty, Giovanni Zaarour, Bhaskar Krishnamachari
https://arxiv.org/abs/2508.07068

Kronos: A Foundation Model for the Language of Financial Markets
Yu Shi, Zongliang Fu, Shuo Chen, Bohan Zhao, Wei Xu, Changshui Zhang, Jian Li
https://arxiv.org/abs/2508.02739 h…

Kronos: A Foundation Model for the Language of Financial Markets
The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such as volatility prediction and synthetic data generation. To address these limitations, we propose Kronos, a unified, scalable pr…

Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity
Yimin Du, Guolin Tang
https://arxiv.org/abs/2507.07037 https://

Cognitive Load and Information Processing in Financial Markets: Theory and Evidence from Disclosure Complexity
We develop a theoretical framework for understanding how cognitive load affects information processing in financial markets and test it using exogenous variation in disclosure complexity. Our model distinguishes between attention allocation and cognitive processing capacity, showing that complex information creates differential effects across investor types. Using a comprehensive dataset of corporate disclosures and a novel identification strategy based on regulatory changes, we find that cogni…

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks
Christian Franssen, Iman van Lelyveld, Bernd Heidergott
https://arxiv.org/abs/2507.00600

A Practical Guide to Interpretable Role-Based Clustering in Multi-Layer Financial Networks
Understanding the functional roles of financial institutions within interconnected markets is critical for effective supervision, systemic risk assessment, and resolution planning. We propose an interpretable role-based clustering approach for multi-layer financial networks, designed to identify the functional positions of institutions across different market segments. Our method follows a general clustering framework defined by proximity measures, cluster evaluation criteria, and algorithm sel…

Performative Market Making
Charalampos Kleitsikas, Stefanos Leonardos, Carmine Ventre
https://arxiv.org/abs/2508.04344 https://arxiv.org/pdf/2508.04344

Performative Market Making
Financial models do not merely analyse markets, but actively shape them. This effect, known as performativity, describes how financial theories and the subsequent actions based on them influence market processes, by creating self-fulfilling prophecies. Although discussed in the literature on economic sociology, this deeply rooted phenomenon lacks mathematical formulation in financial markets. Our paper closes this gap by breaking down the canonical separation of diffusion processes between the …

Bounded fuzzy logic control for optimal scheduling of green hydrogen production and revenue maximisation
Sleiman Farah, Jens Jakob S{\o}rensen, Kary Fr\"amling, Matej Simurda
https://arxiv.org/abs/2508.01468

Bounded fuzzy logic control for optimal scheduling of green hydrogen production and revenue maximisation
Hydrogen Purchase Agreements (HPAs) guarantee revenue streams that mitigate the financial risks inherent in the long-term production of green hydrogen from renewable energy sources. However, the intermittency of renewable electricity and the availability of parallel revenue opportunities in both the electricity and hydrogen markets complicate the scheduling of green hydrogen production. The scheduling should maximise the total revenue from short-term sales of electricity and hydrogen against th…

"My read of economic and financial history is that market pricing almost never takes into account the possibility of huge, disruptive events, even when the strong possibility of such events should be obvious. The usual pattern, instead, is one of market complacency until the last possible moment."
https://info…

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Yen Jui Chang, Wei-Ting Wang, Yun-Yuan Wang, Chen-Yu Liu, Kuan-Cheng Chen, Ching-Ray Chang
https://arxiv.org/abs/2507.03963

Quantum Stochastic Walks for Portfolio Optimization: Theory and Implementation on Financial Networks
Financial markets are noisy yet contain a latent graph-theoretic structure that can be exploited for superior risk-adjusted returns. We propose a quantum stochastic walk (QSW) optimizer that embeds assets in a weighted graph: nodes represent securities while edges encode the return-covariance kernel. Portfolio weights are derived from the walk's stationary distribution. Three empirical studies support the approach. (i) For the top 100 S\&P 500 constituents over 2016-2024, six scenario portfolio…

A surge in AI company valuations is driving the popularity of special purpose vehicles, but some involve high fees, opaque structures, and layers of middlemen (Business Insider)
https://africa.businessinsider.com/mar

Investors warn AI FOMO could be fueling a risky bubble in companies like OpenAI and Anthropic
As AI valuations soar into the hundreds of billions, a parallel market of temporary financial shells is proliferating to meet investor FOMO, and many investors are starting to sound the alarm bells that it portends a bubble in the AI gold rush

Signal from Noise Signal from Noise: A Neural Network-Based Denoising Approach for Measuring Global Financial Spillovers
Abdullah Karasan, \"Ozge Sezgin Alp
https://arxiv.org/abs/2509.01156

Signal from Noise Signal from Noise: A Neural Network-Based Denoising Approach for Measuring Global Financial Spillovers
Filtering signal from noise is fundamental to accurately assessing spillover effects in financial markets. This study investigates denoised return and volatility spillovers across a diversified set of markets, spanning developed and developing economies as well as key asset classes, using a neural network-based denoising architecture. By applying denoising to the covariance matrices prior to spillover estimation, we disentangle signal from noise. Our analysis covers the period from late 2014 to…

Automatic Differentiation of Agent-Based Models
Arnau Quera-Bofarull, Nicholas Bishop, Joel Dyer, Daniel Jarne Ornia, Anisoara Calinescu, Doyne Farmer, Michael Wooldridge
https://arxiv.org/abs/2509.03303

Automatic Differentiation of Agent-Based Models
Agent-based models (ABMs) simulate complex systems by capturing the bottom-up interactions of individual agents comprising the system. Many complex systems of interest, such as epidemics or financial markets, involve thousands or even millions of agents. Consequently, ABMs often become computationally demanding and rely on the calibration of numerous free parameters, which has significantly hindered their widespread adoption. In this paper, we demonstrate that automatic differentiation (AD) tec…

Automated Trading System for Straddle-Option Based on Deep Q-Learning
Yiran Wan, Xinyu Ying, Shengzhen Xu
https://arxiv.org/abs/2509.07987 https://arxiv.or…

Automated Trading System for Straddle-Option Based on Deep Q-Learning
Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multi-dimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To …

MM-DREX: Multimodal-Driven Dynamic Routing of LLM Experts for Financial Trading
Yang Chen, Yueheng Jiang, Zhaozhao Ma, Yuchen Cao Jacky Keung, Kun Kuang, Leilei Gan, Yiquan Wu, Fei Wu
https://arxiv.org/abs/2509.05080

MM-DREX: Multimodal-Driven Dynamic Routing of LLM Experts for Financial Trading
The inherent non-stationarity of financial markets and the complexity of multi-modal information pose significant challenges to existing quantitative trading models. Traditional methods relying on fixed structures and unimodal data struggle to adapt to market regime shifts, while large language model (LLM)-driven solutions - despite their multi-modal comprehension - suffer from static strategies and homogeneous expert designs, lacking dynamic adjustment and fine-grained decision mechanisms. To …

Sociophysics models inspired by the Ising model
Pratik Mullick, Parongama Sen
https://arxiv.org/abs/2506.23837 https://arxiv.org/pdf/…

Sociophysics models inspired by the Ising model
The Ising model, originally developed for understanding magnetic phase transitions, has become a cornerstone in the study of collective phenomena across diverse disciplines. In this review, we explore how Ising and Ising-like models have been successfully adapted to sociophysical systems, where binary-state agents mimic human decisions or opinions. By focusing on key areas such as opinion dynamics, financial markets, social segregation, game theory, language evolution, and epidemic spreading, w…

Pricing Fractal Derivatives under Sub-Mixed Fractional Brownian Motion with Jumps
Nader Karimi
https://arxiv.org/abs/2506.24111 https://arxiv.org/pdf/2506.…

In a report, Trump's Working Group on Digital Asset Markets urges regulators to clarify digital asset trading rules and ease adoption of new financial products (Josh Wingrove/Bloomberg)
https://www.bloomberg.com/news/articles/20

Non-Linear and Meta-Stable Dynamics in Financial Markets: Evidence from High Frequency Crypto Currency Market Makers
Igor Halperin
https://arxiv.org/abs/2509.02941 https://

Non-Linear and Meta-Stable Dynamics in Financial Markets: Evidence from High Frequency Crypto Currency Market Makers
This work builds upon the long-standing conjecture that linear diffusion models are inadequate for complex market dynamics. Specifically, it provides experimental validation for the author's prior arguments that realistic market dynamics are governed by higher-order (cubic and higher) non-linearities in the drift. As the diffusion drift is given by the negative gradient of a potential function, this means that a non-linear drift translates into a non-quadratic potential. These arguments were ba…

Crosslisted article(s) found for stat.AP. https://arxiv.org/list/stat.AP/new
[1/1]:
- Network Contagion in Financial Labor Markets: Predicting Turnover in Hong Kong
Abdulla AlKetbi, Patrick Yam, Gautier Marti, Raed Jaradat

Pathwise analysis of log-optimal portfolios
Andrew L. Allan, Anna P. Kwossek, Chong Liu, David J. Pr\"omel
https://arxiv.org/abs/2507.18232 https://ar…

Pathwise analysis of log-optimal portfolios
Based on the theory of càdlàg rough paths, we develop a pathwise approach to analyze stability and approximation properties of portfolios along individual price trajectories generated by standard models of financial markets. As a prototypical example from portfolio theory, we study the log-optimal portfolio in a classical investment-consumption optimization problem on a frictionless financial market modelled by an Itô diffusion process. We identify a fully deterministic framework that enable…

A look at OneChronos, as it seeks to create "smart markets" that would allow firms to trade GPU compute like other commodities, such as electricity and oil (Alex Konrad/Upstarts Media)
https://www.upstartsmedia.com/p/one-chronos-auctionomics-launc…

OneChronos And Auctionomics Launch First Auction Market For GPU Compute
Startup OneChronos teamed up with prize-winning economist Paul Milgrom of Auctionomics to develop the world's first financial market for GPU compute. Now comes the hard part.

Cryptocurrencies and Interest Rates: Inferring Yield Curves in a Bondless Market
Philippe Bergault, S\'ebastien Bieber, Olivier Gu\'eant, Wenkai Zhang
https://arxiv.org/abs/2509.03964

Cryptocurrencies and Interest Rates: Inferring Yield Curves in a Bondless Market
In traditional financial markets, yield curves are widely available for countries (and, by extension, currencies), financial institutions, and large corporates. These curves are used to calibrate stochastic interest rate models, discount future cash flows, and price financial products. Yield curves, however, can be readily computed only because of the current size and structure of bond markets. In cryptocurrency markets, where fixed-rate lending and bonds are almost nonexistent as of early 2025…

Covariance Matrix Estimation for Positively Correlated Assets
Weilong Liu, Yanchu Liu
https://arxiv.org/abs/2507.01545 https://arxiv.…

Covariance Matrix Estimation for Positively Correlated Assets
The comovement phenomenon in financial markets creates decision scenarios with positively correlated asset returns. This paper addresses covariance matrix estimation under such conditions, motivated by observations of significant positive correlations in factor-sorted portfolio monthly returns. We demonstrate that fine-tuning eigenvectors linked to weak factors within rotation-equivariant frameworks produces well-conditioned covariance matrix estimates. Our Eigenvector Rotation Shrinkage Estima…

CTBench: Cryptocurrency Time Series Generation Benchmark
Yihao Ang, Qiang Wang, Qiang Huang, Yifan Bao, Xinyu Xi, Anthony K. H. Tung, Chen Jin, Zhiyong Huang
https://arxiv.org/abs/2508.02758

CTBench: Cryptocurrency Time Series Generation Benchmark
Synthetic time series are essential tools for data augmentation, stress testing, and algorithmic prototyping in quantitative finance. However, in cryptocurrency markets, characterized by 24/7 trading, extreme volatility, and rapid regime shifts, existing Time Series Generation (TSG) methods and benchmarks often fall short, jeopardizing practical utility. Most prior work (1) targets non-financial or traditional financial domains, (2) focuses narrowly on classification and forecasting while negle…

The UK CMA says it has provisionally found Google meets the legal tests to designate it with "strategic market status" in general search and search advertising (Financial Times)
https://www.ft.com/content/26aa105f-fabb-4061-bd6d-fd13ba94f691

UK regulator proposes loosening Google’s control of its search engine
Competition and Markets Authority says market should be more open and competitive

Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting
Micha{\l} Wawer, Jaros{\l}aw A. Chudziak
https://arxiv.org/abs/2506.16813

Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting
Traditional technical analysis methods face limitations in accurately predicting trends in today's complex financial markets. This paper introduces ElliottAgents, an multi-agent system that integrates the Elliott Wave Principle with AI for stock market forecasting. The inherent complexity of financial markets, characterized by non-linear dynamics, noise, and susceptibility to unpredictable external factors, poses significant challenges for accurate prediction. To address these challenges, the s…

Sources: Apple is locked in last-minute EU negotiations over App Store changes to avoid fines set for this week, and is expected to offer "steering" concessions (Barbara Moens/Financial Times)
https://www.ft.com/content/b5d51870-e864-4aa5-b998-c0d2994a7e2…

Apple locked in last-minute App Store negotiations to avoid Brussels fines
Tech group must make concessions to comply with Digital Markets Act or be hit by escalating levies from Thursday

Can News Predict the Direction of Oil Price Volatility? A Language Model Approach with SHAP Explanations
Romina Hashamia, Felipe Maldonado
https://arxiv.org/abs/2508.20707 https…

Can News Predict the Direction of Oil Price Volatility? A Language Model Approach with SHAP Explanations
Financial markets can be highly sensitive to news, investor sentiment, and economic indicators, leading to important asset price fluctuations. In this study we focus on crude oil, due to its crucial role in commodity markets and the global economy. Specifically, we are interested in understanding the directional changes of oil price volatility, and for this purpose we investigate whether news alone -- without incorporating traditional market data -- can effectively predict the direction of oil …

Complexity of Financial Time Series: Multifractal and Multiscale Entropy Analyses
Oday Masoudi, Farhad Shahbazi, Mohammad Sharifi
https://arxiv.org/abs/2507.23414 https://

Complexity of Financial Time Series: Multifractal and Multiscale Entropy Analyses
We employed Multifractal Detrended Fluctuation Analysis (MF-DFA) and Refined Composite Multiscale Sample Entropy (RCMSE) to investigate the complexity of Bitcoin, GBP/USD, gold, and natural gas price log-return time series. This study provides a comparative analysis of these markets and offers insights into their predictability and associated risks. Each tool presents a unique method to quantify time series complexity. The RCMSE and MF-DFA methods demonstrate a higher complexity for the Bitcoin…

FanDuel partners with CME Group to offer event-based contracts, letting users wager "yes" or "no" on financial events and benchmarks, starting later this year (Dean Seal/Wall Street Journal)
https://www.

Crosslisted article(s) found for q-fin.TR. https://arxiv.org/list/q-fin.TR/new
[1/1]:
- Non-Linear and Meta-Stable Dynamics in Financial Markets: Evidence from High Frequency Crypto Cur...
Igor Halperin

QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning
Xiangdong Liu, Jiahao Chen
https://arxiv.org/abs/2508.20467 https…

QTMRL: An Agent for Quantitative Trading Decision-Making Based on Multi-Indicator Guided Reinforcement Learning
In the highly volatile and uncertain global financial markets, traditional quantitative trading models relying on statistical modeling or empirical rules often fail to adapt to dynamic market changes and black swan events due to rigid assumptions and limited generalization. To address these issues, this paper proposes QTMRL (Quantitative Trading Multi-Indicator Reinforcement Learning), an intelligent trading agent combining multi-dimensional technical indicators with reinforcement learning (RL)…

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Arif Pathan
https://arxiv.org/abs/2506.17244

Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Short-term sentiment forecasting in financial markets (e.g., stocks, indices) is challenging due to volatility, non-linearity, and noise in OHLC (Open, High, Low, Close) data. This paper introduces a novel CMG (Chaos-Markov-Gaussian) framework that integrates chaos theory, Markov property, and Gaussian processes to improve prediction accuracy. Chaos theory captures nonlinear dynamics; the Markov chain models regime shifts; Gaussian processes add probabilistic reasoning. We enhance the framework…

Causal Interventions in Bond Multi-Dealer-to-Client Platforms
Paloma Mar\'in, Sergio Ardanza-Trevijano, Javier Sabio
https://arxiv.org/abs/2506.18147 h…

Causal Interventions in Bond Multi-Dealer-to-Client Platforms
The digitalization of financial markets has shifted trading from voice to electronic channels, with Multi-Dealer-to-Client (MD2C) platforms now enabling clients to request quotes (RfQs) for financial instruments like bonds from multiple dealers simultaneously. In this competitive landscape, dealers cannot see each other's prices, making a rigorous analysis of the negotiation process crucial to ensure their profitability. This article introduces a novel general framework for analyzing the RfQ pr…

Deep Reinforcement Learning for Optimal Asset Allocation Using DDPG with TiDE
Rongwei Liu, Jin Zheng, John Cartlidge
https://arxiv.org/abs/2508.20103 https://

Deep Reinforcement Learning for Optimal Asset Allocation Using DDPG with TiDE
The optimal asset allocation between risky and risk-free assets is a persistent challenge due to the inherent volatility in financial markets. Conventional methods rely on strict distributional assumptions or non-additive reward ratios, which limit their robustness and applicability to investment goals. To overcome these constraints, this study formulates the optimal two-asset allocation problem as a sequential decision-making task within a Markov Decision Process (MDP). This framework enables …

Crosslisted article(s) found for q-fin.GN. https://arxiv.org/list/q-fin.GN/new
[1/1]:
- Non-Linear and Meta-Stable Dynamics in Financial Markets: Evidence from High Frequency Crypto Cur...
Igor Halperin

Overparametrized models with posterior drift
Guillaume Coqueret, Martial Laguerre
https://arxiv.org/abs/2506.23619 https://arxiv.org/…

Overparametrized models with posterior drift
This paper investigates the impact of posterior drift on out-of-sample forecasting accuracy in overparametrized machine learning models. We document the loss in performance when the loadings of the data generating process change between the training and testing samples. This matters crucially in settings in which regime changes are likely to occur, for instance, in financial markets. Applied to equity premium forecasting, our results underline the sensitivity of a market timing strategy to sub-…

Predicting Stock Market Crash with Bayesian Generalised Pareto Regression
Sourish Das
https://arxiv.org/abs/2506.17549 https://arxiv.…

Predicting Stock Market Crash with Bayesian Generalised Pareto Regression
This paper develops a Bayesian Generalised Pareto Regression (GPR) model to forecast extreme losses in Indian equity markets, with a focus on the Nifty 50 index. Extreme negative returns, though rare, can cause significant financial disruption, and accurate modelling of such events is essential for effective risk management. Traditional Generalised Pareto Distribution (GPD) models often ignore market conditions; in contrast, our framework links the scale parameter to covariates using a log-line…