Reimagining and reengineering how portfolios are rebalanced

Technology is changing portfolio rebalancing by automating formerly manual processes. The payoff is enormous: rebalancing automation enables wealth managers to do more for less — to simultaneously save clients more in taxes than most advisors charge in fees, increase customization such as adding ESG, reduce dispersion by over 50% and improve compliance — all at a lower cost. 

That’s all good but there’s a catch. You don’t achieve scale by just automating each step of your existing manual workflow. You need to reimagine and rebuild your portfolio management processes from the ground up.

By way of analogy, when manufacturing robots first appeared, some firms tried to create efficiency by the simple step of bolting robots to the factory floor where workers previously stood. It didn’t work. To take advantage of manufacturing robots, you need to redesign the assembly line — and possibly the product — from scratch.

Something similar applies to portfolio management. What does it look like to redesign the portfolio management process? There are four key elements:

1. Specialization
2. Parameterized customization
3. Models-based intellectual capital
4. Holistic cost-benefit analysis 

Let’s walk through these one at a time.

1. Specialization

Portfolio management has traditionally been something of a lone-wolf activity, with each advisor taking sole responsibility for all aspects of managing their accounts. 

With automated workflows, portfolio management becomes collaborative — the joint effort of three specialist groups:

• The investment policy committee (IPC) is responsible for research and due diligence — the intellectual capital of the firm. This includes tactical and strategic asset allocation recommendations, maintaining in-house equity models and enforcing compliance rules such as approved products, limits on asset allocation modifications and asset-class drift constraints.
• The advisor is responsible for account setup — designing a customized solution (as expressed by a set of parameters) for each client. Customization includes selecting the appropriate risk category, modifying asset allocations and product selection for each asset class, setting tax and cash management preferences, entering constraints, etc.
• The overlay manager's role is to manage the daily rebalancing workflow, approving trade recommendations to be sent for execution and solving bad data issues such as stale data or an unknown CUSIP.

Specialization enables each person or group to focus on what they do best. This results in simultaneously greater efficiency managing portfolios and the opportunity to provide clients with higher levels of customization and tax management. And, critically for the firms we work with, it allows advisors to spend more time with clients and prospects.

2. Parameterized customization

For many advisors, “customization” means one-off, trade-by-trade decisions tailored to the needs of each client. 

In contrast, an automated system stores customization requirements and preferences as parameters that describe how each portfolio should be managed. This includes tax management preferences, asset allocation modifications, product substitutions, SRI screens, etc.

Automated rebalancing systems then incorporate these preferences and constraints into every analysis.

With this approach, there are no extra steps required to implement tax management or customization. The incremental cost to the firm of providing tax management and customization is effectively reduced to zero. The only cost to the advisor is the time involved in talking to the client about their preferences and entering this information as a parameter in the rebalancing system. This leads firms and advisors to offer their clients more tax management and customization options.

This leads to an important and somewhat counterintuitive effect: rebalancing automation leads to increased customization and tax management. When you make something free, people consume more of it. Customization and tax management are no different.

Parameterizing customization and folding these parameters into every analysis also aids with compliance. Client requests are no longer lost in a sea of Post-it notes. Once a request is in the system, it will be included in every analysis.

3. Models-based intellectual capital

“Models-based intellectual capital” is another way of saying “parameterized inputs for holdings, asset allocation and trade information.” It’s a cousin of parameterized customization, and it’s necessary to support automation. The alternative is to have buy lists and asset-class allocation ranges — and manual stock selection and trading.

A models-based approach does not mean “cookie cutter.” On the contrary, a models-based approach enables automation, including the automation of customization. 

4. Holistic cost-benefit analysis

Traditional portfolio rebalancing and trading workflows are “action forward.” This means that firms implement a series of discrete actions, such as tax-loss harvesting, or a cash withdrawal.¹ With each rebalance, the basic idea in a traditional approach is to implement these actions and do as little else as possible. Firms may occasionally implement a “general rebalance,” but usually not in a particularly tax- or expense-efficient way unless there is manual intervention.

While the concept of an “action forward” workflow sounds sensible, even exciting, it leads to undesirable outcomes for both advisors and their clients. Problems start as soon as different goals start conflicting. What, for example, do you do if a stock swap would result in short-term gains? Sell anyway? Wait until the position is long-term? What if the client also has a cash-out request? Then what? The advisor ends up reactively addressing a single aspect of a particular policy for investment and trading — while trying to make sure their actions are not in conflict with other aspects of the same policy. There are not enough hours in the day to make this process work for every — or even most — investor accounts.

In an automated approach, all of these discrete rebalancing “triggers” (cash-out request, drift, loss harvesting, etc.) are replaced with a holistic analysis of ways to make the portfolio better, where “better” includes:

• Greater fidelity to the firm’s recommended asset allocations and security weights
• Lower taxes and lower transaction costs 
• Adherence to investor customization requests
• Adherence to the firm’s compliance limits

There’s no “model change day,” “loss harvesting day” or “security swap rebalancing day.” Every day is simply “make-the-portfolio-better day.” 

As we noted at the beginning of this post, rebalancing automation benefits are enormous, both for the wealth advisory firm and their clients: higher after-tax returns, greater customization and improved compliance. And lower costs. However, an automated rebalancing workflow isn’t merely a faster version of the old way of doing things. It involves a reimagining of the entire process.

We get that this can be a little intimidating, but at this point, there really isn’t any alternative. Old ways of doing things are uncompetitive. The good news is that any firm, of any size, can adapt. The key is to decide that you want to.

¹ Other common actions include implementing tactical asset allocations shifts or securities swaps, correcting for drift and incorporating a ticker rating downgrade.