Customizing models at scale
In our use case, the firm can apply a 60/40 model and augment with target overrides to modify allocations to client-specific needs. This approach offers simplicity and scalability without sacrificing the ability to tailor models to client needs.
With more than 800 active target overrides as part of its rebalancing and trading ecosystem, each is a specialized rule on top of all other equivalents and rules. The firm uses 40 models to implement bespoke client allocations at scale by applying target overrides. This gives them a level of dexterity and specificity, without causing portfolio managers or investment officers to feel as though they are compromising in some way or beholden to the models.
Phase two: cash overrides
Cash overrides are often used with clients who are looking to meet a model cash target (not a reserve) that becomes part of the model percentage. This ensures everything else in the portfolio works appropriately and places a hard stop on that cash. Thirty percent or more of our use-case firm’s clients utilize some kind of cash override that exists outside of the model.
Initially, the key challenge was that cash targets are typically driven by unexpected model features. Therefore, cash had to be handled separately, in a scalable and best-practice manner, while eliminating the need to trim the trade level or perform an exemption to exclude the cash.
This led to a second phase of our use-case firm’s journey to customize at scale – making cash targets different than what’s in the model, without using a cash action to do it. With the cash overrides in place, the firm gained an even greater level of confidence in our intelliflo redblack system to execute rebalancing across a broader book of business.
Institutionalizing proprietary knowledge
The rules-based framework of target and cash overrides is a sound way to institutionalize proprietary knowledge in terms of client needs and investment strategies. This helps increase rebalancing and trading efficiencies while maximizing the amount of time a firm’s high-paid, client-focused talent spends on certain types of work.
Portfolio managers and investment officers are some of the most knowledgeable and highest-paid assets in an organization. They also want to be explicit in how they do their jobs. Yet many spend too much time manually rebalancing in spreadsheets or handling mundane trading tasks, which can add up to hundreds of thousands of dollars in person-hours per year. When a firm is trading hundreds of millions of dollars every day, there is no margin for error or delays.
In the past, when a client wanted a subsector to be X and another Y, such preferences were recorded on paper or in a spreadsheet and saved on a hard drive.
Once the process is standardized and automated to accelerate speed and accuracy, knowledge of clients’ specifications are memorialized forever within the system, rather than getting lost or walking out the door with a portfolio manager or advisor. Trading is streamlined, and middle- and back-office tasks are automated, giving portfolio managers and investment officers more time to add strategic value and respond faster to client needs.