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Automated Rebalancing

What is automated rebalancing?

When we talk about an automated rebalancing solution, we mean a system that, well, automates rebalancing. Not an alerting system. Not a suggestion system. Rather, a system that proactively generates solutions in the form of actionable trades that improve the portfolio and meet requests, such as cash in or cash out. This seems obvious, but it’s worth emphasizing. The word “automation” is commonly used to describe pretty much any efficiency tool. Here, we’re using the term in its more literal sense.

That being said, “automation” does not mean no humans are involved. Stuff happens, such as delayed processing of corporate actions and consequent inaccurate holding records, that will require human intervention. And firms may want to take a second look at accounts where the system’s proposed trades are unusual—for example, a $10 million account with proposed turnover that exceeds 30%. These accounts are “exceptions,” but not in the sense of being accounts that need to be rebalanced; these are simply the accounts with trades that firms decide merit a review.

At a basic level, automated rebalancing has been around for decades. Think of a strategy made up of five mutual funds rebalanced back to target weight on the first of every quarter. 

What’s new is the ability to automate highly customized, tax-managed accounts, including those with individual equities and legacy holdings. Not only can the rebalancing of these types of accounts be automated, we have reached the stage where machines can do it better than portfolio managers. This isn’t because portfolio managers don’t know how to tax manage; it’s because portfolio managers simply don’t have the time. Expert tax and risk management requires daily attention. That’s impractical for portfolio managers, but not for computers. For this reason, computers are better at tax and drift management, even for ultra high net worth (UHNW) accounts. Automated rebalancing is not just a mechanism to do things more efficiently. It is a mechanism for doing things better.

What does "rebalancing" really mean?

When most people think of a rebalancing system, they think of a system that “snaps to center”. That is, it simply suggests trades that get the holdings in a portfolio exactly back to their target weights – if the target weight of IBM is 3%, then the system would generate trades that get you there. 

That is not what we mean here. A better phrase might be a “balancing” system – a system that continually balances the competing considerations of low tracking error, low taxes, low transaction costs and low churn. An automated rebalancing system is a smart decisioning system that takes multiple inputs and produces trades that balance multiple competing goals. They control risk, ensure compliance, generate tax alpha, manage transitions and implement customization.

Their purpose is to actively and quickly convert the intellectual capital of the firm into specific trades for each account in a way that adheres, every day, to each account’s customization and tax management preferences. Or, viewed the other way around, the aim is to convert each account’s customization and tax management preferences into specific trades that reflect the firm’s investment guidance.

How automated is "automated" rebalancing?

The term “automated” is used pretty loosely in the industry. Sometimes it means nothing at all — the rough equivalent of when a local pizza shop adds “World Famous” to their name. As a result of this confusion, we tend to have conversations like the following:

Q: How many staff would it take to rebalance a book of 100 accounts?

A: Two people (really one, but you need a backup).

Q: What about 1,000 accounts?

A: Two people.

Q: 10,000 accounts?

A: Two people.

Q: 100,000?

A: Two.

This gets people’s attention, but then, understandably, this leads to a bit of poking:

Q: Wait, what if the accounts are tax managed?

A: No difference. Tax management, including tax-sensitive transition, is automated.

Q: What if clients have custom asset allocations? Or custom product mixes?

A: No difference

Q: What if there are social or religious screens? Lots of cash-out requests? Tactical asset allocation change? Model swaps?

A: No difference.

At this point, the idea becomes clear that “automated” means, well, automated.

How does automated rebalancing differ from traditional rebalancing?

The traditional approach to rebalancing is to trade portfolios in response to specific triggering events, for example:

  • Cash in; cash out
  • A security swap in a model
  • A swap of one model for another
  • A tactical asset allocation change 
  • Drift triggers
  • A tax loss harvesting opportunity
  • An updated client profile
  • A security transfers in; security transfers out
  • Time since the last rebalance

Some of these events trigger a “full rebalance”, meaning you reset the entire portfolio — asset allocation, security weights, the works. But most events trigger some form of partial rebalance, e.g. you trade just the securities in a model swap and nothing more. Things get complicated when there are two or more trigger events at one time. They get even more complicated when you start having to also worry about taxes and constraints. Problems really 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.

This whole “trigger events + rebalancing + exceptions for tax & constraints” approach is pretty typical. And it’s a terrible way to manage a portfolio — time consuming and expensive. Worse, it results in high drift, poor tax management and low levels of customization. This traditional approach to rebalancing was never intrinsically desirable, it is just what was practical given the limitations of manual rebalancing.

Automated rebalancing systems don’t work by simply mechanically implementing event-driven workflows — no more than airplanes work by mechanizing the flapping of wings. 

With an automated rebalancing system, you set high-level goals and delegate the details. You tell the system how you want the account managed, and, like a good administrative assistant, the system does it. Examples include instructions like:

  • Implement tax sensitive transitioning
  • Apply an ESG screen
  • Practice year-round loss harvesting
  • Remove real estate as an asset class and rescale
  • Use a direct index for US large cap, etc.

In this way, tax management and customization are no longer on-the-fly adjustments. They’re built right in. 

Automated rebalancing systems replace the event-driven approach associated with traditional rebalancing with a simple, uniform (and automated) daily rebalancing workflow. The discrete rebalancing “triggers” (cash-out request, drift, loss harvesting, etc.) associated with traditional rebalancing 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.” 

Conflicting goals (e.g. low drift vs low taxes) are handled through an optimization analysis. Desirable outcomes, like lower drift (lower tracking error), higher-quality securities (as determined by your firm) and tax loss harvesting, get a positive score. Undesirable side-effects, like taxes, higher drift, lower-securities and trading costs get a negative score. Trading happens when the net benefit of trading exceeds a predetermined threshold, or when trading is required to satisfy a mandate (such as cash out, “never own” constraint, etc.).

Compared to traditional rebalancing, automated rebalancing is less expensive and more consistent. More importantly, it enables wealth managers to simultaneously lower return dispersion AND provide investors with higher levels of customization and tax management. We can quantify this. Return dispersion can’t be avoided completely if you want to provide clients with customized solutions and manage taxes and trading costs effectively. So you’d expect that there would be a trade-off between tax efficiency and return dispersion. But firms that adopt automated rebalancing see both return dispersion and taxes reduced by more than 60%. This is a strong measure of, well, how bad traditional rebalancing is. It means that most of the return dispersion in accounts managed with traditional rebalancing is simply unnecessary. It serves no useful purpose; it’s not the result of customization nor is it evidence of good tax management; it’s just “noise.”

Automated rebalancing does better.

What are some examples of the difference between traditional and automated rebalancing workflows?

Scenario 1: Ford is replaced with Walmart in a model.

Traditional event-driven rebalancing

Automated rebalancing using holistic cost-benefit analytics

Sell Ford and Buy Walmart across all accounts. There is a largely manual review/processing of exceptions for constraint and tax management. 

Sell Ford and Buy Walmart in accounts where doing so creates sufficient benefit in terms of increased return expectations (as determined by each firm’s security rankings) and reduced tracking error, net of transaction costs and taxes.


Scenario 2: IBM is a little over-weighted in a portfolio and has a small unrealized loss.

Traditional event-driven rebalancing

Automated rebalancing using holistic cost-benefit analytics

No trades. Neither a small overweight nor a small tax loss is considered a trigger event. 

Sell IBM if doing so creates sufficient benefit in reduced tracking error (and increased return expectations if the firm ranks IBM lower than other securities in the portfolio), net of transaction costs and taxes (which in this case are negative). This level of benefit is possible even though neither the reduction in overweight or the realized losses are individually large enough to warrant trading.

Scenario 3: The client has newly imposed a “never own tobacco stock” mandate.

Traditional event-driven rebalancing

Automated rebalancing using holistic cost-benefit analytics 

Sell tobacco stock. 

Sell tobacco stock.

What is the value of automated rebalancing compared to traditional rebalancing?

First, and not surprisingly, it’s more efficient and compliant. Your costs are lowered, and you have no capacity constraints. Even when markets become really volatile, like in the first half of 2022, you can stay on top of rebalancing.

Second, by making customization and tax management incrementally costless, it makes it possible to customize and tax optimize every account, no matter how small. 

Third, you have more control over your portfolios. You are able to implement decisions of your investment policy committee quickly across all portfolios, no matter how personalized. 

Finally, and perhaps most importantly, it lets your client-facing advisors spend more time with clients and prospects.

What types of firms adopt automated rebalancing?

The firms we deal with typically have concluded that performance-, trade- and product-oriented value propositions are in decline. They wish, instead, to lead with a value proposition founded on financial planning. Tax and customization are also important. Their aim is to be their clients’ “lifetime financial coach”. And to help achieve this goal, they wish to free the client-facing advisor from having to spend time rebalancing portfolios. To this end, they’ll centralize rebalancing, while bringing the customization and tax management they offer to a higher level. Some firms are going beyond centralization towards outsourcing. They do it to focus on their core competency, and as with centralization, to increase the levels of customization and tax they can offer their clients. 

What are the inputs and outputs of automated rebalancing?

The main inputs are, for each account, 1) holdings, with tax basis at tax lot level, 2) customization and tax management preferences and 3) a target portfolio. The main outputs are 1) tax-lot level trades, with 2) explanations of trades, and 3) documentation of value delivered.

Let's take a closer look. 


The basics:

  • The holdings for each account, with tax basis, uploaded from your custodian or system of record
  • Restricted securities, e.g. pledged securities, also from your custodian or system of record
  • Data, from data feeds sourced by the rebalancing system vendor:
    • Price data
    • Corporate action data to ensure that corporate actions are synchronized across all data sources 
    • Risk model or other security substitution data to appropriately handle required substitutions in the case of never buys and counterbalancing underweights in the case of never sells

The investment strategies of your firm, as determined by your investment policy committee:

  • Asset allocations and compliant limits on asset allocation customization for different risk profiles
  • Recommended product and approved substitute products for each asset class
  • Security models, e.g. a “our firm’s proprietary 50 stock large cap core model” (optional)

Account-level client personalization parameters, as determined by your client-facing advisors, in conversation with your clients:

  • Choice of asset allocation, as well as asset allocation and product selection customizations 
  • Social screen, security and sector restrictions
  • Cash management parameters:
    • Min cash
    • Max cash
    • Creation of reserve cash from income to support recurring periodic withdrawals
    • Cash-out requests
  • Tax management information and preferences:
    • Tax budgets
    • The client’s marginal tax rates
    • Loss carryforwards and YTD realized gains and losses
    • The account tax status (e.g. “401K”, “taxable”) {this information may also come from your custodian or system of record}


Trades. The first and obvious output of an automated rebalancing system is trades. Not just trades, but actionable and compliant tax-lot level trades that ensure each account is faithful both to the intellectual capital of the firm and the customization preferences of each account. All rebalancing systems produce trades of one sort or another. Traditional systems may do as little as calculate how to implement model weights in each account, e.g. “to get the Smith account to 2% IBM, buy 520 shares”. For almost all forms of customization or tax management, these suggestions are just the starting point of generating actual trades. Automated rebalancing systems do much more. Their trades are not a first pass at an answer; they are the complete answer. The secondary output of an automated rebalancing system are reports. These include:

  • Who/what/when: what trades were made, released by whom, when and why? And were portfolios that needed to be traded in fact traded in a timely manner?
  • Proof of value added: reports that let advisors and firms document the value that they’ve added. How much did they save their clients through active tax management? Were all client requests and customization parameters followed?

When you look at the above lists of inputs and outputs, it’s pretty clear that “rebalancing system” isn’t a great name for what automated rebalancers do. They don’t “rebalance”. They customize and make portfolios tax-efficient; they make sure every account gets the benefit of the firm’s best thinking. They make it possible to automate that which was once manual, and through this automation make it possible to manage portfolios without compromise.

What does it take to leverage an automated rebalancing system?

You need to embed it in an efficient rebalancing workflow, with responsibility for portfolio management spread across three groups, each specializing in what they do best.

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 central rebalancing group'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.

Can compliance be automated?

Yes, or at least major components of compliance. Automated rebalancing doesn’t help with suitability, disclosure, consent, etc. But it does ensure fidelity of an account to its investment strategy and its customization parameters is “built in”. 

Let’s start from the beginning. Wealth management compliance today is like auto manufacturing 30+ years ago. A 1980’s ad for a European luxury car company featured dozens of quality control inspectors in white lab coats surrounding a car fresh off the assembly line — the company was touting the time and effort they put into catching defects. At the same time this ad came out, Toyota, which pioneered lean production, was manufacturing higher quality cars in less time than the competitor spent just correcting defects. Toyota’s secret (which was subsequently copied by all car manufacturers) was to “build in” quality. They reorganized their assembly lines and their processes to enable them to avoid defects in the first place. The result was both higher quality and lower costs.

Similarly, automated rebalancing enables wealth management firms to “build in” compliance, with a similar double win of both lower costs and superior compliance. When rebalancing is automated, compliance is fundamentally transformed. This comes from two fundamental characteristics of automated rebalancing: “structured” data and support for efficient daily review and rebalancing processes:

Structured data: Traditionally, an account’s customization parameters have been recorded — if they were recorded at all — as ordinary text, e.g. “the client requested no tobacco stocks.” With automated rebalancing, these parameters are stored in machine readable form (so called, “structured data”). Structured data can be passed from system to system and can be “validated”, meaning that users can be stopped from entering invalid or undesired information, like websites that won’t let you enter “March 34th” as your birthday. This has two implications for compliance:

  • Risk-suitable targets: Rebalancing systems don’t determine suitability, but they can be linked to profiling systems that have APIs, ensuring that every portfolio is rebalanced to a risk-suitable target. For example, you can make sure that an “income” client (as determined by the profiling system) will never have an “aggressive growth” target.
  • Bounded, validated customization: Customization is critical to providing investors with an investment solution that meets their needs, but customization can also be a backdoor path to non-compliance. For example, it would be problematic to “customize” a strategy by changing the recommended real estate allocation from 5% to 95%, or by designating a small cap ETF as an acceptable alternative to a firm’s recommended large cap holdings. On the other hand, you do want to permit reasonable customizations. For example, it may be Okay to increase a 5% allocation to real estate to 10% and it may be Okay to replace a large cap mutual fund with a large cap ETF. These sorts of bounds on permitted customizations can easily be built into the rebalancing system.

Daily review and actionable response: The need for rebalancing can come from many sources — market drift, a change in asset allocation, model changes, a change in client customization parameters, security transfers in or out of an account, cash withdrawals or deposits, etc. An automated rebalancing system needs to be able to handle all of these whenever they occur. This has two implications for compliance:

  • Daily compliance review and response: With automated rebalancing, every portfolio is reviewed daily for violation of any type of mandate — drift, ESG constraint, etc. More importantly, the system will automatically propose trades that fix any problem it finds. This means that no portfolio will ever be out of compliance with its parameters for more than one business day.
  • Consistent rebalancing processes: Consistent processes are at the heart of compliance. With an automated rebalancing system, it becomes possible to design and implement a consistent process for rebalancing. Not just a consistent process for when to rebalance (e.g. “every quarter” or “when asset class ranges drift more than 20%”) but a consistent process for how portfolios — no matter how customized — are rebalanced.
How do automated rebalancing systems handle bad data?

Automated rebalancing only works if the data it relies on is accurate. There’s an old computer science saying: garbage in, garbage out. If you feed an automated rebalancing system incorrect holdings or model data, it’s not likely to generate useful trades. The solution? It comes in two steps:

  1. Perform integrity checks to catch bad data before you trade.
  2. Implement a rebalancing workflow that auto-corrects any data-related bad trades as soon as the data errors are fixed.

Let’s walk through these one at a time.

Data integrity checks:

The first line of defense in protecting against bad data is to suspend trading of accounts that may be affected by bad data. There's lots of possible sources of bad data. In practice, we see three:

  1. Stale Uploads: Rebalancing systems upload holdings information from custodians or intermediate accounting systems. If, for whatever reason, an account is missing from an upload, the data for those accounts will be stale.
  2. Corporate Actions: In theory, custodians and intermediate accounting systems all apply corporate actions on the same day. But in practice, corporate action processing is often delayed. This can cause problems. If, for example, your holdings don’t reflect a 2 for 1 split, you’re going to think you have ½ as much of the security as you really do.
  3. Unknown Securities: Rebalancing systems like Smartleaf have their own security master for prices and risk signature. Occasionally, we won’t recognize a CUSIP or ticker included in a model or a holdings upload. Most of the time, it’s because of delayed corporate action processing by the custodian (see above), but sometimes it’s because the security is obscure and thinly traded.

The solution is the same in each case: suspend trading for accounts affected by the bad data. However, the details of how this is done will vary from system to system. Here’s how it works in ours:

  1. Stale Uploads: This one’s easy. Our system automatically suspends trading for accounts with holdings information that has not been updated since the last market close.
  2. Corporate Actions: We show our clients all the corporate actions we know about from our own corporate action feed. Our clients can compare our list of corporate actions with their own. If there’s a mismatch — for example, if their custodian is a day late in recording a split — our clients can apply a filter to suspend trading of any affected account.
  3. Unknown Securities: We show our clients all the securities we don’t recognize. Usually, they’ll apply a filter to suspend trading of any affected account. If the client knows about a security and feels it can be safely ignored, they can choose to proceed with trading.


Self-Correcting Daily-Rebalancing Workflow:

With these data integrity checks in place, it is exceedingly rare for any account to be traded on bad data. For most of our clients, there have never been any bad-data trades. For others, it’s a once-every-few-years event.

Fortunately, when it does happen, the bad trades are auto-corrected as soon as the data is fixed. This auto self-correction only works because automated rebalancing systems support a uniform daily rebalancing workflow. If, instead, you had a calendar-based rebalancing workflow — say, every quarter — you’d need a special process to make sure you go back and fix errors caused by bad data. But if you have a daily rebalancing workflow, this isn’t necessary. The problem is just caught and corrected the next day (or whenever the data problems are fixed).

Here’s how a daily rebalancing workflow works: every day, you trade any account that meets one or both of two conditions:

  1. The account is not compliant with one or more important constraints, such as “satisfy a cash out request” or “never own tobacco stocks.”
  2. The net benefit of trading exceeds a preset “cost/benefit threshold.” For each account, the system generates a “cost/benefit score” that measures how much the account can benefit from trading. What constitutes a benefit? It is strictly defined by the client. It includes bringing the account closer to its target asset allocation and recommended security weights; buying securities with higher security return rankings, and tax loss harvesting. What constitutes a cost? Costs include commissions, bid-ask spread and taxes.

Here’s the thing. If one of these triggers causes an account to be traded, the system-generated trades will remove the trigger. Specifically, the trades always fix whatever constraint was being violated, and take advantage of any opportunity to improve the portfolio net of costs. So, after the account trades, there’s nothing left to do until circumstances change — e.g. there’s a tactical asset allocation change, a security swap in a model, a new loss harvesting opportunity, a change in the account’s customization settings, etc. This means that under normal circumstances, the account won’t trade again for a while — it will neither violate a constraint nor have trades that exceed a cost/benefit threshold.

However, this isn’t true if an account is traded on bad data and that data is subsequently fixed. In this case, it is likely to trade the account twice in rapid succession — perhaps two days in a row. The first day it trades based on bad data; the second day, armed with good data, it undoes the previous day’s trades. The key point here is that there was no special process for correcting the error — just the application of the firm’s standard daily rebalancing workflow.

Automated rebalancing is extraordinarily powerful, but it does depend on clean data. The bad news is that there will never be a world with perfect data. The good news is that data integrity checks and a self-correcting rebalancing workflow can reduce the problems caused by bad data to near zero.