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Stay up to date on market trends, operational strategies, and the technology that’s shaping the future of investment management.
Understand the differences between cross-sectional, time-series, and statistical risk factor models — and why interpretability matters for portfolio attribution, construction, and decision-making.
The biggest risk in insurance portfolios right now is not a misallocated asset or an unexpected rate move. It is the growing distance between the complexity of what APAC insurers are managing and the capability of the systems they are using to manage it.
Explore how to build an AI-powered markdown knowledge base that grounds AI agents in your team’s documentation and slashes onboarding time.
Most compliance breaches aren’t discovered at order entry. They’re discovered at end of day, or after settlement, during an LP’s operational due diligence review, or by a regulator. For firms without automated pre-trade checks, every order sent is a potential…
This article is not an argument to rip and replace. It’s an honest look at what the market has changed, what modern platforms now offer that older ones structurally cannot, and how to evaluate whether your current setup is still the right fit for where your business is going.
Can the U.S. economy skirt recession again?
Key insights from the “What Does a Strong Data & AI Strategy Look Like for Hedge Funds and Asset Managers in 2026?” breakfast briefing panel discussion
InvestOps attendees described a set of problems that will sound familiar to anyone running investment operations today. Validating data across systems that should agree but don’t. Accessing historical records back to inception for transparency requests and audits. Spending hours on manual cleanup before any downstream work can begin.
Many legacy OMS platforms were built when equity-centric strategies dominated the hedge fund landscape. As strategies have evolved to include meaningful fixed income, FX, swaps, options, futures, and structured products, those platforms have struggled to keep pace.
The cost of running a multi-fund, multi-asset book of record in Excel is much more expensive than investment teams realize. If your book of record lives in a spreadsheet, your book of record lives in a single point of failure.
With the recent marriage of generative AI tools and software development, tasks that required significant manual effort are increasingly automated — enabling teams to shift their focus from repetitive implementation to higher-order design, architecture, and innovation.
Effectively adding AI trading technology to your risk conversations requires more than just a single agent operating as your AI Assistant. An effective AI team is made up of a variety of specialized agents that work together, designed, controlled and orchestrated by their human leader to tackle the multi-step and multi-role activities that are part of real-life trading and portfolio management.
As markets speed up and uncertainties multiply, end-of-day risk reports no longer cut it. Risk management is now an ongoing dialogue throughout the day. Beacon AI agents are the first step from static analytics to interactive intelligence, adding a new voice to the risk conversation as AI grows into a holistic and collaborative approach with traders, quants, and analysts.
Peek behind the code at Clearwater Engineering. Discover how our team tackles challenges like AI orchestration, auto‑scaling, and zero‑trust.