Fund managers need quants: but do quants need fund managers?

People who are data-savvy and investment data-savvy specifically are an increasingly necessary resource across the investment process. 

But the game has vastly changed. 

The realm of quant models and analysis has expanded far beyond version one of factor investing introduced in the 1990s. 

New alternative data, new methodologies, natural language processing, and increasing access to a global talent pool of data scientists and supercomputers have expanded the capabilities of the humble quant well beyond what they were. The increased use of data has lifted all functions to be data-savvy and has pressured teams to re-train or hire those skills. 

For research-focused quants, this has also meant re-training into fields of data science like AI, machine learning, NLP, and reinforcement learning methodologies, amongst others. 

For more operational quants, this means their skills are in much more demand across lots of other functions within an asset management business. Aside from data cleaning, fraud detection, and operational error detection, these people are also very good at bringing people up to speed with data gathering and data manipulation. Many quants have aspirations to be investors, not just support in the back and middle office.

During my career, I have seen a lot of this tug-of-war between talent investment people that know how to manipulate data and use it to make better decisions and a big business that is hungry for their talents but doesn't have a place for them as investors. 

In short, no. 
 
While quant capability has changed dramatically, it is fair to say most investment teams have not gone along for the ride.
 

Admittedly, it's tough to find people who are both data-savvy and know investment data which is inherently big and complex and can be highly contextual. To understand why, you really have to look back on the role of judgment, evidence, and structure of thinking.
 
A quant will seek to build a framework that captures how they think the world works and finds evidence to prove or disprove their assertions. They will downplay the role of personal judgment and in some cases, dismiss it as unreliable, and filled with behavioural bias. 
 

They will seek to build processes that learn, update, and make decisions based on the weight of probability and their models. Decision-making is therefore constrained to the model: making it transparent, predictable, but at times rigid. 
 
The goal is to tilt the weight of probability in your favor by bringing new data and new processes. That's why you rarely see quants on Bloomberg, because they interface with models first, and the world second.  
 
Most discretionary teams feel like a hippy commune compared to a quant shop. Teams tend to chop and change topics daily, and weekly, get distracted, and fall into and out of market narratives, which they propagate on media and their letters to investors. 
 
They want to be responsive to markets and avoid locking their decision-making into models or data. Outside of a broad umbrella of definition terms like 'value investor', they are hunting and scanning constantly, and rely heavily on their judgment and experience to carry them from opportunity to opportunity.
 
The bridge between the two sides isn't merely a matter of data expertise. It relates to the fundamental idea of how to be successful in asset management: either through building a better collective investment model (and then handing over decision making) or by improving your knowledge and judgment and retaining decision-making and any biases that come with it.  
 
Most non-quant teams will therefore use quants to gather data, create dashboards, create graphs of historical data: all for the use of enabling a conversation and hopefully improving an investor's judgment of that data. But, it's rare to see a team that holds both quantitative models and human judgment in equal standing and that will decide against the former.
 
Along comes AI, which crunches more data in ways that a person cannot possibly match. It simply won't wash for most teams - they will feel like AI is encroaching on their freedoms. 
 
I find it interesting that so many investors look for companies using tech and data cleverly, deploying intelligent automation, but so few will follow suit.

I would say, no. 
 
Despite being an industry filled with smart people and incredible data, it is slow to change. Working on enhanced equity strategies and helping support reporting functions with data tables are still the main uses for a quant. Yet, there is much more innovation in other industries. 
 
Crypto, blockchains, and technology / AI startups present a much more dynamic, fast-moving, and evolving space for young quants to hone their skills. That space is moving so fast that not being part of those industries is detrimental to their long-term viability in this space.
 
If I were starting my career today, I would absolutely not go into investment management. I think it is a slowly dying field in terms of innovation. The investment industry is massive, and it attracts lots of smart people, but it's not where the next generation is headed.
They're joining technology companies.
 
If investment managers want to attract modern quants, they have to ask themselves the following. If your quant tells you to buy a stock or take a position, will you do it? Or, will you only consider it? I

f you only consider their recommendation or want them to clean up your spreadsheet or fix up operational errors, then essentially, you are never going to attract talented quants. You are essentially placing someone with an exceptional academic pedigree in your organisation as a second-rate citizen. You will likely attract someone starting their career, but you probably won't retain them for very long. 
 
Technology and AI companies will put these same people at the heart of their value proposition, train, nurture, and empower them. 

Firstly, funds have to ask themselves: what is the core of my value proposition? Does it lie with my experienced strategists and their judgments, or does it lie with the quality of the systems and data models we can build? Can I take seriously the idea that data will give me a much better answer? Would I consider paying the same amount for a high-caliber quant as a high-caliber strategist? Or am I looking for souped-up data support?

Second, they have to accept the fact that start-ups and private equity are typically younger companies that offer a better work environment, more upward mobility, and better social missions than much of the investment management industry provide. Many funds are shareholders in the companies that compete for their talent, usually through private equity holdings. It may be useful to reach out and speak with these firms and ask what they do so well.

Funds that want to attract modern quants have to be competitive. They should comb through LinkedIn and contact them.

These professionals won't send a CV - that's not how it works.

Give them a mission a purpose and explain why they think they should spend their time with your firm. Just because you're in a privileged and powerful position doesn't mean a modern quant will want to work for you unless you know what you want from them and understand their value.

Responsible use of technology is impacting just about every company in every industry.

ESG investors that can discern the responsible deployment of AI and automation technologies will likely have an enormous impact on the firms' behaviors in this area. Data today is sparse on this topic, and other than a few startups (like EthicsGrade in the UK), we are still at the beginning of this theme.

Automation is probably the first big impact of AI. And, because people are losing jobs and income - and therefore super contributions - I suspect it will get the most attention in the coming years. 

Quantum computing is like a booster shot in the arm of AI and many data-led activities. It is much, much closer than we think. The acceleration of computing power of this specific nature will see more chatbots, butlers, assistants, raising more interesting angles around data privacy, ethical AI, and areas that should increasingly feature in the 'S' of ESG.