Insights & Announcements
A weekly Newsletter on technology applications in investment management with an AI / LLM and automation angle. Curated news, announcements, and posts, primarily directly from sources. We apply some of the ROI's Kubro(TM) Engine tools at the backend for production, yet with a human in the loop (for now). It's a start, and it will evolve on a weekly basis.
Disclaimers: content not-fully-human-verified, with AI summaries below. AI/LLMs may hallucinate and provide inaccurate summaries. Select items only, not intended as a comprehensive view. For information purposes only. Please DM with feedback and requests.
The article by Tianyi Zhang and Mu Chen, submitted on October 24, 2025, presents a novel Chain-of-Thought (CoT) summarization framework designed to condense financial news into concise, event-driven summaries. This framework incorporates user-specified keywords to generate personalized outputs that highlight only the most relevant contexts. The personalized summaries serve as an intermediate layer aiding language models in producing investor-focused narratives, thereby bridging the gap between raw financial news and actionable investment insights. The work addresses information overload faced by financial advisors and investors due to irrelevant content and noise in financial news. 🔗 Source: View Source | Found on Nov 12, 2025
The paper by Anmar Kareem and Alexander Aue evaluates classical time series models—ARIMA, SARIMA, GARCH, and EGARCH—for forecasting Bitcoin prices using daily data from 2010 to 2020. Models were trained on the first 90% of data and tested on the last 10%, with accuracy measured by MAE, RMSE, AIC, and BIC. ARIMA performed best for short-run log-price forecasts, while EGARCH was superior in modeling volatility by capturing asymmetric shock responses. The study demonstrates that classical models remain effective for short-term Bitcoin forecasting despite its high volatility and suggests future research incorporating machine learning and macroeconomic factors. 🔗 Source: View Source | Found on Nov 12, 2025
The paper titled "TimeFlow: Towards Stochastic-Aware and Efficient Time Series Generation via Flow Matching Modeling," submitted on 11 November 2025 by He Panjing, Cheng Mingyue, Li Li, and Zhang XiaoHan, addresses the challenge of generating high-quality time series data that captures intrinsic stochasticity. The authors propose TimeFlow, a novel stochastic differential equation (SDE)-based flow matching framework with an encoder-only architecture. TimeFlow features a component-wise decomposed velocity field and an augmented stochastic term to improve representational expressiveness. It supports both unconditional and conditional generation tasks within a unified framework. Experiments on diverse datasets show that TimeFlow outperforms strong baselines in generation quality, diversity, and efficiency. 🔗 Source: View Source | Found on Nov 13, 2025
The paper titled "Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction," submitted on 10 November 2025 by So-Yoon Cho and colleagues, introduces Diffolio, a diffusion model designed to improve multivariate financial time-series forecasting and portfolio construction. Diffolio uses a denoising network with hierarchical attention at both asset and market levels, incorporating a correlation-guided regularizer based on a stable target correlation matrix. Tested on daily excess returns of 12 industry portfolios, Diffolio outperforms existing probabilistic forecasting methods in accuracy and portfolio performance, achieving higher Sharpe ratios for mean-variance tangency portfolios and greater certainty equivalents for growth-optimal portfolios. 🔗 Source: View Source | Found on Nov 12, 2025
Google announced Private AI Compute, a new cloud-based AI processing platform launched on November 11, 2025, that combines its Gemini models with strong privacy and security measures similar to on-device processing. This platform enables faster, more helpful AI responses by leveraging advanced reasoning and computational power beyond on-device capabilities. Private AI Compute protects user data through a multi-layered system built on Google’s Secure AI Framework, using custom Tensor Processing Units (TPUs) and Titanium Intelligence Enclaves (TIE). It ensures data isolation via remote attestation and encryption so that sensitive information remains accessible only to the user, not even Google. 🔗 Source: View Source | Found on Nov 11, 2025
IDC’s research reveals organizations experienced an average of nine cloud security incidents in 2024, with 89% reporting increases year-over-year. Cloud-native application protection platforms (CNAPPs) are among the top three security investments for 2025, addressing challenges legacy tools cannot. In 37% of organizations, CISOs now oversee cloud security management, aligning security with business priorities. Organizations use an average of ten cloud security tools, causing complexity and blind spots. Generative AI is enhancing threat detection and incident response. Seventy-one percent of organizations see value in investing in unified SecOps platforms combining CNAPP, XDR, SIEM, generative AI, and threat intelligence over the next two years. 🔗 Source: View Source | Found on Nov 06, 2025
Anthropic is investing $50 billion in American computing infrastructure by building custom data centers with Fluidstack in Texas and New York, with additional sites planned. These facilities, designed to maximize efficiency for Anthropic’s workloads, will create about 800 permanent jobs and 2,400 construction jobs, coming online throughout 2026. The project supports the Trump administration’s AI Action Plan to maintain U.S. AI leadership and strengthen domestic technology infrastructure. Anthropic serves over 300,000 business customers, with large accounts growing nearly sevenfold in the past year. Fluidstack was chosen for its agility in delivering gigawatts of power rapidly. 🔗 Source: View Source | Found on Nov 12, 2025
At its November 11, 2025 Financial Analyst Day, AMD outlined a long-term growth plan targeting over 35% revenue CAGR and non-GAAP EPS above $20. The company expects data center revenue to grow over 60% CAGR and aims for more than 50% server CPU market share with its EPYC processors. AMD’s Instinct MI350 GPUs are the fastest ramping product in company history, with “Helios” systems launching in Q3 2026 and MI500 series planned for 2027. The AI PC portfolio has grown 2.5x since 2024, with next-gen “Gorgon” and “Medusa” processors delivering up to 10x AI performance gains by 2024. AMD also targets over 40% client market share and more than $50 billion in design wins since 2022 across adaptive computing segments. 🔗 Source: View Source | Found on Nov 11, 2025
The research to uncover useful quantum computing applications involves five stages. Stage I is the discovery of new abstract quantum algorithms like Simon’s, Grover's, and quantum phase estimation algorithms, which may theoretically outperform classical methods but have limited practical utility initially. Stage II focuses on identifying specific problem instances where these algorithms show true advantage over classical methods, often in complex cases such as finding a molecule's lowest energy state. Stage III assesses whether these problem instances translate into real-world use cases, highlighting challenges due to knowledge gaps between quantum experts and application specialists. Stage IV involves engineering optimization, resource estimation, and error correction implementation for practical use. 🔗 Source: View Source | Found on Nov 13, 2025
Microsoft’s AI Diffusion Report, published on November 10, 2025, reveals that over 1.2 billion people have used AI tools in less than three years, marking a faster adoption rate than the internet, personal computers, or smartphones. The report analyzes data from more than 100 countries and introduces three indices: the AI Frontier Index, AI Infrastructure Index, and AI Diffusion Index to track breakthroughs, capacity for scaling, and usage impact. It highlights that adoption is fastest where digital infrastructure is strongest but notes nearly four billion people lack basic access to participate in the AI economy. The report calls for collaborative efforts to expand infrastructure, develop skills, and promote responsible policies to ensure equitable access and shared prosperity through AI. 🔗 Source: View Source | Found on Nov 11, 2025
The rapid rise of Chinese open source and open-weight AI models like DeepSeek R1, Alibaba’s Qwen 3, Moonshot AI’s Kimi K2, and MiniMax M2 marks a new phase in global AI competition by offering lower costs, local deployment, and strong performance. MiniMax M2 claims twice the speed of Claude Sonnet at 8% of the cost and shows superior safety and jailbreak resistance compared to other models. Testing revealed varied safety levels: MiniMax M2 excelled in safe-response rates and jailbreak resilience, while Kimi and Qwen were more vulnerable. Holistic AI’s Governance Platform offers enterprise-grade testing, monitoring, and filtering to make these models production-ready. 🔗 Source: View Source | Found on Nov 13, 2025
Man Group’s AlphaGPT is a proprietary agentic AI research workflow designed as a digital three-person team that rapidly processes vast financial data and follows strict investment methodology. It generates dozens of testable investment hypotheses within minutes, writes production-grade Python code to implement ideas, and rigorously evaluates results using statistical and economic criteria. AlphaGPT demonstrates creativity by exploring novel concepts beyond human scale but requires safeguards against risks like p-hacking and hallucinations through stringent methodology, prompt engineering, and multi-stage validation. Human oversight remains integral, with comprehensive logging and dual-track review ensuring transparency. The system currently excels in systematic equity research with plans for scalable adaptation across asset classes. 🔗 Source: View Source | Found on Nov 13, 2025