The democratization of quant in wealth management

How do we empower wealth managers with quantitative tools?

Growth is the name of the game when it comes to the wealth management industry. Increased M&A within the Registered Investment Advisor (RIA) space, the transfer of wealth from baby boomers to younger generations, the growing demand for more personalized investment strategies like direct indexing and the rise of Separately Managed Accounts (SMAs) and Unified Managed Accounts (UMAs), are just some of the forces fueling the growth of this segment.

But, in the face of this growth, RIAs and wealth managers are grappling with increasingly complex client portfolios, which require more sophisticated tools to manage risk, tax, asset allocation and model portfolio creation. That’s where the integration of quantitative risk models into a wealth management workflow, can really add value. In particular, a risk model that recognizes the specific needs of a portfolio manager or risk manager at a wealth management firm, is indispensable.

Introducing the Axioma fund allocation models

In the past, wealth managers often lacked access to the sophisticated risk models employed by institutional asset managers. Even when they attempted to use institutional-level tools, they would quickly discover that these models were not tailored to their specific needs. The Axioma fund allocation models – which come in two flavors – are multi-asset class risk models, designed specifically for portfolios of ETFs and mutual funds. The Axioma Fund Allocation Model (AXFAM4.1) captures fund investment risk through exposures to a curated set of fund category risk factor returns and fund specific risks. The Axioma Worldwide Fund Allocation Model (AXWWFAM4.1) combines the capabilities of the Axioma Worldwide Equity Factor Risk Model and the Axioma Fund Allocation Model into a single model. It captures fund investment risk through a combination of exposures to the Axioma Worldwide Equity Factor Risk Model (AXWW4) for equity ETFs, single name stocks and a curated set of fund and market category factors.

But first, let’s back up: What are risk models? 

A factor risk model is a tool that helps reduce the complexity of portfolios by identifying and isolating the key factors that drive risk and return. Rather than treating every single fund or security as a separate entity, the model groups them into categories (e.g. large-cap equity) or factors (e.g. Momentum).

For example, a typical portfolio might include dozens or even hundreds of mutual funds, ETFs, and individual stocks, each with its own unique risk profile. Without a factor risk model, a wealth manager would need to evaluate the risk of each asset individually. Factor models, however, condense these assets into a smaller set of risk factors, allowing the manager to see the bigger picture. This streamlined approach has many advantages including reducing operational risk, as wealth managers won’t need to manually input or research the specific risk profiles of every single fund or security.

What challenges do the fund allocation models solve?

These new risk models are particularly valuable because they address several pain points. For one, they allow managers to use a single model for all asset classes – equities, fixed income, and commodities – rather than having to rely on multiple models where a manager may miss the cross-correlations among asset classes.

Second, these models offer wide coverage, spanning equity and fixed income mutual funds, ETFs, and single-name securities. This ensures that wealth managers don’t need to worry about whether certain funds or securities are adequately covered by the model.

Third, the models are particularly well equipped to handle tax-efficient strategies – a key concern for high-net-worth clients.

Fourth, the models incorporate machine learning to help model the specific returns of funds with similar specific return dynamics. With this feature, the Comparable Fund Specific Covariance (CFSC), users can easily find replacement securities and manage the risk of different funds tracking the same benchmark. 

And finally, this model in particular is highly parsimonious, meaning they are designed to use the fewest number of factors to capture the most important drivers of risk and return. This makes the models well-suited for portfolio construction.

What other use cases are the models particularly well-suited for?

 
As mentioned above, tax-efficient strategies is one of the most significant. For example, in transition management, when wealth transfers from one generation to the next or when portfolios from one RIA firm needs to be moved to another because of an acquisition, the Axioma fund allocation models can help manage that transition to a new portfolio while minimizing tax consequences.

In tax overlay management, wealth managers need to implement tax-efficient strategies across one or multiple model portfolios. The new Axioma models can help the manager reduce the likelihood of incurring unnecessary capital gains taxes or other tax-related inefficiencies.

A third application is for UMAs. UMAs allow clients to combine multiple customized investment strategies – regardless of if they are active or passive – within a single account. The Axioma fund allocation models provide alignment of strategic objectives across all of the accounts.

The rise of quant tools

It’s hard to ignore the significant shift that is happening in the wealth management industry. Integrating sophisticated quantitative tools like risk models and portfolio optimizers into wealth management workflows represents an opportunity for scale and to drive better outcomes for their clients.

What sets the Axioma fund allocation models apart from traditional alternatives is their ease of use and transparency. Unlike dense models that might use thousands of factors — making it difficult to explain the sources of risk — these new models are designed with simplicity and clarity in mind.

Learn more about the Axioma fund allocation models here.