Chip giant Nvidia beat Wall Street's expectations for revenue and upcoming sales, easing investor concerns about heavy artificial intelligence (AI) spending that have unsettled markets. In its quarterly earnings report on Wednesday, the firm said revenue for the three months to October jumped 62% to $57bn, driven by demand for its chips used in AI data centres. Sales from that division rose 66% to more than $51bn. Fourth-quarter sales forecasts in the range of $65bn also topped estimates, sending shares in Nvidia more than 3% higher in after-hours trading. Nvidia, the world's most valuable company, is seen as a bellwether for the AI boom.

Financial documents like earning reports or balance sheets often involve long tables and multi-page reports. Large language models have become a new tool to help numerical reasoning and understanding these documents. However, prompt quality can have a major effect on how well LLMs perform these financial reasoning tasks. Most current methods tune prompts on fixed datasets of financial text or tabular data, which limits their ability to adapt to new question types or document structures, or they involve costly and manually labeled/curated dataset to help build the prompts. We introduce a self-improving prompt framework driven by data-augmented optimization. In this closed-loop process, we generate synthetic financial tables and document excerpts, verify their correctness and robustness, and then update the prompt based on the results. Specifically, our framework combines a synthetic data generator with verifiers and a prompt optimizer, where the generator produces new examples that exposes weaknesses in the current prompt, the verifiers check the validity and robustness of the produced examples, and the optimizer incrementally refines the prompt in response. By iterating these steps in a feedback cycle, our method steadily improves prompt accuracy on financial reasoning tasks without needing external labels. Evaluation on DocMath-Eval benchmark demonstrates that our system achieves higher performance in both accuracy and robustness than standard prompt methods, underscoring the value of incorporating synthetic data generation into prompt learning for financial applications.

Next-gen networking tech, sometimes powered by light instead of electricity, is emerging as a critical piece of AI infrastructure. The new era of Silicon Valley runs on networking--and not the kind you find on LinkedIn. As the tech industry funnels billions into AI data centers, chip makers both big and small are ramping up innovation around the technology that connects chips to other chips, and server racks to other server racks. Networking technology has been around since the dawn of the computer, critically connecting mainframes so they can share data. In the world of semiconductors, networking plays a part at almost every level of the stack--from the interconnect between transistors on the chip itself, to the external connections made between boxes or racks of chips.

SoftBank sells Nvidia stake for $5.8 billion to fund AI bets SoftBank Group founder Masayoshi Son is aggressively seeking to capitalize on booming investment in AI and chips, even as he scales back other investments. SoftBank Group sold its entire stake in Nvidia for $5.83 billion to help bankroll artificial intelligence investments, even as investors question the amount of capital pouring into a technology with uncertain returns. Founder Masayoshi Son has been unwinding positions to pay for a plethora of AI projects, from Stargate data centers with OpenAI and Oracle to robot manufacturing sites in the United States. The Nvidia exit coincides with a growing debate about whether spending by big tech firms like Meta Platforms and Alphabet -- expected to surpass $1 trillion in coming years -- will produce commensurate returns. SoftBank's stock slid more than 10% in Tokyo on Wednesday, highlighting how investors remain nervous about lofty tech valuations.

Small and Medium Enterprises (SMEs), particularly freelancers and early-stage businesses, face unique financial management challenges due to limited resources, small customer bases, and constrained data availability. This paper presents the development and deployment of an integrated financial prediction system that combines accounts receivable prediction and cash flow forecasting specifically designed for SME operational constraints. Our system addresses the gap between enterprise-focused financial tools and the practical needs of freelancers and small businesses. The solution integrates two key components: a binary classification model for predicting invoice payment delays, and a multi-module cash flow forecasting model that handles incomplete and limited historical data. A prototype system has been implemented and deployed as a web application with integration into Cluee's platform, a startup providing financial management tools for freelancers, demonstrating practical feasibility for real-world SME financial management.

Large Language Models are increasingly adopted in financial applications to support investment workflows. However, prior studies have seldom examined how these models reflect biases related to firm size, sector, or financial characteristics, which can significantly impact decision-making. This paper addresses this gap by focusing on representation bias in open-source Qwen models. We propose a balanced round-robin prompting method over approximately 150 U.S. equities, applying constrained decoding and token-logit aggregation to derive firm-level confidence scores across financial contexts. Using statistical tests and variance analysis, we find that firm size and valuation consistently increase model confidence, while risk factors tend to decrease it. Confidence varies significantly across sectors, with the Technology sector showing the greatest variability. When models are prompted for specific financial categories, their confidence rankings best align with fundamental data, moderately with technical signals, and least with growth indicators. These results highlight representation bias in Qwen models and motivate sector-aware calibration and category-conditioned evaluation protocols for safe and fair financial LLM deployment.

Three of the biggest US tech companies reported record profits and record infrastructure spending on Wednesday, fueling speculation about a possible AI market bubble. Three of the biggest US tech giants--Microsoft, Meta, and Google--sent investors a blunt message when they reported quarterly earnings on Wednesday: Their lavish spending on AI infrastructure is only just getting started. Meta said that its capital expenditure would total between $70 billion and $72 billion this year, up from its previous lower forecast of $66 billion to $72 billion. Next year, Meta's chief financial officer Susan Li said that she expected the company's spending would be "notably larger." The social media giant's soaring investment matches its soaring revenue: Meta reported raking in $51.24 billion last quarter, up 26 percent year-over-year.

Financial narratives from U.S. Securities and Exchange Commission (SEC) filing reports and quarterly earnings call transcripts (ECTs) are very important for investors, auditors, and regulators. However, their length, financial jargon, and nuanced language make fine-grained analysis difficult. Prior sentiment analysis in the financial domain required a large, expensive labeled dataset, making the sentence-level stance towards specific financial targets challenging. In this work, we introduce a sentence-level corpus for stance detection focused on three core financial metrics: debt, earnings per share (EPS), and sales. The sentences were extracted from Form 10-K annual reports and ECTs, and labeled for stance (positive, negative, neutral) using the advanced ChatGPT-o3-pro model under rigorous human validation. Using this corpus, we conduct a systematic evaluation of modern large language models (LLMs) using zero-shot, few-shot, and Chain-of-Thought (CoT) prompting strategies. Our results show that few-shot with CoT prompting performs best compared to supervised baselines, and LLMs' performance varies across the SEC and ECT datasets. Our findings highlight the practical viability of leveraging LLMs for target-specific stance in the financial domain without requiring extensive labeled data.

We present a multi-agent, AI-driven framework for fundamental investing that integrates macro indicators, industry-level and firm-specific information to construct optimized equity portfolios. The architecture comprises: (i) a Macro agent that dynamically screens and weights sectors based on evolving economic indicators and industry performance; (ii) four firm-level agents -- Fundamental, Technical, Report, and News -- that conduct in-depth analyses of individual firms to ensure both breadth and depth of coverage; (iii) a Portfolio agent that uses reinforcement learning to combine the agent outputs into a unified policy to generate the trading strategy; and (iv) a Risk Control agent that adjusts portfolio positions in response to market volatility. We evaluate the system on the constituents by the CSI 300 Index of China's A-share market and find that it consistently outperforms standard benchmarks and a state-of-the-art multi-agent trading system on risk-adjusted returns and drawdown control. Our core contribution is a hierarchical multi-agent design that links top-down macro screening with bottom-up fundamental analysis, offering a robust and extensible approach to factor-based portfolio construction.

People walk past a large electronic screen showing the Samsung logo at a train station in Seoul on Tuesday. Samsung Electronics has posted its biggest quarterly profit in more than three years, reflecting booming memory chip demand while AI development accelerates globally. South Korea's largest company reported an operating profit of 12.1 trillion won ($8.5 billion) in the September quarter, compared with analysts' projection for 9.70 trillion won, according to a preliminary earnings report released on Tuesday. Revenue climbed to 86 trillion won. The company will provide a full financial statement with net income and divisional breakdowns later this month. The results may bolster confidence among investors betting on the durability of demand for AI servers and memory chips.