This is part 4 of a series on the implications of automated portfolio rebalancing technology for the wealth management industry. You can read previous installments here: Part 1, Part 2, Part 3
.jpeg?width=1139&name=AdobeStock_52170496%20(1).jpeg "AdobeStock_52170496 (1).jpeg")
Forget what you think you know about 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
- 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 worry about taxes and constraints.
Does this whole “trigger events + rebalancing + exceptions for tax & constraints” approach to sound familiar? It’s 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 is not intrinsically desirable, it is just what was practical given the limitations of manual rebalancing.
There’s a better way made possible by automated rebalancing systems. But automated rebalancing systems don’t work by simply mechanically implementing event-driven workflows — no more than airplanes work by mechanizing the flapping of wings.
Automated rebalancing systems replace the traditional approach with a simple, uniform (and automated) daily rebalancing workflow. This workflow is based on a holistic cost-benefit analysis. All events — drift, taxes, security swaps, model swaps, etc., as well as transaction costs and taxes — are combined into a single cost-benefit score. Trading happens when the cost-benefit score exceeds a predetermined threshold, or when trading is required to satisfy a mandate (such as cash out, “never own” constraint, etc.).
But how does an automated rebalancing systems calculate a cost-benefit score? It’s a combination of four factors:
- Rankings — how much does the firm favor or disfavor a security?
- Transaction Costs — the costs of trading, including commissions and estimated bid/ask spread.
- Taxes — the tax impact of trading (which, in the case of tax loss harvesting, can be negative)
- Drift — alignment between a portfolio and its “target” asset allocation and security holdings. This is usually measured by “tracking error.” Tracking error can be caused by changes in the price of securities, market security swaps in a model, a swap of one model for another, etc.
The difference between traditional rebalancing workflows and the cost-benefit-based approach supported by automated rebalancing systems can be illustrated by a few examples:
Scenario 1: Ford is replaced with Walmart in a model
|
Traditional event-driven rebalancing |
Automated rebalancing using 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 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 cost-benefit analytics |
|
Sell tobacco stock. |
Sell tobacco stock. |
Why is the cost-benefit approach better? For one, it can be automated, which 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 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 can and does do better.
Next week: the implications of automated rebalancing for investors
Related: Part 1, Part 2, Part 3, Part 5
For more on this topic, check out What is Rebalancing Automation?
COMMENTS