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.

Apple has been under pressure this year. It's playing catch-up to its fellow tech giants on artificial intelligence, it's seen its stock fall by double digits since the year began, it closed a store in China for the first time ever this week, and looming US tariffs on Beijing threaten its supply chain. On Thursday, the company released its third-quarter earnings of the fiscal year as investors scrutinize how the iPhone maker might turn things around. Despite the gloomy outlook, the company is still worth more than 3tn, and it beat Wall Street's expectations for profit and revenue this quarter. Apple reported a massive 10% year-over-year increase in revenue to 94.04bn, and 1.57 per share in earnings.

Whether it's poaching top talent away from competitors, acquiring AI startups or proclaiming that it will build data centers the size of Manhattan, Meta has been on a spending spree to boost its artificial intelligence capabilities for months now. The massive splurge is paying off, according to Meta's chief executive. In a new memo posted on Wednesday ahead of the company's quarterly earnings report, Mark Zuckerberg, describes his ambitions for developing what he calls "superintelligence". "Over the last few months we have begun to see glimpses of our AI systems improving themselves," Zuckerberg wrote. "The improvement is slow for now, but undeniable. Developing superintelligence is now in sight."

Microsoft, the world's second-most valuable company, is dumping enormous sums of money into its artificial intelligence efforts. At the same time, the company is earning money hand over fist. The enterprise software giant reported fiscal fourth-quarter results that exceeded expectations on Wednesday as the company races to acquire datacenters and talent, which continues to be investigated by investors. The company predicted its capital expenditure for the next fiscal year would top 100bn, a 14% increase from the year prior. It's the fifth quarter in a row that Microsoft has beaten Wall Street's expectations.

Large Language Models (LLMs) are frequently utilized as sources of knowledge for question-answering. While it is known that LLMs may lack access to real-time data or newer data produced after the model's cutoff date, it is less clear how their knowledge spans across historical information. In this study, we assess the breadth of LLMs' knowledge using financial data of U.S. publicly traded companies by evaluating more than 197k questions and comparing model responses to factual data. We further explore the impact of company characteristics, such as size, retail investment, institutional attention, and readability of financial filings, on the accuracy of knowledge represented in LLMs. Our results reveal that LLMs are less informed about past financial performance, but they display a stronger awareness of larger companies and more recent information. Interestingly, at the same time, our analysis also reveals that LLMs are more likely to hallucinate for larger companies, especially for data from more recent years. The code, prompts, and model outputs are available on GitHub.

In Table-to-Text (T2T) generation, existing approaches predominantly focus on providing objective descriptions of tabular data. However, generating text that incorporates subjectivity, where subjectivity refers to interpretations beyond raw numerical data, remains underexplored. To address this, we introduce a novel pipeline that leverages intermediate representations to generate both objective and subjective text from tables. Our three-stage pipeline consists of: 1) extraction of Resource Description Framework (RDF) triples, 2) aggregation of text into coherent narratives, and 3) infusion of subjectivity to enrich the generated text. By incorporating RDFs, our approach enhances factual accuracy while maintaining interpretability. Unlike large language models (LLMs) such as GPT-3.5, Mistral-7B, and Llama-2, our pipeline employs smaller, fine-tuned T5 models while achieving comparable performance to GPT-3.5 and outperforming Mistral-7B and Llama-2 in several metrics. We evaluate our approach through quantitative and qualitative analyses, demonstrating its effectiveness in balancing factual accuracy with subjective interpretation. To the best of our knowledge, this is the first work to propose a structured pipeline for T2T generation that integrates intermediate representations to enhance both factual correctness and subjectivity.

Tesla has reported its biggest decline in quarterly revenue in more than a decade as CEO Elon Musk's political activity weighs on the electric carmaker brand's reputation. Revenue fell to 22.5bn for the April-June quarter from 25.5bn a year earlier, according to its earnings report, which Tesla released after the closing bell on Wall Street. Analysts on average were expecting revenue of 22.74bn, according to data compiled by LSEG. Revenue from car sales declined by 16 percent. Tesla attributed the revenue dip to a decline in vehicle deliveries.