In little more than two decades model portfolios have come to dominate the way investment decision-making is packaged and sold to clients. Their success has enabled investment groups to deploy their best ideas, from asset allocation to portfolio construction whilst at the same time delivering greater efficiencies and cost-effectiveness to investment firms.

However, with all of that success, investment firms now face a proliferation challenge which impacts some of the core efficiency benefits once gained. With some investment firms now managing hundreds and hundreds of models they now face significant challenges to the scalability and control that they once generated. The good news is that with technology, firms can both customize model portfolios and manage them efficiently enabling a higher volume of output.

Something for everyone
All clients are unique. Each has their own specific requirements. These may be risk tolerances, liquidity preferences, volatility limits, tax sensitivities and so on.

Add in preferences around active and passive approaches, fee levels, and in-house or open architecture – as well as investment groups targeting the outsourced CIO market with bespoke products – and you end up with many potential product permutations.

No wonder investment groups and wealth managers have expanded the number of model portfolios they offer to clients. Having 30, 40 or 50 in a single organisation is no longer unusual, and some have as many as several hundred.

This growth in model portfolio customisation comes at a price. It can mean investment groups lose control of their own products. In such a world, how do portfolio managers ensure consistency across the daily decisions necessary for asset allocation, portfolio construction and security selection? If, say, a model portfolio drifts outside of its range of risk tolerances, to what extent is that portfolio, and the clients it serves, diverging from agreed targets?

And if consistency ebbs away, how can an organization hope to report to clients with the granularity and accuracy now seen as the norm? What level of confidence can a leadership team have that they are adhering to increasingly onerous regulatory and governance requirements?

Whilst customisation brings many benefits to the client – it poses challenges to providers.

Proliferation of model portfolios … and systems
It is not just model portfolios that have mushroomed. Systems have too.

An investment group might not have as many as 30, 40 or 50 model portfolio systems. But the chances are they will have too many for the task at hand. They will have varying degrees of compatibility. Data will be common to some, but not to others.

Parts of their data will almost always live in spreadsheets – often disconnected from other systems. If we think about why model portfolios exist, this is a profoundly sub-optimal way to operate them.

The value in managing model portfolios, and communicating that management to clients, surely lies in making it as easy as possible for those clients to see why their assets are positioned as they are. This direct throughput from the investment team to the client is essential to the quality and duration of the relationship between them. But increased customisation has bred complexity.

There is a second aspect to multiple and inflexible systems.

When investment groups rebalance model portfolios, they require their portfolio management and trading systems to engage seamlessly and efficiently. Data needs to move whole and unimpeded between the two.

It is possible to do this through spreadsheets and ad hoc data input. But, in an era of increased customisation, this is inefficient and risky. Portfolio managers increasingly need to set spreadsheets aside and embrace a new way of working, where aggregated data can be stored, managed and assessed in one place – and shared with internal and external stakeholders in a controlled manner.

Command and control
Imagine a world where model portfolio data – across 500 or even more products – all sit in one place. Such a singularity of perspective enables anyone to track every aspect of a set of model portfolios and to make accurate assessments and decisions with the utmost confidence.

Customisation and consistency need not be a binary choice.

For example, the strategic asset allocation (SAA) used to design a model portfolio uses a consistent asset schema and set of capital market assumptions to validate and analyse existing portfolios. Firms can use them to set the SAA with an optimisation framework that reflects the unique process of the investment firm. This can then be customised to different client preferences and objectives.

For both customisation and consistency to flourish, investment teams require appropriate analytical tools to design and manage model portfolios with scalability.

Take the task of making tactical shifts to existing models. Portfolio management teams must make proposed changes rapidly and then analyse how these changes affect their many portfolios. Both the analytics and metrics used to assess the impact on expected risk and return should be consistent with those used in the initial portfolio design process.

Clear optics – not spreadsheet spaghetti
One of the advantages of a system that not only houses all model portfolio data but allows for the management of those models is significantly improved client engagement.

Let’s say a portfolio manager models a series of forward-looking risk and return scenarios across their whole range of customised portfolios. The data that feeds the modelling all come from one system; this same system facilitates the modelling; and it also generates live, real-time charts that demonstrate to clients or other key stakeholders the full impact of those potential future scenarios.

This is the strongest possible link between the intellectual property of investment models and client objectives.

And, compared to the spaghetti-like approach of spreadsheets – with all the work they require to compile, maintain and interpret – having just one system offers clients optical clarity over their investments and the processes around them.

This optical clarity works not just from portfolio manager to client – it also flows inwards, within an organisation.

Say a portfolio management team needs internal approvals for certain asset allocation calls. Or perhaps they are nudging against the limits of designated liquidity ranges, given security selection decisions, and need sign-off from compliance. Perhaps there is a need to de-risk a series of portfolios. Whatever the scenario, the workflow associated with such governance can be complex and time-consuming.

But a multi-model portfolio management system that enables scalability and control can also offer a much smoother workflow – again because all relevant data is held within the same system, making interrogation and decision-making by internal colleagues much more efficient and effective.

And this is across an organisation’s 30, 40, 50 – or 500–model portfolios. Not just one.

Model portfolios – remembering tomorrow
If we accept that model portfolios are here to stay and that they will continue to grow in number, in terms of the assets they manage, and in customisation, then every single provider must be prepared for what is to come.

Current best practice sees investment groups centralising their data and deciding which components of model portfolios must remain consistent and which must be customised. Done well, this enables providers to operate as many model portfolios as they wish with the utmost levels of control.

Future best practice sees more of this – as smarter technology becomes increasingly the norm over the next decade – offering greater transparency around objectives, product governance and portfolio management.

This means that any group operating model portfolios can embrace hyper customisation wholeheartedly – rather than fear it and its consequences – as they serve clients more effectively, and help them reach their objectives.