Are We Leaving $800 Billion on the Table? [4 of 3]

• Financial Services firms generally underspend other sectors when it comes to digitization as a proportion of total operating expenses.
• This problem is exacerbated by: the rapidly growing quantity of portfolio data, the obstinacy of hiring talent for tasks which can be automated, an inherent communication disconnect between portfolio managers and capital allocators.
• Investing in portfolio intelligence software to automate tasks could result in $40 billion cost savings.
• More importantly, using portfolio intelligence software to extract and act on investment insights could add as much as $800 billion in annual returns to the industry.
• The single best initial step investors could take is to create a digital investment book of records, making use of the portfolio information available at their fingertips.

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View Part 1 of this series, 3 Reasons Why Your Firm Might Be Making Bad IT Decisions

View Part 2 of this series, Saving the World (While Generating Returns) with ESG Data

View Part 3 of this series, Assembling the "Investment Avengers" Using Skill-Set Analytics

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I’ve been writing on why the financial industry – and institutional investors in particular – need to digitize operations. I had hoped to confine the message to three posts but, looking back, I can see there’s one thing missing: the summation. Hence, here is my encore; part four of three.

We discussed how and why we came to focus on the wrong data sets. Next, we concentrated on the special case of Environmental, Social and Governance, and how ESG data can be used to predict results and mitigate risk. Then, we described how to deploy analytics to optimize your firm’s skill sets.

But we never really made the case in so many words. So now we’ll delve into how much rent we’re paying on our ignorance or avoidance; we hope to call some readers to action. There is one simple yet crucial requirement you should pursue to address all these issues we’ve covered.

Sticking with our superhero theme, I wish I had the power to go back in time and plan this over again as a four-part series. But time travel is beyond virtually any caped hero’s capability. It’s usually a villain move.

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“Brother, can you spare a gigabyte?”

It never ceases to amaze me how some people who style themselves as Masters of the Universe – with millions of dollars in annual income, tens or hundreds of millions in personal wealth and billions in assets under management – cry poverty whenever they must spend on ancillary services, especially enterprise software. It’s as if they value their own skill set as fund managers, and maybe they understand what it costs to hire and retain a good financial analyst, but nobody else’s time or expertise is worth anything to them.

Technology is a prime example. In other industries, tech and R&D spending hovers around 10% of total operating costs. But on Wall Street, the Square Mile, the Central District and all the world’s other financial metonyms, such investments represent a paltry 0.58% of total operating expense.

When you consider how much more we (over-) pay for people and real estate, and that our compliance burden is higher than most sectors’, a gap is to be expected. But two orders of magnitude? Really?

Consider the chart below, which reports in basis-point terms the average management fees spent for outsourced portfolio management (i.e., the amount paid to external managers – comprised of both management and performance fees) as a share of assets under management vs. the yearly spend on technology.

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We’ve heard it all:

“The reality is that we pay way too much for perhaps the returns managers promise,” says the chief investment officer of a U.S. pension fund with $12 billion AUM.

“We have a history of beating custodians on price, and we’re left with poor service, overworked client management teams, and no technology offered,” reports the chief operating officer of a $3 billion U.S. foundation.

“It’s easier to convince the board to pay 2-and-20 than adding a Bloomberg,” recounts the CIO of a European pension fund.

And, for the record, convincing boards to pay 2% of AUM plus 20% of excess performance isn’t easy these days.

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Pennywise, Hundreds of Billions Foolish

I’m not suggesting throwing money at the problem but … Who am I kidding? I’m precisely suggesting throwing money at the problem. But throwing more in the sense of darts than of shovelfuls of dirt. Hitting the target is the goal, not spending money just to stimulate the data analytics industry. Now you may say: “What’s the urgency? After all, I’ve been investing for decades and I did not need any of this.” Well, I’ll introduce in just a bit what I think are the three most compelling reasons to up your technology budget.

Here’s what changed and why you should act:

1. The sheer quantity of portfolio data that investors need to be on top of to perform their duties has grown 100x in the past 10 years.

2. Manual workloads have crowded out 40% of investment teams’ time, which they could otherwise spend more productively.

3. Portfolio managers and data allocators are so disconnected from one another that the friction involved in their everyday communication eats up a cumulative $10 billion annually.

These all have a price tag. Sure, we could say that keeping up with the burgeoning growth of data can be handled by readily available software, but the next step is to actually buy and implement that software. Right now the industry is spending $5 billion per year on portfolio management software, most of which is for risk systems which few can understand and even fewer can operate.

Novus estimates that if we could just automate the workloads associated with the increasing amount of portfolio data, as an industry we would save $40 billion costs.

That’s not exactly chump change – unless you compare it to the sea of missed opportunities that comes from the lack of action on the insights which are embedded in  portfolio intelligence.  Working for more than a decade with institutional investors who took portfolio intelligence to heart and used the insights provided by the Novus platform to make better investment decisions, we conclude that a minimum of 100 bps of incremental annualized alpha could be attributed to acting on such insights.

When you apply 100 bps to the $80 trillion dollar industry that is institutional asset management, you get $800 billion of incremental returns distributed to beneficiaries.

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What Have We Learned?

While the Financial Services sector has reaped rewards for solving signal generation challenges and finding inefficiencies between a security’s price and its intrinsic value, the fact remains it perennially lags overall adoption of machine learning, artificial intelligence and other big data techniques. This is mostly because the underlying portfolio data (onto which to apply said techniques) is not in order.  

Organizationally, that’s largely because fund managers tend to be better at picking technology stocks for their portfolios than technology for their IT portfolios. As a result they look at the wrong data to make meaningful predictions, persist in hiring techies rather than subscribing to solutions and asking these techies either the wrong questions or no questions at all. We learned that in the first article.

If you’re looking at use cases to get started with more impactful IT spending, I recommend ESG data and skill-set analytics. As we learned in the second article, ESG transformation might not impact returns – although that’s a hypothesis worth testing – but it can absolutely attract assets and flows.

Meantime, as revealed in the third article, skill-set analytics allows investors to iterate on their skills, generating higher returns in the process.

Combine all that with the investment thesis presented here – an incremental increase in IT budgets could yield as much as a full percentage point of alpha – and it leads to this recommendation:

Start building an industry-scale investment book of records. Otherwise any digitization endeavor will come crashing down like the Sokovian capital in Age of Ultron.

An IBOR – a well-structured, cleansed and deep repository of portfolio data that provides firms with a unique digital inventory of all their balance and transaction data, holdings, and manager-provided transparency – is the starting point. Get on it and spend to build it now. Firms that have developed IBORs report reduced trading errors, better investment decisions and, as a result, improved performance and pleasure at work.  

It has the benefit of not being an inherently disruptive technology. If your firm can generate an accounting book of records or a performance book of records, it can generate an IBOR.

As Sokovia’s most famous daughter Wanda Maximoff, the Scarlet Witch, sadly knows, magic alone will not keep something from eventually crashing to the ground. You can’t just wave a wand at an IBOR and have it solve your analytics problems for you.

It takes expertise – and real money – to develop an IBOR for your data, let alone do so with modern techniques for storing and accessing data (API strategy anyone?). The buy vs. build is a no-brainer by the way. While a decade ago, as an engineer myself, I would have advocated for a ‘build’, there is no chance that it’s an economically sound decision today. You’ve heard the reasons ad nauseam in this and other posts.  

After the buy decision, of course, you must educate the end users on the team how to use the IBOR. Bear in mind that, once they do learn to use it, there’s no guarantee that they will unless their upper management owns IBOR and promotes it.

If you can get your portfolio managers, traders, analysts, data allocators and IT professionals to work together toward this single goal, then you might find that outperforming the market is your superpower, and that everyone in your firm brings a unique skill to achieving its financial goals. But be careful what you name your super-team. Alpha Flight is already taken.