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Managing alternatives has never been straightforward. Private assets are illiquid and can be complex to model, responding to macro conditions in ways that are hard to anticipate and demanding planning that most forecasting tools are not equipped to support. They can also be hard to exit quickly — so when interest rates spike or geopolitical shocks arrive, the quality and responsiveness of economic forecasting matters enormously.
The continued rise of alternatives is, of course, impossible to ignore. According to JP Morgan’s Alternative Investments Outlook 2026, private markets have grown rapidly over the past decade, with assets under management now exceeding $20 trillion globally.1 Given ongoing market volatility and an investment landscape facing sudden shifts due to geopolitics and AI advancements, investment management firms can no longer rely on more traditional forecasting approaches.
Most firms managing portfolios with alternatives allocations already leverage capital market assumptions. The question is: are they current, independent, and rigorous enough to be relied upon?
During a recent webinar, Addepar and Oxford Economics — one of the world's leading independent economic forecasting firms founded in 1981 in partnership with Oxford University — explored what institutional-grade macro forecasting looks like as a result of our recent integration, and how it can benefit alternatives strategy and planning.
Independent assumptions produce better analysis
Capital market assumptions (CMAs) are only as useful as they are credible. But, there is a structural tension in how most capital market assumptions are produced. Typically, providers are asset managers themselves, and when the company producing assumptions also manages capital, forecasts may be subject to bias — which can be problematic for quality analysis and effective alternatives strategy.
Addepar partnered with Oxford Economics because the company operates outside that structure.
“Contrary to other providers of CMAs, Oxford Economics does not have any trading exposure or other institutional bias,” says Alessandro Theiss, the firm’s Director of Financial Modelling and Scenarios. “Instead, our focus purely reflects our modelling and expert analysis of the economic outlook and developments in financial markets. At the heart of the company is our proprietary Global Economic Model. It's the most comprehensive macro-financial model of its kind, and within that same model, we develop the economic outlook for all countries, ensuring an elevated consistency of the projections.”
For EMEA wealth managers, this matters in practice. Integrating with an independent company avoids problematic bias so users can more confidently leverage assumptions for strategic total portfolio decision-making.
The integration also allows users to compare their forecasts directly against Oxford Economics. “It allows users to compare house views to Oxford Economics' neutral baseline,” says Rajiv Sharma, Senior Director of Product, Addepar. “This strengthens investment committee oversight, governance, and documentation.”
Most CMAs aren’t keeping pace with a fast-moving world
The problem with many CMAs is not just bias, but latency. In a stable macro environment, when allocations were more traditional, quarterly or annual updates were sufficient. But for alternatives-heavy portfolios facing a volatile market, regular updates to assumptions is hugely advantageous.
Oxford Economics updates its global forecast bi-monthly. The company also reacts to geopolitical shocks or significant market events to ensure the most up-to-date CMAs where possible. For example, when conflict escalated in Iran in early 2026, Oxford Economics published updated forecasts for all 85 countries and asset markets it tracks within ten days of the first strikes, applying changes to the model and tracing cross-country transmissions through to inflation, growth, and asset returns.
Within Addepar Navigator, where the Oxford Economics integration lives on the platform, those updates flow through automatically. “Historically, clients had to create and update their own CMAs as market conditions changed,” says Casey Robinson, Senior Navigator Product Specialist, Addepar. “Leveraging Oxford Economics allows those updates to flow through automatically, reflecting current market conditions without manual intervention.”
Modelling the full complexity of an alternatives portfolio
As alternatives span a wide range of asset classes, including private equity, real assets, hedge funds, fixed income, and commodities, nuanced and granular modelling is vital. A model that lacks the necessary depth will produce assumptions that are incomplete at best and misleading at worst.
Oxford Economics' Global Economic Model spans granular asset price models across government and corporate bonds, equities, exchange rates, commercial real estate, commodities, and hedge funds. And, the data underpinning it is also increasingly proprietary, which is beneficial for asset classes where data quality is an issue.
“Developing proprietary data sets is a big focus,” says Theiss. “Many data providers have curtailed access, driving us to more holistically own the data we project.” For the corporate bond return model, Oxford Economics built regional-specific returns from bottom-up aggregation of individual bonds, giving full oversight into which bonds are included and the ability to clean data for outliers.
The result is assumptions that are granular enough to reflect the complexity of a modern alternatives portfolio.
AI as powerful tool, but not a replacement
The question of how AI fits into economic forecasting is one the industry is still exploring. With the promise of faster processing, broader data coverage, and more dynamic modelling, AI could transform forecasting. For alternatives managers dealing with complex, multi-asset portfolios across multiple geographies, technology that makes rigorous scenario analysis faster and more accessible has obvious appeal. But there are definite risks and limitations, particularly relating to trust, accuracy, and transparency.
Oxford Economics is investing heavily in AI, without shying away from its current limitations. “We are developing an AI-driven interaction with our Global Economic Model based on client feedback,” says Theiss. “This will allow users to spell out a scenario narrative, which the AI translates into specific model assumptions.” The technology lets managers stress-test complex, real-world scenarios in plain language, without needing to manually input assumptions, making the modelling process faster and more accessible.
But Theiss is measured about how far that goes. “Forecasting is as much an art as it is a science,” he says. “We are still some way off from a fully AI-generated process, as you can't easily codify the expertise of more than 400 economists with deep knowledge of specific countries and sectors.”
For alternatives managers, it's a useful corrective: the model is a powerful tool, but expert human judgement remains at the centre of it. Find out more about the role of human oversight in fintech AI in our recent blog: Humans in the loop: Why human oversight still matters in AI.
As alternatives continue to command a greater share of institutional portfolios, the quality of the assumptions underpinning them matters more than ever. Independent, frequently updated, and built for complexity, the Oxford Economics integration within Addepar Navigator gives investment teams the rigorous foundation they need to plan with confidence.
Reference:
Alternative Investments Outlook 2026, J.P Morgan Asset Management, 2026.