If information can impact an investment decision, it belongs in a centralized system that supports that decision.
TL;DR: Most investment teams still run their process across a collection of disconnected tools: a risk system, shared drives, spreadsheets of price targets, research notes, sell-side reports, and alternative data platforms. Each addresses a separate part of the investment management process and alone does not provide a holistic view of the portfolio or the decisions made to get there. A modern portfolio intelligence platform should bring the portfolio, research, risk, internal views, external market data, and into one live environment that AI agents can interrogate.
Does your portfolio analytics system have everything you need to make an investment decision?
The test is simple: if the information is relevant to the investment decision, it should be found in the place where that decision is made. This is easier said than done since the type of information that managers rely on during due diligence and monitoring of their portfolio has grown.
Investment teams now have access to (and consume) more data and analytics than ever. The data is usually located in many different places at once causing friction, and missed opportunities that are exceptionally hard to measure and quantify.
For instance, your risk system can show how each position contributes to factor exposure, but it may not know anything about the analyst’s thesis or the PM’s overarching view. The research management system holds the thesis and the supporting memos, but may not show how the position is affecting portfolio volatility. Price targets are in a spreadsheet on a local or shared drive, sell-side research, expert network calls, filings, and alternative data sit in still more places. Every one of these inputs can change an investment decision like sizing. When the PM needs an answer to a question, often it will take hours to chase down all the data needed to answer it and analysts will have multiple touchpoints with various platforms and databases.
The cost across the entire organization is difficult to measure, even though we understand it intuitively: time spent rebuilding context and chasing down inputs requires hours of manual work that highly paid analysts could be investing into investment due diligence or new idea generation.
- "The solution is a centralized investment system of record. The portfolio, research assets, and risk should be kept in one environment, fit to the way the team works. EDS was built around that premise."
Start with the live portfolio
Your portfolio should be the primary working surface and the launchpad for deeper analysis. In EDS, a manager can see the complete long/short book with the fields that matter to the firm: exposure, liquidity, short interest, internal price targets, expected returns, factor sensitivities, and data from the risk model the firm uses. Because no two investment processes are identical, views are configurable baked on each analyst or PM. Benchmarks can be included for relative strategies or removed for an absolute-return view. The same book can be reorganized by different categories such as direction, GICS sector, industry group, or any other classification captured in the system. This “bucketing” helps analysts and PMs get a quick view of their book in exactly the way they think about the world.
Every position should carry the firm’s view with it
Clicking into a security should feel like you are inside a sophisticated cockpit for the position. The concepts you care about most should be surfaced: factor risk, factor performance, market data, and the firm’s internal research on the same screen.
In EDS, an internal price target can be plotted against the stock’s historical price, allowing a portfolio manager to see how the team’s view evolved through time. The position record can also show the covering analyst, the last update, implied return, as well as the base and upside / downside scenarios.
Staying in that same workflow, the manager can retrieve the latest model and search the research management system for internal notes, sell-side research, filings, alt data, earnings calls, and expert network transcripts. A decision to add, trim, or exit a position can be evaluated with the full context without having to log into separate platforms.
Screenshot: Security cockpit with internal price targets, market data, and portfolio context
Put internal conviction in market context
Capturing price targets is table stakes but using those targets as inputs and understanding them in the context of the rest of your portfolio is what you should expect a great system to do. Once internal price targets are logged, a strong portfolio analytics platform can answer questions like “what return is the portfolio expected to generate based on our targets and how does that compare to returns baked on current street estimates?
We know that we live in a world of imperfect information, and sometimes data can get stale. EDS uses internal targets where they exist and can supplement uncovered names with street estimates, flagging the source for the manager. The platform then calculates a forward-looking return for the portfolio, weighted by exposure based on the bes- available target prices.
It can also compare the team’s aggregate view with consensus across the total portfolio and separately across the long and short books. You can see, for instance, if your short book is priced more bearishly than consensus and click in to understand why that is, and what is contributing to that overall bearish positioning.
Make risk a core part of your investment management process
LPs have pressed fundamental managers on risk for years, and now all managers looking for large allocations from institutional investors should be more than just “factor aware". Risk should be incorporated in their views and analytical workflows, and easily referenced if a question arises.
EDS makes these conversations easier for managers. The system decomposes the portfolio into idiosyncratic risk, market risk, industry risk, and other factor exposures using the firm’s chosen model. A manager can open a factor, examine how its contribution changed over time, and drill down to the positions driving the move. They can use that information to create a report and present risk as a central pillar in their investment management process. LPs expect to see robust risk systems they can comfortably call "institutional".
Monthly investor calls also become more data-backed when you have the right systems in place. The question “Why did momentum exposure spike?” can be linked to handful of names, which are linked to a thesis, which is usually linked to the overarching view of the portfolio manager. Having access to all of this in one system is the difference between providing LPs with a confident evidence-backed answer and “we need to get back to you on that”
Screenshot: Portfolio risk decomposition with factor-level drilldown
Give AI the context to be useful
This unified architecture also creates a huge opportunity for AI. We have recently written an article detailing how the right data foundation can be a revolutionary unlock for managers looking to leverage AI in their investment workflows. Many of the drawbacks of AI models, and the reasons for measured adoption in agentic AI can be addressed with the right data structure and process.
Fusion AI, our AI product, pulls from the firm’s own research, price targets, positions, and risk context, while grounding responses in source material and relying on verified analytics engines for calculations. Hosted agents can turn that context into repeatable workflows. One example is a review that flags names where recent filings or buy-side positioning shifts conflict with the internal thesis, then organizes the output into a prioritized list for the investment team.
The same governed intelligence can be accessed outside the EDS interface through the EDS MCP server. An analyst working in an approved AI environment can query portfolio data, factor scores, and research notes or run saved workflows such as an earnings preview scorecard or monthly strategy review, subject to the same entitlements and auditability. Learn more in “EDS MCP Layer: AI Answers Grounded in Your Fund.”
It really is a new world with AI and there is no reason why your portfolio analytics should have blind spots or be stuck in the past without leveraging AI for your time-consuming, onerous tasks and some of your most challenging questions. This is why so many managers are now centralizing their workflows in a modern RMS and using agentic AI as the answers layer on top of that data.
Set a higher standard for portfolio analytics
Your portfolio analytics platform should not be missing the information your most critical decisions depend on. Especially given that you already have that information, it just needs to be organized better.
The firm’s analysis, price targets, market data, consensus estimates, risk decomposition, alternative data, and research should be connected in one environment. The payoff is beyond mere convenience and quick answers, it is the ability to ask better questions, test ideas while they are still relevant, find missed opportunities, and move from evidence to action with more confidence.
Choose three important questions the investment team asked last week and measure how long they took to answer. Now imagine you can ask an AI agent the same questions and it comes back with the answer in seconds. This is the reality many funds are living in right now. Give it a try.
Try EDS for free →
