In recent years, China's bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data's irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-making. To tackle these issues, we propose EMDLOT (Explainable Multimodal Deep Learning for Time-series), a novel framework for multi-class bond default prediction. EMDLOT integrates numerical time-series (financial/macroeconomic indicators) and unstructured textual data (bond prospectuses), uses Time-Aware LSTM to handle irregular sequences, and adopts soft clustering and multi-level attention to boost interpretability. Experiments on 1994 Chinese firms (2015-2024) show EMDLOT outperforms traditional (e.g., XGBoost) and deep learning (e.g., LSTM) benchmarks in recall, F1-score, and mAP, especially in identifying default/extended firms. Ablation studies validate each component's value, and attention analyses reveal economically intuitive default drivers. This work provides a practical tool and a trustworthy framework for transparent financial risk modeling.

Larry Ellison, the chair and chief technology officer of Oracle, is a supporter of Donald Trump and has regularly appeared at the White House. Larry Ellison, the chair and chief technology officer of Oracle, is a supporter of Donald Trump and has regularly appeared at the White House. Oracle co-founder's shares rose by 40% in early trading, valuing his fortune at $393bn, just ahead of Musk's $384bn US tech billionaire Larry Ellison is neck-and-neck with Elon Musk in the contest to be the world's richest person after briefly overtaking the Tesla chief executive on Wednesday Ellison's wealth surged after Oracle, the business software company in which he owns a stake of 41%, reported better than expected financial results. Oracle shares rose by more than 40% in early trading, at one point valuing the business software company at approximately $960bn (£707bn) and Ellison's stake at $393bn, just ahead of Musk's fortune of $384bn, according to Bloomberg's billionaires index. However, Ellison's lead was short-lived as the stock closed at $328, a rise of 36% valuing Ellison's shareholding at $378bn and putting Musk back ahead.

London-listed miner Anglo American and Canada's Teck Resources plan to merge, marking the sector's second-biggest mergers and acquisitions deal ever and forging a new global copper-focused heavyweight. Under the proposed deal, which will require regulatory approvals and was announced on Tuesday, Anglo American shareholders will own 62.4 percent of the new company, Anglo Teck, while shareholders in Teck would hold 37.6 percent. The deal to form the world's fifth-largest copper company is also a big bet on copper by Anglo. Glencore's $90bn merger with Xstrata in 2013 remains the largest mining deal in history. Copper, used in the power and construction sectors, is set to benefit from burgeoning demand spurred by electric vehicles and artificial intelligence.

Earnings calls represent a uniquely rich and semi-structured source of financial communication, blending scripted managerial commentary with unscripted analyst dialogue. Although recent advances in financial sentiment analysis have integrated multi-modal signals, such as textual content and vocal tone, most systems rely on flat document-level or sentence-level models, failing to capture the layered discourse structure of these interactions. This paper introduces a novel multi-modal framework designed to generate semantically rich and structurally aware embeddings of earnings calls, by encoding them as hierarchical discourse trees. Each node, comprising either a monologue or a question-answer pair, is enriched with emotional signals derived from text, audio, and video, as well as structured metadata including coherence scores, topic labels, and answer coverage assessments. A two-stage transformer architecture is proposed: the first encodes multi-modal content and discourse metadata at the node level using contrastive learning, while the second synthesizes a global embedding for the entire conference. Experimental results reveal that the resulting embeddings form stable, semantically meaningful representations that reflect affective tone, structural logic, and thematic alignment. Beyond financial reporting, the proposed system generalizes to other high-stakes unscripted communicative domains such as tele-medicine, education, and political discourse, offering a robust and explainable approach to multi-modal discourse representation. This approach offers practical utility for downstream tasks such as financial forecasting and discourse evaluation, while also providing a generalizable method applicable to other domains involving high-stakes communication.

Salesforce has slashed another 4,000 jobs from its customer support workforce as the tech giant doubles down on artificial intelligence, even as the company reports strong financial results. AI agents now reportedly handle about one million customer conversations. In a recent episode of The Logan Bartlett Show, CEO Marc Benioff justified the cuts by saying he "needs less heads" as Salesforce invests heavily in AI across its operations. Earlier this year, Benioff boasted that AI was already doing 30 to 50 percent of the work, which he framed as efficiency gains – a 17 percent cost reduction achieved after shedding 1,000 people in February. On Wednesday, the Slack owner reported revenue topped 10.2bn for the quarter ending July 31, up 10 percent from the same period last year.

Chipmaker Nvidia set a fresh sales record in the second quarter, surpassing Wall Street expectations for its artificial intelligence chips. But shares of the chip giant still dropped 2.3% in after hours trading, in a sign that investors' worries of an AI bubble and the repercussions of Donald Trump's trade wars are not quelled. Nvidia's financial report was the first test of investor appetite since last week's mass AI-stock selloff, when several tech stocks saw shares tumble last week amid growing questions over whether AI-driven companies are being overvalued. On Wednesday, Nvidia reported an adjusted earnings per share of 1.08 on 46.74bn in revenue, surpassing Wall Street's projection of 1.01 in earnings per share on 46.05bn in revenue, according to Fact Set data. But investors had high expectations for the company.

Chip giant Nvidia is set to release its latest earnings report – and the results could move the entire US stock market. Over the past two years, the chipmaker has risen to become the world's most valuable company, with a market capitalisation of more than 4 trillion. When Nvidia announces its earnings on Wednesday, investors will get to see how the tech giant has been faring amid the tumult of President Donald Trump's trade salvoes and concerns about whether artificial intelligence has been overhyped. Nvidia specialises in making the graphics processing units (GPUs) that power AI, including the Blackwell B200, marketed as the world's most powerful chip. The California-based company's chips have become essential to the world's largest tech companies, including Microsoft, Meta, Amazon and Alphabet, since AI exploded into the mainstream with the release of OpenAI's generative AI chatbot, ChatGPT, in November 2022.

We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. Additionally, we provide an extensive analysis of various models' performance on our benchmark. By making SECQUE publicly available, we aim to facilitate further research and advancements in financial AI.

--Corporate credit rating serves as a crucial intermediary service in the market economy, playing a key role in maintaining economic order . Existing credit rating models rely on financial metrics and deep learning. However, they often overlook insights from non-financial data, such as corporate annual reports. T o address this, this paper introduces a corporate credit rating framework that integrates financial data with features extracted from annual reports using FinBERT, aiming to fully leverage the potential value of unstructured text data. In addition, we have developed a large-scale dataset, the Comprehensive Corporate Rating Dataset (CCRD), which combines both traditional financial data and textual data from annual reports. The experimental results show that the proposed method improves the accuracy of the rating predictions by 8-12%, significantly improving the effectiveness and reliability of corporate credit ratings.

The US's largest companies have spent 2025 locked in a competition to spend more money than one another, lavishing 155bn on the development of artificial intelligence, more than the US government has spent on education, training, employment and social services in the 2025 fiscal year so far. Based on the most recent financial disclosures of Silicon Valley's biggest players, the race is about to accelerate to hundreds of billions in a single year. Over the past two weeks, Meta, Microsoft, Amazon, and Alphabet, Google's parent, have shared their quarterly public financial reports. Each disclosed that their year-to-date capital expenditure, a figure that refers to the money companies spend to acquire or upgrade tangible assets, already totals tens of billions. Capex, as the term is abbreviated, is a proxy for technology companies' spending on AI because the technology requires gargantuan investments in physical infrastructure, namely data centers, which require large amounts of power, water and expensive semiconductor chips.