A fund’s reported track record provides valuable insight into historical performance, but it may not tell the full story. Alternative investments often have limited return histories, infrequent valuations, and reporting practices that can make returns appear smoother than the investment’s underlying economic behavior.
These characteristics can make it more difficult to assess risk, understand return drivers, and evaluate how an investment may behave across different market environments.
iCapital’s proprietary Generalized Multi-Asset Model, or GMAM, was recently published in The Journal of Alternative Investments1. The framework is designed to provide additional insight into the underlying economic behavior and risk exposures of alternative investments while supporting Architect’s backcasting capabilities, which help extend analysis beyond a fund’s available track record.
Designed for the Realities of Alternative Investment Data
Many traditional analytical approaches apply factor regressions directly to reported fund returns. While reported performance is an important input, alternative investments often have valuation and reporting characteristics that can make it difficult to fully observe underlying economic behavior.
GMAM was developed specifically to address these realities. The framework jointly estimates:
- Underlying economic returns
- Factor exposures
- Fund-specific NAV smoothing
By analyzing these components together, GMAM seeks to distinguish a fund’s underlying economic behavior from the effects of valuation and reporting practices. This can provide additional insight into the drivers of risk and return that may not be fully reflected in reported performance alone.
This distinction is important because funds with similarly smooth reported returns may exhibit different underlying economic risk profiles.
GMAM does not replace reported performance. Rather, it uses available data within a framework designed for the limited, infrequent, and potentially smoothed return histories commonly found in alternative investments.
Extending the Historical View
A limited track record presents a separate challenge. Newer funds may not have performance histories that span different market cycles, periods of stress, or changing economic conditions.
Using a fund’s reported performance and longer-term factor data, GMAM can generate an estimated return history prior to the fund’s inception date. This process is known as backcasting.
A backcast is not actual fund performance. Rather, it’s is a model-based estimate of how a fund may have behaved during periods when reported returns were unavailable. When reported return data exists, that information remains part of the analysis.
What a Backcast Is and Isn’t
Is: A model-generated estimate designed to extend the analytical history of a fund.
Is Not: Actual historical fund performance or a prediction of future returns.
Backcasting can help advisors explore questions such as:
- How might the fund have behaved across earlier market environments?
- Which market factors may have influenced its estimated performance?
- How might its underlying economic risk differ from what is visible in reported returns?
By extending the available history, backcasting can provide additional perspective when evaluating funds with relatively short track records.
From Research to Adoption
GMAM supports the analytical framework used within Architect, iCapital’s portfolio construction tool. Architect enables advisors to evaluate alternatives and structured investments alongside traditional assets through portfolio analysis, stress testing, and risk-factor visualization.By incorporating GMAM’s backcasting capabilities, Architect helps bring additional historical perspective into the fund evaluation process.This connection demonstrates how iCapital translates proprietary quantitative research into practical advisor tools.
Recently Published and Recognized
The GMAM methodology is detailed in GMAM (Generalized Multi-Asset Model): Standardizing Alternative Investments Returns Estimation, authored by Arnav Sheth, Zheng Wang, Weidong Jin, Clinton Tepper, and Aniket Inamdar.
The paper was recently published in The Journal of Alternative Investments and selected for the journal’s “In Focus” feature, which highlights research considered particularly timely, innovative, or relevant to the industry.
In the paper, the authors compare GMAM with a traditional ordinary least squares factor regression approach.
In the research test sample, GMAM achieved a higher average out-of-sample fit to reported returns. The analysis also found that model-estimated economic returns may exhibit greater volatility than reported returns, highlighting the potential impact of valuation and reporting practices on observed fund performance.
The publication provides greater visibility into the research supporting GMAM and the analytical framework behind Architect’s backcasting capabilities.
Advancing Alternative Investment Analysis
Alternative investment present analytical challenges that differ from those of traditional public-market assets. Limited return histories, infrequent valuations, and evolving portfolio structures often require approaches designed specifically for alternatives.
GMAM was developed to address these realities, helping provide a deeper understanding of risk exposures, return drivers, and historical behavior. The framework’s recent publication in The Journal of Alternative Investments offers greater visibility into the research supporting iCapital’s analytical capabilities.
Through Architect, that research is translated into advisor-facing technology, helping wealth managers incorporate alternative investments into portfolio analysis and client conversations with greater confidence.
Interested in Learning More?
To learn more about GMAM, Architect, or the research featured in The Journal of Alternative Investments, please contact:
[email protected]
1. Arnav Sheth, Zheng Wang, Weidong Jin, Clinton Tepper, and Aniket Inamdar, “GMAM (Generalized Multi-Asset Model): Standardizing Alternative Investments Returns Estimation,” The Journal of Alternative Investments 29, no. 1 (Summer 2026): 110-140.
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