The integration of open architecture and advanced technology has become pivotal in delivering customized and scalable model portfolios. This approach helps enhance the personalization of investment strategies and can allow advisors to efficiently manage a growing client base without compromising on service quality.
Leveraging Open Architecture for Enhanced Flexibility
Model portfolios serve as structured investment strategies that advisors implement on behalf of their clients. While they offer a foundation for asset allocation and fund selection, the increasing demand for personalized investment solutions has highlighted the limitations of one-size-fits-all models. Clients today seek portfolios that align with their unique financial goals, risk tolerances, and personal values. This necessitates a shift towards more customizable model portfolios.
In the context of model portfolios, an open architecture framework enables advisors to incorporate a diverse range of investment products and strategies from multiple providers. Different providers may cater to varying client objectives – that is, some model providers offer geographically focused strategies while others specialize in models composed of alternative investments. By drawing on the best-of-breed offerings across a broad universe of model offerings, advisors are able to tailor their recommendations to specific client needs, preferences, and constraints.
“Having an open architecture structure for model portfolios is extremely important as it offers greater objectivity and optionality for the investor. It allows for tailored investment solutions that respect individual goals and constraints,” says Kei Sasaki, chief investment officer at Members Trust Company. While some advisors may view limiting this flexibility as an opportunity to further their own investment strategies, Sasaki warns that “restricting optionality within these model portfolios or embedding proprietary solutions can introduce conflicts of interest and ultimately undermine the outcomes for individual investors.”
Technology for Customization and Scale
Advancements in technology, particularly AI, are transforming various industries, but their integration into financial markets may take longer than expected. While AI tools like ChatGPT and Midjourney are becoming widely accessible, their application in finance faces unique challenges tied largely to the limited data sets available for use in training AI models for investment applications.
The rapid development of hardware to support AI is exciting for quantitative managers, enabling faster computation and improved data processing for model portfolios.
“There’s a lot of hardware that is going to be built to facilitate the anticipated compute load requirements of more intelligent predictive systems. As quantitative managers that have a server closet in our offices and run computers to generate and fit models 24 hours a day, it’s exciting to us that all this new hardware is being produced. We’ll ultimately gain access to better resources, and we’ll be able to develop more extensive and interesting datasets. We’ll be able to compute faster and as a result expect to see the data drive predictive power into the model portfolios more quickly,” says Andrew Rice, partner, COO, and portfolio manager at Algorithmic Investment Models (AIM), a quantitative manager known until recently as Beaumont Capital Management.
However, financial markets present a more complex problem due to the limited historical data available compared to the vast datasets used to train AI models in other domains. Accordingly, Rice says, “people should not expect to see AI make its way effectively into the financial markets perhaps as fast as we’ve seen it show up in other markets. Relatively, there’s not a lot of data compared to the vast amounts of data the leading AI products are using in their training. ChatGPT has used virtually all of the available written English language to train itself – the inputs are fairly vast. Comparatively, there is only one financial market history, so it’s a harder problem to solve.”
Unlike language or image-based AI, which can continuously expand their training sets, financial markets have only one historical record to analyze. As a result, while AI and machine learning hold promise, their meaningful application in finance will likely take time and should be approached with caution despite advances for model portfolios specifically.
Balancing Customization with Scalability
While customization is essential, it must be balanced with the need for scalability. Over-customization can lead to inefficiencies and increased operational burdens. Therefore, it’s crucial to implement customization within a framework that supports scalability, ensuring that personalized services can be delivered efficiently across a broad client base.
Looking to the future, model portfolios may well include more asset classes and investment vehicles. “With open architecture, model providers can differentiate and enhance value by leveraging the full spectrum of investment vehicles and styles,” says Rob Eckrote, VP of institutional consulting at BlackRock. “While index-tracking ETFs will remain essential portfolio building blocks, we anticipate growing adoption of less traditional vehicles and asset classes – such as active ETFs and private markets – as managers seek innovative, efficient ways to customize portfolios and engage high-net-worth clients through models.”
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Carefully consider the investment objectives, risk factors, charges and expenses of funds within the model portfolios before investing. This and other information can be found in the funds’ prospectuses or, if available, the summary prospectuses which may be obtained by visiting each fund company’s website or calling their toll-free number. For BlackRock and iShares Funds, please visit www.BlackRock.com or www.iShares.com. Read the prospectuses carefully before investing.
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International investing involves risks, including risks related to foreign currency, limited liquidity, less government regulation and the possibility of substantial volatility due to adverse political, economic or other developments. These risks often are heightened for investments in emerging/developing markets and in concentrations of single countries.
There can be no assurance that performance will be enhanced or risk will be reduced for funds that seek to provide exposure to certain quantitative investment characteristics (“factors”). Exposure to such investment factors may detract from performance in some market environments, perhaps for extended periods. In such circumstances, a fund may seek to maintain exposure to the targeted investment factors and not adjust to target different factors, which could result in losses.
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Transactions in shares of ETFs may result in brokerage commissions and will generate tax consequences. All regulated investment companies are obliged to distribute portfolio gains to shareholders.
Certain traditional mutual funds can also be tax efficient.
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