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.

High-dimensional decision-making tasks, such as business partner selection, involve evaluating large candidate pools with heterogeneous numerical, categorical, and textual features. MAS, a hierarchical multi-agent framework that decomposes evaluation into three layers: a Planner Agent that designs strategies, Specialized Agents that perform role-specific assessments, and a Supervisor Agent that integrates their outputs. To support systematic evaluation, we also introduce a curated benchmark dataset of venture capital co-investments, featuring diverse firm attributes and ground-truth syndicates. MAS consistently outperforms single-agent and debate-based multi-agent baselines, achieving up to 10-15% higher match rates. Analysis of agent reasoning shows that planners are most responsive to domain-informed prompts, specialists produce complementary feature coverage, and supervisors play an important role in aggregation. Our implementation is available at this anonymous link. In real-world decision-making, practitioners often navigate high-dimensional data including extensive option sets and numerous evaluative features (Sandanayake et al., 2018; Sigle et al., 2023). Business partner selection which includes partner shortlisting and strategic alliance formation exemplifies this challenge (Mindruta et al., 2016): firms often face a vast pool of potential candidates, each described by diverse attributes ranging from quantitative indicators (e.g., financial metrics, geographic presence) to text-rich information (e.g., strategic fit, investment preferences) (Shah & Swaminathan, 2008). The scale and complexity of such data can easily overwhelm human decision-makers, incurring significant costs (Li et al., 2008). This underscores the need for intelligent systems capable of analyzing large candidate sets and diverse features. Large language models (LLMs) have emerged as promising tools for addressing reasoning tasks in data-rich domains (Lee et al., 2025; Mischler et al., 2024). With appropriate prompting (e.g., few-shot learning) or information retrieval techniques (e.g., RAG), these models can identify salient features using only feature and task descriptions, achieving performance comparable to established methods (Li et al., 2025a; Jeong et al., 2024).

There are inefficiencies in financial markets, with unexploited patterns in price, volume, and cross-sectional relationships. While many approaches use large-scale transformers, we take a domain-focused path: feed-forward and recurrent networks with curated features to capture subtle regularities in noisy financial data. This smaller-footprint design is computationally lean and reliable under low signal-to-noise, crucial for daily production at scale. At Increase Alpha, we built a deep-learning framework that maps over 800 U.S. equities into daily directional signals with minimal computational overhead. The purpose of this paper is twofold. First, we outline the general overview of the predictive model without disclosing its core underlying concepts. Second, we evaluate its real-time performance through transparent, industry standard metrics. Forecast accuracy is benchmarked against both naive baselines and macro indicators. The performance outcomes are summarized via cumulative returns, annualized Sharpe ratio, and maximum drawdown. The best portfolio combination using our signals provides a low-risk, continuous stream of returns with a Sharpe ratio of more than 2.5, maximum drawdown of around 3%, and a near-zero correlation with the S&P 500 market benchmark. We also compare the model's performance through different market regimes, such as the recent volatile movements of the US equity market in the beginning of 2025. Our analysis showcases the robustness of the model and significantly stable performance during these volatile periods. Collectively, these findings show that market inefficiencies can be systematically harvested with modest computational overhead if the right variables are considered. This report will emphasize the potential of traditional deep learning frameworks for generating an AI-driven edge in the financial market.

In financial markets, Graph Neural Networks have been successfully applied to modeling relational data, effectively capturing nonlinear inter-stock dependencies. Yet, existing models often fail to efficiently propagate messages during macroeconomic shocks. In this paper, we propose OmniGNN, an attention-based multi-relational dynamic GNN that integrates macroeconomic context via heterogeneous node and edge types for robust message passing. Central to OmniGNN is a sector node acting as a global intermediary, enabling rapid shock propagation across the graph without relying on long-range multi-hop diffusion. The model leverages Graph Attention Networks (GAT) to weigh neighbor contributions and employs Transformers to capture temporal dynamics across multiplex relations. Experiments show that OmniGNN outperforms existing stock prediction models on public datasets, particularly demonstrating strong robustness during the COVID-19 period.

"Double, double toil and trouble; Fire burn and cauldron bubble." As Shakespeare's witches foretold chaos through cryptic prophecies, modern capital markets grapple with systemic risks concealed by opaque AI systems. According to IMF, the August 5, 2024, plunge in Japanese and U.S. equities can be linked to algorithmic trading yet absent from existing AI incidents database exemplifies this transparency crisis . Current AI incident databases, reliant on crowdsourcing or news scraping, systematically overlook capital market anomalies, particularly in algorithmic and high - frequency trading. We address this critical gap by proposing a regulatory - grade global database that elegantly synthesi s es post - trade reporting frameworks with proven incident documentation models from healthcare and aviation. Our framework's temporal data omission technique masking timestamps while preserving percentage - based metrics enables sophisticated cross - jurisdictional analysis of emerging risks while safeguarding confidential business information. Synthetic data validation ( modelled after real life published incidents, sentiments, data) (n=2,999 incidents) reveals compelling patterns: systemic risks transcending geographical boundaries, market manipulation clusters distinctly identifiable via K - means algorithms, and AI system typology exerting significantly greater influence on trading behaviour than geographical location, This tripartite solution empowers regulators with unprecedented cross - jurisdictional oversight, financial institutions with seamless compliance integration, and investors with critical visibility into previously obscured AI - driven vulnerabilities. We call for immediate action to strengthen risk management and foster resilience in AI - driven financial markets against the volatile "cauldron" of AI - driven syste m ic risks.

The exponential technological breakthrough of the Fin-Tech industry has significantly enhanced user engagement through sophisticated advisory chatbots. However, large-scale fine-tuning of LLMs can occasionally yield unprofessional or flippant remarks, such as "With that money, you're going to change the world," which, though factually correct, can be contextually inappropriate and erode user trust. The scarcity of domain-specific datasets has led previous studies to focus on isolated components, such as reasoning-aware frameworks or the enhancement of human-like response generation. To address this research gap, we present Fin-Solution 2.O, an advanced solution that 1) introduces the multi-turn financial conversational dataset, Fin-V ault, and 2) incorporates a unified model, Fin-Ally, which integrates commonsense reasoning, politeness, and human-like conversational dynamics. Fin-Ally is powered by COMET -BART -embedded commonsense context and optimized with a Direct Preference Optimization (DPO) mechanism to generate human-aligned responses. The novel Fin-V ault dataset, consisting of 1,417 annotated multi-turn dialogues, enables Fin-Ally to extend beyond basic account management to provide personalized budgeting, real-time expense tracking, and automated financial planning. Our comprehensive results demonstrate that incorporating commonsense context enables language models to generate more refined, textually precise, and professionally grounded financial guidance, positioning this approach as a next-generation AI solution for the FinTech sector.

Market making is a popular trading strategy, which aims to generate profit from the spread between the quotes posted at either side of the market. It has been shown that training market makers (MMs) with adversarial reinforcement learning allows to overcome the risks due to changing market conditions and to lead to robust performances. Prior work assumes, however, that MMs keep quoting throughout the trading process, but in practice this is not required, even for ``registered'' MMs (that only need to satisfy quoting ratios defined by the market rules). In this paper, we build on this line of work and enrich the strategy space of the MM by allowing to occasionally not quote or provide single-sided quotes. Towards this end, in addition to the MM agents that provide continuous bid-ask quotes, we have designed two new agents with increasingly richer action spaces. The first has the option to provide bid-ask quotes or refuse to quote. The second has the option to provide bid-ask quotes, refuse to quote, or only provide single-sided ask or bid quotes. We employ a model-driven approach to empirically compare the performance of the continuously quoting MM with the two agents above in various types of adversarial environments. We demonstrate how occasional refusal to provide bid-ask quotes improves returns and/or Sharpe ratios. The quoting ratios of well-trained MMs can basically meet any market requirements, reaching up to 99.9$\%$ in some cases.