As the legal war over how to regulate prediction markets rages on, financial institutions are embracing the industry anyway. When Troy Dixon first suggested incorporating prediction markets into the electronic trading platform where he works, he was met with incredulity. "People told us we were crazy," Dixon, Tradeweb's cohead of global markets, tells WIRED. But after the company announced it was partnering with Kalshi in February, Dixon says, the mood changed dramatically. "We've been inundated with calls," he says.

The'Claude crash' references the plug-in legal products added by the AI firm Anthropic to its Claude Cowork office assistant. The'Claude crash' references the plug-in legal products added by the AI firm Anthropic to its Claude Cowork office assistant. How to deal with the "Claude crash": Relx should keep buying back shares, then buy more A s the FTSE 100 index bobs along close to all-time highs, it is easy to miss the quiet share price crash in one corner of the market. It's got a name - the "Claude crash", referencing the plug-in legal products added by the AI firm Anthropic to its Claude Cowork office assistant. This launch, or so you would think from the panicked stock market reaction in the past few weeks, marks the moment when the AI revolution rips chunks out of some of the UK's biggest public companies - those in the dull but successful "data" game, including Relx, the London Stock Exchange Group, Experian, Sage and Informa.

The launch of the Anthropic legal tool will reignite fears of job losses caused by the AI boom. The launch of the Anthropic legal tool will reignite fears of job losses caused by the AI boom. Anthropic's launch of AI legal tool hits shares in European data services firms Tue 3 Feb 2026 08.38 ESTLast modified on Tue 3 Feb 2026 08.54 EST European publishing and legal software companies have suffered sharp declines in their share prices after the US artificial intelligence firm Anthropic announced a tool aimed at companies' in-house lawyers. The UK publishing group Pearson's shares fell by 4%, while the information and analytics firm Relx plunged nearly 11% on the London stock exchange, and the Dutch software company Wolters Kluwer dropped almost 9% in Amsterdam. Stocks in the London Stock Exchange Group and the credit reporting company Experian fell by more than 7%, amid fears over AI's impact on data companies. Anthropic, the company behind the popular chatbot Claude, said its tool could automate legal work such as contract reviewing, non-disclosure agreement triage, compliance workflows, legal briefings and templated responses.

Despite the existence of many applied scenarios where the relationship between a pair changes, change point methods specifically designed for paired data are quite scarce.

That is, testing whether two random vectors X and Y are independent given a third random vector Z . For example, there are conditional independence tests based on conditional densities [SW08], characteristic functions [SW07], empirical likelihood ratios [SW14], discretization [Mar05; Hua10], permutation [Dor+14; Sen+17], kernels [Fuk+07; Zha+11; SP11], copulas [BRT12], and conditional mutual information [Run18b]. Also, there are many conditional independence tests based on regressing X on Z and Y on Z followed by testing for independence between the residuals [Pat+09; Pet+14; Ram14; FFX20; ZZG17; Zha+19]. Unfortunately, conditional independence tests oftentimes struggle to control the Type-I error in finite samples, as shown by Shah and Peters [SP20]. In fact, Shah and Peters [SP20] prove that conditional independence testing is fundamentally impossible without making further assumptions. This issue has sparked significant interest in conditional independence testing over the last several years. We begin by providing an overview of recent advances in conditional independence testing. Afterwards, we discuss how our work addresses limitations in the existing literature. Finally, we motivate our work by reviewing key applications of conditional independence tests for time series in areas such as variable selection and causal discovery.

Financial data analysis plays a pivotal role in today's business landscape [1, 2, 3, 4, 5, 6, 7], including credit risk assessment (such as loan prediction and credit scoring), fraud detection, and cost optimization, etc. However, when we develop solutions to address financial problems, we will inevitably encounter a number of key challenges [1, 2, 3, 4, 5]. For example, financial data is often voluminous, dynamically and frequently generated in real time, and distributed across diverse locations, making it challenging to process and analyze in a centralized manner[1], e.g., the New Y ork Stock Exchange (NYSE) alone has billions of transactions per day. Similarly, other major exchanges, such as the Shanghai Stock Exchange (SSE) and the London Stock Exchange (LSE), also generate vast amounts of stock data. Additionally, noise and missing values unavoidably occur in financial data, which can cause results and predictions to be skewed (or even completely wrong). These challenges require firms to come up with more efficient and smarter solutions. In recent decades, machine learning has achieved remarkable success across various domains [8, 9, 10], owing to its effective generalization ability and adaptability, and has also received increasing attention in financial data analysis [11, 12], such as credit risk assessment, resource allocation, and cost optimization. However, these classical (supervised) machine learning based solutions, such as logistic regression and random forest, usually implicitly assume that 1) all the data is stored and centralized at one location, typically a single machine, and that we have full access to the entire data; 2) these algorithms expect to run on a single machine with minimal concerns for memory or disk storage limitations; and 3) the provided data is clean and free from outliers introduced by malicious adversaries, as it is stored at a single location equipped with high security protection mechanisms to prevent data corruption. Nonetheless, these assumptions do not always hold in practice.

Despite the growing attention to time series forecasting in recent years, many studies have proposed various solutions to address the challenges encountered in time series prediction, aiming to improve forecasting performance. However, effectively applying these time series forecasting models to the field of financial asset pricing remains a challenging issue. There is still a need for a bridge to connect cutting-edge time series forecasting models with financial asset pricing. To bridge this gap, we have undertaken the following efforts: 1) We constructed three datasets from the financial domain; 2) We selected over ten time series forecasting models from recent studies and validated their performance in financial time series; 3) We developed new metrics, msIC and msIR, in addition to MSE and MAE, to showcase the time series correlation captured by the models; 4) We designed financial-specific tasks for these three datasets and assessed the practical performance and application potential of these forecasting models in important financial problems. We hope the developed new evaluation suite, FinTSBridge, can provide valuable insights into the effectiveness and robustness of advanced forecasting models in finanical domains.

Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks. However, their ability to generalize to dynamic, unseen tasks, particularly in numerical reasoning, remains a challenge. Existing benchmarks mainly evaluate LLMs on problems with predefined optimal solutions, which may not align with real-world scenarios where clear answers are absent. To bridge this gap, we design the Agent Trading Arena, a virtual numerical game simulating complex economic systems through zero-sum games, where agents invest in stock portfolios. Our experiments reveal that LLMs, including GPT-4o, struggle with algebraic reasoning when dealing with plain-text stock data, often focusing on local details rather than global trends. In contrast, LLMs perform significantly better with geometric reasoning when presented with visual data, such as scatter plots or K-line charts, suggesting that visual representations enhance numerical reasoning. This capability is further improved by incorporating the reflection module, which aids in the analysis and interpretation of complex data. We validate our findings on NASDAQ Stock dataset, where LLMs demonstrate stronger reasoning with visual data compared to text. Our code and data are publicly available at https://github.com/wekjsdvnm/Agent-Trading-Arena.git.

This manuscript introduces the Hype-Adjusted Probability Measure developed in the context of a new Natural Language Processing (NLP) approach for stock return and volatility forecasting. A novel sentiment score equation is presented to capture component and memory effects and assign dynamic parameters, enhancing the impact of intraday news data on forecasting next-period volatility for selected U.S. semiconductor tickers. This approach integrates machine learning techniques to analyze and improve the predictive value of news. Building on the research of Geman et al [6], this work improves forecast accuracy by addressing news bias, memory, and weight, and incorporating shifts in senti-ment direction. Finally, we propose the Hype-Adjusted Probability Measure, proving its existence and uniqueness, and discuss its theoretical applications in finance for NLP-based stock return forecasting, outlining future research pathways inspired by its concepts.