One Size Fits All.
The notion of putting retail investors in cookie-cutter mass-produced standardized buckets of allocations, referred to as model portfolios, is a remnant of antiquated ‘90s thinking. In 2022, it is akin to paying a doctor expensive fees for an over-the-counter pain medicine prescription.
Wealth management firms, RIAs and broker dealer systems that ignore the disruptive ability of new asset allocation frameworks and technologies do so at their own peril. No longer can financial advisors take clients for granted and present them with undifferentiated canned finished asset allocation models. That business is up for grabs by robo advisors and is the road to commoditization. Self-directed retail investors increasingly know more, and often wish to participate in the asset allocation process if they could. However, they are often quite unable to do so, for their advisors often tell them that creating customized asset allocation models is complex, sophisticated and time consuming. These allocation plans, once developed, can remain frozen in time – other than perfunctory ritualistic periodic rebalancing to a historical set of circ*mstances and assumptions.
Good for the Goose but Not for the Gander.
Advisors favor model portfolios for it makes life easier for them. It reduces the investible universe to the most popular sub-types of equity and bond classes, makes for automated implementation, permits scalability in managing assets, eases trading and periodic rebalancing, expresses house views, as well as reduces portfolio construction and diligence complexity to a few products. Despite oft cited claims to the contrary, model portfolios are remarkably like each other and often near identical - much like a rack suit that comes in different sizes. Expressed as varying amounts of asset class allocations across a spectrum of "conservative, moderate and aggressive" client risk tolerance levels, such models can be constructed in minutes. They are then often back tested to provide false comfort with a disclaimer "past performance is not an indication of future results."
Drawbacks.
Amongst the many drawbacks of the model portfolio approach is that they do not constitute a personal recommendation or carefully consider the investment objectives, investment strategies, financial situation and changing needs of a specific client. Moreover, many are based on numerous flawed assumptions that can often result in materially different results than envisaged. For strategic asset allocation, back testing model portfolios is a meaningless exercise. After all model allocations are based on estimated forward-looking return, risk, correlation, inflation, and risk premium assumptions that, when realized, one knows full well in hindsight. Model portfolios work in an ideal world where there are no particular investment time horizon constraints, where the supply and demand for investments is always in balance, where client preferences are static and largely similar. They oversimplify and do not consider changing risk-exposure. Model portfolios almost always shirk active management and recommend broad passive asset class exposure. After all active management requires resources to implement, is intensive and costly - for instance it implies better look through by aggregating granular position level fund data and classifying such data properly to arrive at true underlying sub-asset exposure. Model portfolios are quite unable to bring active and passive, liquid & illiquid, traditional, and alternative investments analysis together in a seamlessly integrated framework. For instance, they do not account for, amongst other things, real world issues such as stale pricing in investment returns measurement, unique risks that differ from those in traditional investing, isolating the effects of illiquidity on portfolios and the special issues that come with investing in inefficient markets. Rather than resolve, they ignore heterogeneity in complex sub-strategies as well as dismiss soft qualitative special manager evaluation considerations.
Technology Driven Change.
There is a dramatic fintech industry transformation happening. In the digital age individual personalization is going to be a key weapon in the battle against irrelevance and disintermediation for financial advisors. Going beyond model portfolios the future is about utilizing disruptive technology driven personalization for purposes of creating directional, semi-directional and non-directional market exposures in client portfolios; exposures that augment traditional, easy to create and implement, stock and bond portfolio holdings typically found in model portfolios. Technology empowers advisors and does not disintermediate them. It can augment advisor-client interactions by bringing in analytics driven, fact based, collaborative, product agnostic thinking to investment decision making. Many software firms as they have morphed and grown have become all things to all people. Their offerings are most often geared to providing the applications and services advisors use to manage their practices. Within this category there are traditional asset allocation software firms that provide packages for creating simple passive model portfolios with stocks and bonds using dated mean variance optimization approaches. In general, most of them compete closely without a clear source of differentiation in content. In doing all the above, many firms have lost sight of the single most reason that a client reaches out to a financial advisor in the first place – which is to create better investment outcomes.
The industry is fast changing and brings new opportunities - with self-directed investors struggling to effectively manage their portfolios now using free model portfolios and cheap implementation using robo advisors. RIAs who rely on just customer intimacy and ignore their investing ability as a source of vital competitive advantage will experience challenges to engage and retain their customers. Emerging internet-based financial services companies are paving the path of innovation as cloud-based firms are simplifying services and product delivery. There is a fast-forming large addressable market that can be fulfilled through technology driven mass-customization.
Bespoke Allocation.
Rather than persist with the historical notion of putting clients in coarse model portfolios, a far better approach is to begin by analyzing the forward-looking statistical properties and expected behavior of a client’s existing portfolio. This helps an advisor recommend the best combination of asset sub-types that improve existing allocations on a variety of chosen metrics. Bespoke allocation goes far beyond conventional passive risk and return tradeoff found in model portfolios. It personalizes for unique investor preferences including accommodating a desire or aversion to alternative investments, expressing preferences for desired levels of illiquidity, considering different investing horizons, incorporating time varying risk preferences as well imposing constraints on specific asset classes to reflect unique investor circ*mstances.
Financial advisors who offer various types of advisory services and programs, including wrap programs, mutual fund asset allocation programs, financial planning services, retirement plan consulting services, investment research, and other customized advisory services need to bring added value. Such value can be created by:
Expanding choice of asset sub-classes; canned portfolios for instance do not intelligently search for asset classes that one may not already own or may benefit from allocating to. Rather, their aim is to strait-jacket investors. With new product expressions, constantly being formed the world is no longer confined to 10-15 asset sub-classes. Expanding investment choices however bring new challenges. These include: (i) choosing from over 50 investments subtypes to be added to an existing portfolio; (ii) deciding the proportion of the portfolio to be allocated to these investments; and (iii) demonstrating the impact these investments may have on the portfolio.
Improving inputs; precise asset allocation requires accurate estimations of several key inputs including expected risk, return and correlations. Financial advisors benefit when they closely monitor market commentary, published research, Wall Street consensus estimates, and other data from wire houses, investment banks and wealth management firms. With such data as starting inputs, they can arrive at their own independent views and incorporate their own estimates allowing their clients to benefit from their specific views. In other cases, they can also work collaboratively with their clients who may want to express their own investment views within their portfolios.
Accounting for client-specific considerations; Every investor has preferences such as tolerance for risk, investment horizon, liquidity requirements, aspirations for a targeted return, choice of alternative assets as well as levels of annual portfolio turnover for tax planning purposes amongst others. A bespoke asset allocation approach takes these into account.
o Selecting portfolio risk target range - whether one has a low, medium, or high-risk tolerance or one can specify a clear number.
o Specifying maximum levels of illiquidity - the level of illiquidity a client is comfortable with.
o Setting limits on alternative investments and types - some investors believe in passive management while many others see value in actively managed investments. Alternative Investments as well as actively managed funds are valuable complements to traditional stock and bond holdings for affluent investors. However, including them in portfolios is extremely difficult. One reason is their heterogeneity and complexity. Another is that Modern Portfolio Theory or MPT, does not work in holistic asset allocation. But simply excluding them in model portfolios curtails investor choice and their potential benefits.
o Providing for maximum turnover - some prefer to own allocations that remain largely static while others are comfortable with higher levels of turnover.
o Targeting returns - clients often have a general expectation of expected target return. Most clients see historical realized returns but wish they had a way to automatically calculate the target return of their existing holdings as a useful starting point.
o Setting an investment horizon - clients have different investing horizons which can heavily influence choices in both asset allocation and portfolio construction.
o Asset class exposure constraints - the maximum or a minimum amount of exposure to any asset class.
It is only after taking these and other preferences into account can one arrive at a personalized, optimized recommended portfolio. Departing from model portfolios has potential to foster greater transparency. It raises the quality of discourse an advisor has with her client. It encourages a conversation not on historical, but around forward-looking risk- return expectations, probabilities of loss or those of exceeding a target. It brings nuance in investment decision making, conspicuous by absence in model portfolios.
Author
Sameer Jain is founder of ActiveAllocator. ActiveAllocator.com is a digital asset allocation platform with technology-enabled customized advice capabilities. It is the world’s first portal that seamlessly integrates traditional, illiquid, and alternative investments within portfolios. It helps investors analyze existing allocations, discover inefficiencies, and create bespoke portfolios in minutes.
Contact: sameer.jain@activeallocator.com
