Sixteen Leading Quants Imagine the Next Decade in Global Finance

AQR Capital Management

Cliff Asness

Managing and Founding Principal

Many types of quantitative factor-based investing strategies, particularly the value factor, have had recent tough times, leading some to question if it is “broken.” A common critique argues that value no longer works as too darn many people know about it. But simple knowledge doesn’t kill a strategy. Real things, like becoming too expensive—yes, perhaps because of this widespread knowledge—can.

If value has been “arbitraged away,” we’d expect to see the value spread—the price difference between expensive and cheap stocks—narrow to smaller than historical levels. This is not what we have seen.

Instead, we find that the current value spread is among the widest in history. In other words, value appears currently quite cheap compared to history—more like a shunned out-of-favor factor than one too many people are chasing. While we have generally cautioned against too much “factor timing,” today we recommend a modest tilt toward value.

It is always important to keep an open mind to the possibility that the world might have changed. But the opposite danger, of overreacting to recent performance, is at least equally as important. Trying to improve our factors and find new ones does not mean we should discard the old ones.

New factors are more susceptible to data mining compared to well-known ones, like value, that have strong long-term evidence across many asset classes and geographies. Well-known factors are well-known for a reason, and we should not toss them out lightly.

In other words, the future of quantitative investing should include a continued energetic search for the new but also include a lot more of the past than many realize.

Two Sigma Investments

Alfred Spector

CTO

First, I think we’ll see quantitative approaches—meaning those grounded in advanced data science and systematic techniques—play a growing role in private market investing. We’ve all seen how these approaches can be effectively applied to investing in publicly traded securities. A next logical step is to extend this methodology to data-rich areas like private equity and venture capital.

Some firms are already exploring ways to source and analyze deals quantitatively. Others are applying sophisticated data science methods to increase the growth and profitability of their portfolio companies. Making this shift will require private market investors to approach their work through a very different lens, but we believe that the rich opportunities in certain private markets more than justify the effort.

Second, as more financial services firms embrace artificial intelligence techniques like machine learning, it will become apparent that human talent and effective teaming are key to unlocking their promise. Systems talent and a systematic approach are required to develop the complex infrastructure that enables data aggregation, analysis and computation reliably and at scale.

Equally important are the highly experienced, intelligent, and creative researchers who can select from the plethora of machine learning techniques available and develop approaches that generate true value. So, paradoxically, for the foreseeable future, progress in machine learning may depend even more on people—people with an interest in financial applications and great skill in computer science, statistics and modeling.

Man Group

Sandy Rattray

CIO

At one level the future for quant investing is easy to predict: The alpha produced by simpler, well-understood, linear strategies will erode while newer, more sophisticated strategies will generate higher alpha. These new strategies—many of them non-linear and employing machine learning—will be difficult for most market participants to develop and will certainly not be immediately apparent. Unsurprisingly, the enormous amount of new data becoming available gives us many alpha opportunities. Most of the data is probably useless, some of it is gold. Finally, the continued advance into markets less populated by quants will prove a rich seam—corporate bonds stand out to me but are by no means the only opportunity.

Underneath all of this, the best quants will continue to become more efficient, especially in trading. This may allow managers that have invested in trading technologies to exploit opportunities that are simply not available to others. Inefficiencies available only to market makers will increasingly become strategies for quant investors: a world where market makers earn bid-offer spread and investors pay it will seem like a quaint description of the past.

Dimensional Fund Advisors

Savina Rizova

Head of Research

More managers will hop on the big data bandwagon to try and time the market or find mistakes in prices. Investors are better off having a robust investment framework that avoids the trap of chasing spurious data patterns. We believe that market prices, set in public competitive capital markets, represent the most complete prediction of the future available. By using information in market prices and adding value through implementation, investors can pursue higher expected returns without having to outguess the market.

Investors’ desire to incorporate ESG will be served well by systematic approaches. Heightened investor demand for ESG considerations matches up well with systematic approaches—offering broad integration of ESG through an investment process enables investors to remain diversified and stay focused on the drivers of returns.

Systematic bond strategies will overtake the Bond Kings. Investors want diversification, higher expected returns and better risk controls. They don’t want surprises. Taking a systematic approach to fixed income—as Dimensional has been doing since 1983—helps investors better achieve their goals.

PanAgora Asset Management

Eric Sorensen

CEO

Price-weighted indices are now concentrated momentum-bully portfolios. Risk-weighted portfolios, value portfolios, quality portfolios and certain defensive equity portfolios will display major stochastic dominance, versus the overbought price-weighted behemoths.

Eddie Qian, having invented multi-asset risk parity, will live to see a future supremacy in risk weighting for all modern portfolios—not just global multi asset, but within fixed income, equity and commodity portfolios.

Machine learning deployed on stock prices will fail. Price has no memory; unstructured ML leads to overfitting, counter-intuitive forecasts, illogical clusters and spurious results. ML applied to natural language processing of CEO veracity or chat-room sentiment will prove better predictors of performance than fundamental analysts (psychiatrists) in face-to-face executive meetings. ML will grow as an alternative. Hierarchical, interactive tree structures (random forests) will prove valuable having causality and targeting fundamental variables (not price).

In 2019 we hear rumors of marketing “quant fixed income.” Some will have to go back to read the old papers. The truth is that fixed income is all quant already, but the tweaking and marketing of fixed income quant has a bright future.

Quants will own the ESG world. Some of the ESG metrics are alpha enhancing, while others are not. Beginning with large European institutions, the ESG landscape will evolve several ways. Firstly, entities will describe their preferences for specific ESG tilts. Secondly, quants will model the alpha enhancement (erosion) in implementing the tilts. Thirdly, entities will prescribe their ESG risk versus alpha return appetite. Fourthly, quants will create conditionally optimal portfolios, as well as implement custom or third-party benchmarks. Fundamental teams will not come close to such efficiency in results or price.

Research Affiliates

Rob Arnott

Founder

The efficiency of quantitative methods means that our industry needs fewer people to serve an ever-larger asset base. This has led to fee wars, a rush to near-zero (even negative) pricing, and has eviscerated the non-quant finance and investing community. We may already have seen peak employment in financial services.

Cost pressures are a two-edged sword. Many investors are now more motivated to save a few basis points in fees than to seek 100 basis points in additional returns. But R&D is not free. The pace of innovation may slow, though it won’t stop, and economies of scale will concentrate the investment management business. We will see fewer asset managers which are mostly much larger than today; new entrants to the business will find “critical mass” far harder to achieve.

So, what hath quant wrought, and what surprises lie ahead? Just as the traditional analyst and portfolio manager had a massive blind spot in overlooking the power of quantitative methods to strip emotion out of our investing decisions, the now-dominant quant community has blind spots. “To a man with a hammer, everything looks like a nail.” To a quant, anything that can’t be quantified is ignored. And historical data is our compass, even though we know that “past performance is no guarantee of future results.”

Instead of chasing the performance of the latest hot stock or hot fund, quants chase the factors and strategies with the best historical performance. It’s impossible to publish a new idea without doing a Fama-French attribution, to make sure the idea isn’t merely another value, size or momentum factor in disguise. But, there’s no pressure to answer the question, “did this strategy or factor win merely because it was becoming more expensive?” Whatever is newly expensive (relative to the market) is likely to have wonderful past returns and mediocre (or worse) future returns.

It’s no longer enough to apply quantitative tools in investing; these tools have to be used intelligently, with awareness of their often subtle vulnerabilities. These changes will drive a growing divide between quants who blindly follow wherever the data leads them, and quants who ask, “what are the limitations of our methods, and where can they go wrong?” In time, the latter—and their customers—will win.

BlackRock

Andrew Ang

Head of Factor Investing Strategies

Through the next decade, financial advisers will use the language of factors to create bespoke portfolios across hundreds or sometimes hundreds of thousands of accounts. Individuals will be more outcome-oriented with factors—growing their nest egg with value and momentum, or being more defensive with factors like minimum volatility or quality. Today and into the future, institutions will view their portfolios—from cash and equities to private alternatives—all through the lens of factors. They will use factors increasingly to minimize unintended risks and maximize diversification.

Capital Fund Management

Jean-Philippe Bouchaud and Marc Potters

Chairman and CIO

As the techniques the quant pioneers discovered proliferate, the industry’s leaders and intellectual frontier will focus on creating models based not only on historical data, like today, but on a fundamental and more data-driven understanding of human behavior. Past patterns can be used to predict future prices but measuring and analyzing human biases in myriad circumstances will lead to the creation of an entirely new suite of models, less prone to the curse of overfitting and to crowding. These techniques will not just be the preserve of the investment industry but also be used by policy makers and central bankers.

Investors are increasingly reflecting society’s growing expectations that businesses need to do more than just make a profit. But current frameworks for assessing non-financial performance are inadequate and inconsistent. Quant techniques will also help to standardize ESG, enabling investors to make choices based on meaningful data.

And as these quant techniques infuse the industry so too will their characteristics. Links with academia will strengthen further. The cult of the star manager will recede as skill can be better separated from luck and as firms accept that collegiality underpins successful quant investing.

Sanford C. Bernstein

Inigo Fraser Jenkins

Head of Global Quantitative Strategy

The big challenge in investing for the next 10 years is going to be: How can asset owners beat inflation in a lower return world and where diversification is harder to come by? Quant approaches can play a key role in this, both in generating return streams and in lowering the price point for certain kinds of returns. Investors have long understood that they don’t need to pay for broad-market beta. We suggest that they shouldn’t have to pay for static exposure to factor beta either.

It is not intrinsically more complicated to track a broad market index such as the S&P 500 than it is to track an index of value stocks, so we think the price point of these smart-beta products is going to fall to zero, or even below. This effectively moves the dividing line between alpha and beta. Asset owners should pay an active fee for returns that are idiosyncratic to cheap factor returns.

There won’t really be a separate “quant” category in 10 years—all that matters is a combination of systematic betas and idiosyncratic alpha. Some quants will be directed to efficiently creating the former and some approaches will attempt to pitch in the latter camp.

Aspect Capital

Anthony Todd

CEO

As the use of technology in finance continues to grow and algorithmic aversion decreases, we think that the quantitative investing industry is becoming more mainstream. The industry is uniquely placed to support investors’ growing needs for customization. Institutional investors are increasingly sophisticated and they are looking to build stronger partnerships with a smaller number of managers with a range of capabilities.

We are also hearing investors’ concerns about their portfolios and their ability to generate returns in today’s macro-economic conditions. As volatility and uncertainty rise, they are becoming increasingly aware of the importance of non-correlated liquid alternatives such as quantitative strategies. In fact, capturing alpha is undeniably a challenge as we are seeing more participants in markets. We believe that this can be offset by continuing to invest in research, technology and the use of alternative data. This rich set of new data provides a world of opportunity for quantitative research. However a manager’s experience remains key to efficiently extract a persistent signal from the noise.

Finally, investors are increasingly focused on corporate and social responsibility. Quantitative managers are well placed to demonstrate the impact of an investment beyond it rising in value.

JPMorgan Chase & Co.

Marko Kolanovic

Global Head of Quantitative and Derivatives Strategy

In the next decade, we expect further increase in the use of technology and quantitative models in the investment process. This will be true in both design and execution of trading strategies. Use of big or alternative data will continue and new strategies will emerge as innovative data sets become available. Most of the fundamental investors will become familiar with principles of quant strategies and will incorporate it in their investment process. This will cause traditional quant strategies (e.g. factors) to lose effectiveness and become increasingly volatile. The investment landscape will become saturated with passive and quant strategies, and this will increase market fragility and make it more prone to tail events. The next severe recession will put the new market structure to the stress-test, and that may lead to widespread failures. Quant models that are calibrated on historical data will not be able to anticipate these events. In the aftermath of the crisis, investors and managers will aim to re-introduce more human oversight and discretionary decision making in the investment process and liquidity provision.

APG Asset Management

Gerben de Zwart

Managing Director for Quantitative Strategies

There is a strong case for quant investing because of the overwhelming amount of data, computer process capacity that’s almost unlimited, cheap storage and open-source computer codes—these four drivers mean a lot of investments will be data-driven.

To process overwhelming amounts of data, you need resources, IT people, data budgets. So it will be very challenging for the small boutique to find an edge. The barriers of entry will rise. We think either consolidation or smaller boutiques going out of business is the scenario for the future. At the same time, dispersion across manager returns will widen because there are now so many different data sets. Each manager will become more unique instead of all targeting the same styles or factors.

Traditional quant strategies have a risk of crowding and getting disrupted. Companies’ structures and business models have changed, and have moved from tangible assets to intangibles. Our vision is valuation is an important driver, but maybe you need to come up with more advanced definitions than in the past and take into account these new developments.

AlphaSimplex

Kathryn Kaminski

Chief Research Strategist

The next decade for quants is about measurement and methodologies. By measurement, we will find new ways to quantify many “fuzzy” complex concepts such as empowerment, bias, and selection. If you cannot measure something, you cannot manage it. The success of quants going forward is going to be how to measure new areas in finance. It is not necessarily doing something new but a new perspective on things that we already do every day. Simple examples are how today we can define metrics to examine softer concepts like corporate governance, carbon footprints, or diversity and inclusion. It is not just data; success is in how you collect the data, what you can measure from it and how you use it.

Acadian Asset Management

John Chisholm

Co-CEO

Over the past few years the growth premium—the relative valuation of growth to value stocks—has increased significantly and is now well above average, making value relatively more attractive. We believe that we are likely to see a rebound in the relative performance of value, whose long-term outperformance is deeply rooted in human behavior and is likely to persist. The likely headwinds to growth include increased regulatory scrutiny, rapidly evolving technologies challenging incumbent business models, and the overvaluation of growth relative to value today. The timing is uncertain, but given that the last 10 years have been the longest run of growth outperformance on record, we suspect value’s time is coming soon.

WorldQuant

Igor Tulchinsky

Founder

Access to vast amounts of data and advancing technology is the new norm. Quant methods are spreading across the investment industry and will become status quo. Only firms with a truly unique approach to discovery and decision-making will set themselves apart and stay ahead of the crowd.

The leading quant firms of the future will position their creative human talent to utilize machine capabilities in ways that find more diverse insights, at a faster pace, and from larger pools of data. Increasing collaboration will be a critical component to compound the benefits of important discoveries across teams and geographies.

BNP Paribas Asset Management

Jean-Philippe Olivier

Head of Investment Specialists, MAQS

We are overwhelmed by a growing supply of data and face the challenge of drawing factual conclusions and true meaning against a backdrop of apophenia—mistakenly perceiving connections and meaning between unrelated things. Technology and human knowledge have already helped analysts to isolate what matters within large data sets. They have already found the few relevant outperformance factors that increase the risk-reward ratio over time; there is little more that machine learning can add to our understanding of financial data sets.

The next challenge is for quants to find relevant indicators in universes with far less data, such as macro news. Moreover, when data lags, we must seek ways to gain an edge by benefiting from the most immediate data. Forecasting will be replaced by nowcasting. Quants are also contributing to defining the right rules for ESG scoring, targeting coherent financial actions and real world impact. “Quantamental” describes the enhancement and augmentation of human portfolio managers with new technologies and quantitative techniques to analyze large data sets and assess risks. What will matter is making sense of data. The human captain must drive the ship, know the pitfalls to avoid and the direction to take to reach the required destination. Old quant is dead; long live quantamental!