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Electronic copy available at: http://ssrn.com/abstract=1679766Electronic copy available at: http://ssrn.com/abstract=1679766
A Dynamic Future for Active Quant Investing
03/31/2011
This is the submitted version of the following article:
“A Dynamic Future for Active Quant Investing”
By Xi Li and Rodney N. Sullivan
Journal of Portfolio Management, vol. 37, no. 3, Spring 2011 (p. 29-36)
Copyright © 2011, Institutional Investor, Inc.
Published in final form at:
http://www.iijournals.com/doi/abs/10.3905/jpm.2011.37.3.029
Xi Li
XL Partners
Boston College
Xi.Li@bc.edu
Rodney N. Sullivan
CFA Institute
Rodney.Sullivan@cfainstitute.org
Abstract
We believe that active quantitative portfolio management is on the verge of
moving toward a more flexible approach capable of capturing dynamics in risk and
return expectations across an array of asset classes. In widespread use today, the
static quant-driven approach to active management is ill equipped to deal with market
environments that diverge substantially from typical conditions. We discuss what
changes are needed at this important juncture for the active quant community to
maintain relevance and improve the odds of long-term investment success. Active
quants must broaden their focus beyond typical systematic bottom-up quantitative
inputs to emphasize top-down qualitative evidence. Such a judgment-based, top-down
(macro-driven) approach offers the flexibility necessary to achieve investment success
in our complex, dynamic capital markets.
Acknowledgments: We are grateful for helpful comments from Paul Bukowski,
Yunfang Cai, Max Golts, Antti Ilmanen, James Picerno, Larry Siegel, Apurv Jain, Peter
Wallison, and James Xiong.
Electronic copy available at: http://ssrn.com/abstract=1679766Electronic copy available at: http://ssrn.com/abstract=1679766
1
A Dynamic Future for Active Quant Investing
Recent events have illuminated the structural problems of traditional active
quantitative management. During the recent global financial crisis (GFC), managers
struggled with the systematic quant approach, which resulted in portfolio
underperformance and unexpected losses to investors—revealing major shortcomings
that have existed for years but now have become readily apparent. The standard
systematic models in today’s quant community are clearly ill equipped to handle
macroeconomic and investment environments that deviate substantially from typical,
long-run conditions. Even though the standard models are now being refurbished to
some degree, our experience suggests that a sea change, not marginal progress, is
needed to accommodate the reality of markets. Unfortunately, mainstream (not all)
active quant management remains focused on models that are inadequate for today’s
complex, adaptive markets.
Investors and financial economists have generally done a poor job of
understanding and incorporating the connection between capital markets and the
macroeconomy. Risk management and portfolio construction techniques must evolve
to accommodate a wider array of possible outcomes, to illuminate the changing nature
of our dynamic global capital markets, and to help guide and manage our decisions
meaningfully. Expert investment management requires a broad and informed view
across global markets. Although we are critical of certain ways that quant modeling is
often used today, our experience suggests that at this important juncture, the practice
of active quant portfolio management should not be abandoned altogether. Rather, we
propose an eclectic approach that combines quantitative modeling with informed
qualitative assessment and offers a robust technique for successful investing.
The inflection point at which we now find ourselves calls for a sharp refocusing
on effective approaches and tossing aside ineffective, “dilequant”1
1We borrowed this term from Mark Kritzman, who has applied it to those who merely dabble in
quant methods.
methods. A more
constructive way forward is within our grasp. Success requires a move toward a
dynamic, top-down (macro-driven) approach, one capable of capturing shifts in risk
and return expectations globally across an array of asset classes and market
environments.
Electronic copy available at: http://ssrn.com/abstract=1679766Electronic copy available at: http://ssrn.com/abstract=1679766
2
This article stems from our previous work at the intersection of investment
management and complex adaptive markets (X. Li 2007; Sullivan 2008, 2009a, 2009b,
2010; Sullivan, Peterson, and Waltenbaugh 2010). Expanding our focus beyond
quantitative inputs to emphasize empirically driven judgment, we examine portfolio
construction that optimizes the strength of signals by quantifying and qualifying
macro-oriented insights combined with solid, bottom-up security selection. Although
data are lacking, the active quant community does not appear to be pursuing our
suggested, macro-driven approach.
Active Alpha
Especially in recent years, the development of modern portfolio theory has
provided the backdrop for the widespread acceptance of the techniques of active
quants and the rise in their popularity. Like all successful investors, successful active
quants make decisions on the basis of informed theory and keen historical insight,
thus fostering market efficiency and sustainability. Although the active quant
approach is difficult to ascertain, its primary component is a systematic, structured
investment process that involves the large-scale collection of relevant current and
historical information summarized in a meaningful, nonarbitrary way for use in
making decisions about the future.2
In essence, active quants seek superior performance through the gathering and
distilling of complex historical information in a systematic fashion.
Active quant management is typified by the
structured, system-driven approaches of Grinold and Kahn (2000).
3
In their purist form, active quant factor models attempt to systematically
capture recurring deviations from the general equilibrium CAPM market model. Active
Quants are so
called because of their intense focus on “quantifying” unique alpha insights—those not
already priced into the market. This quantifying is accomplished via formulaic
empirical factor models applied across a wide array of assets in a hunt for statistically
significant deviations from historical trends. The basic idea is successful forecasting
driven by identifying signals (skill) and applying that skill as often as possible per year
(breadth).
2We use the terms active quant, active alpha, and active portfolio management synonymously.
By quant, we mean traditional active portfolio management strategies, not other quant forms,
such as statistical arbitrage or high-frequency trading.
3Of course, this definition is not limited to quant investors. For a brief overview of both
fundamental and quant investing, including pitfalls, see Sullivan (2010).
3
quants view the traditional CAPM, as driven by the single market factor, an
oversimplification of the realities of markets. Active quants thus seek to generalize the
traditional CAPM with skillful exposures to additional tradable factors. Though there
exists no predetermined set of factors, many strategies and factors do exist. Examples
of such insights include the various documented behavioral biases exhibited by
investors. Collectively, this set of factors forms the driving force behind abnormal
returns and can span a wide range of likely alpha sources. We can easily understand
how investors view the culture of active portfolio management as a valuable and
disciplined process for extracting alpha. By our very nature, we humans love
structure. It is elegant in its intuitive appeal and implementation.
A distinct advantage touted by active quant practitioners is that the process
leads to a highly disciplined investment approach: A rigorous, variable-centric
approach that follows a logical, hypothesis-oriented decision process is largely
uninhibited by the various cognitive lapses in judgment exemplified by the average
investor. Furthermore, computer models have the superior ability to process vast
amounts of information rapidly. Given the dynamic complexity of markets and the
many assets at play, active quant modeling is undoubtedly a useful aid in the ongoing
search for alpha. These various quant attributes can be turned into a competitive
advantage when applied in the right circumstances. Before discussing this way
forward, let us turn our attention briefly to the ways that quant models frequently trip
us up in our search for performance.
Active Alpha Shortcomings
Any discussion of the challenges of successful active portfolio management
must begin with the fact that alpha sources are very rare, even rarer than commonly
supposed. To see why, let us consider persistent alpha capture, which, as noted by
Jarrow (2010), requires two conditions: (1) The activities of arbitrageurs do not
eliminate market mispricing and (2) the alpha source is continually funded in a
structural way, either knowingly or unknowingly, thereby allowing arbitrageurs to
reap profits. Of course, such market conditions occasionally exist, but given these two
4
rather stringent conditions, alpha is certainly not present in abundance.4
Now that we know our biggest challenge is finding active alpha, let us briefly
consider the various ways in which the process-driven active quant approach, as it is
sometimes applied today, can hinder success. Key obstacles can be found in the model
development process: the systematic sourcing and implementing of the active signals.
These issues combine to give active quant investors a false sense of skill.
For this and
other reasons discussed later, active alpha is more elusive than many believe.
The most pernicious issue concerns the inability of static active quant models
to capture change or the unexpected effectively. The future direction of markets—like
the path of a bird just released in flight—is highly uncertain. Although computer
speed and mathematical models are a powerful combination in ferreting out vast
amounts of information and recurring patterns, automating human nature effectively
remains elusive. As recent events have demonstrated, markets seem to mock the
structured, disciplined approach of models. They abruptly jump from one equilibrium
point to another, giving yield to fat tails and rendering the commonly used static
structural models impotent. Consider how, during the GFC, governments around the
world implemented a flood of unconventional policies at lightning speed. These shocks
quickly introduced a variety of new elements that influenced financial markets in
unprecedented ways. No single model can contain all the information necessary to
capture the uncertain path of outcomes and their attendant consequences. A more
dynamic approach, accompanied by empirical judgment, is needed.
Unfortunately, many active approaches have been oversimplified. For example,
they are based on the assumptions of normal distribution and stationarity. That is,
market prices will behave as they generally have in the recent past with the hope that
the strategy can be adjusted just before markets start to change direction. Over a
series of market cycles, little or no value is added by such approaches—indeed,
negative value add is most often the result after fees.5
4One example of persistent alpha is the value of the call option implied by a residential
mortgage-backed security (RMBS) mortgage, which has historically tended to be overpriced by
homebuyers, thus affording RMBS buyers an arbitrage opportunity. Will this source remain
viable in the future?
5 For example, Agarwal and Naik (2004) and Mitchell and Pulvino (2001) find that many hedge fund strategies
possess characteristics similar to a put option.
5
Of course, history serves as an important guide to understanding the
interconnectedness of markets and the range of possible outcomes. In reality, market
linkages, even those established over long horizons, are dynamic, subject to the ebbs
and flows of markets. Models that are “overfitted” on the basis of historical dynamics
can be severely hampered by the structural changes and sudden stops that financial
markets frequently experience.6
Active quant managers attempt to deal with overfitting in a variety of ways,
some of which may be useful and effective tools in the search for alpha (e.g., regime
switching, extreme value theory), but other methods are downright problematic (e.g.,
winsorization, which essentially ignores extreme events and volatile markets
altogether). Perhaps well-established relationships will revert to their old ways
following a particular event, or maybe the newly established structural relationships
will become dominant. In any case, uncertainty cannot be removed from the equation.
Factors are stochastic and time-varying processes; they are inadequate
representations of the reality of complex, adaptive, and unpredictable markets.
These changes can significantly alter the relationships
established by a model temporarily or even permanently.
Put another way, static active alpha factors are built to perform well over time,
on average. Success thus depends on the future’s mimicking history in a structured,
persistent way. But history repeats itself only in broad tendencies. Perhaps driven, in
part, by “physics envy,” many active quants treat factors as factual and permanent,
when they should be viewed as merely temporary, as suggested by the social nature of
markets. Guided by the free will of agents, markets and the economy adapt
responsively over time. Using the same overfitted model parameter weightings through
all macroeconomic cycles will likely not work well. In short, static alpha factors fitted
with historical data may prove to be false factors when applied to future horizons.
Another not-so-subtle point is that we cannot know the true general
equilibrium model that describes market returns. Models miss the richness of
complex, adaptive markets. As stated earlier, although CAPM is a solid theory, it is
viewed as naive by modern investment managers and thus possesses limited
applicability. The well-known Fama–French size and style factors represent a
6To varying degrees, all investors rely on historical information to guide their decisions.
Therefore, rigorous historical analysis is not necessarily problematic unless the user expects
the future to mirror the past. The backward-looking nature of quant investing, however, should
serve as a reminder of the limits of quant models.
6
constructive step forward in generalizing the CAPM. But variables are always missing
from any model that attempts to capture the interactions in capital markets. Even the
best statistical models make simplifying (or hidden) assumptions and can incorporate
only a small number of variables, thus obscuring the variables’ implications.
Even if we could accurately define the true market model, model builders can
describe only those aspects of financial markets that are measurable or can be
reasonably proxied (e.g., the value metric can be measured in many reasonable ways).
Some desirable inputs, however, cannot be reasonably measured or proxied. Consider
the modeling of the accrual anomaly associated with earnings gimmickry. Effective
arbitrage of the impact of any accrual-related earnings management requires an
estimate of those accruals for which management exercises discretion (normal
accruals are difficult to manipulate). Only imperfect proxies exist for discretionary
accruals, however, and any inputs are measured with error. X. Li (2010), for instance,
shows that discretionary accruals are just one aspect of earnings gimmickry. He goes
on to demonstrate how additional earnings management activities regularly used by
managers can also be used to predict performance. In sum, forecast error related to
the modeling process emanates from many sources: omitted or mismeasured
variables, misspecification, overfitting, and obscured or wrong proxies or factors. In
our experience, these errors have led to costly, unintentional bets for some investors.
The empirical weakness of common quant models is evidenced by weak
explanatory power (low R2s), accompanied by significant idiosyncratic volatility.
Residual risk imposes real uncertainty, and it is a primary factor in limiting both
effective arbitrage and the extraction of alpha. In turn, investors seeking to profit from
various anomalies must bear greater uncertainty in outcomes than was previously
understood (see Pontiff 1996, 2006; X. Li and Sullivan, 2011; X. Li and Sullivan, 2010;
and X. Li and Sullivan, 2011a). This uncertainty comes in the form of high
idiosyncratic risk, and transactions costs, which raises costs and hinders the
profitable arbitrage of seemingly anomalous effects. The foregoing leads us to conclude
that active quant models frequently possess biases in what modelers believe to be true
about the relationships established via the modeling process.
7
Moreover, as alpha opportunities become known, overcrowding occurs.7
The aforementioned issues also present major challenges to the business of
implementing the often dense and opaque quant models. Even though quants may
embrace Einstein’s advice to make everything as simple as possible but no simpler,
highly structured models geared to complex, dynamic markets are necessarily complex
themselves. Although investment personnel can grasp a model’s key insights, the
difficulty in gaining a full, working knowledge seems to increase with the square of the
number of inputs. Operational managers are thus challenged to ensure that all
segments of the firm fully understand the implications of any model put into practice.
For success, one must know what risks both the firm and its clients face in the
investment process—and how to manage those risks effectively—as well as what
changes are needed to improve (or worse, fix) a model that is not producing.
As
noted by Lo (2004), markets are not only complex but also adaptive. Such crowding
into the trade has decreased (or eliminated) the persistence of the well-known alpha
opportunities and may even result in a sudden reversal of well-established
relationships (e.g., large caps outperforming small caps for an extended period). For
instance, note that the equity market flare-up in early August 2007 was deemed to be
driven, in large part, by a sudden downdraft of concentrated equity holdings common
to many quant equity funds (Kandhani and Lo 2007). Increased trading commonality
also suggests a rise in market vulnerability as evidenced by increased systematic risk
in recent years (Xiong and Sullivan 2011).
For all these reasons, one may fairly say that these challenges create a high
degree of uncertainty for typical quant performance. Static models make little sense in
a world where risk premiums, correlations, and volatility change dramatically from
year to year.
As mentioned earlier, the rigorous decision-making process of active quants has
value under certain circumstances; but when misused or overused, it can be
detrimental. Models are merely a guide for quality decision making in the face of
uncertainty. They interpolate and extrapolate the world, but by no means offer the
correct interpretation of reality. Therefore, those depending on quant models have a
7Although nuances certainly exist, quant alpha frameworks bear a striking similarity. As
evidence, also consider how most quants rely on the same risk and optimization models
provided by a limited number of vendors, many of whom deliver data updates on the same
schedule. See Li (2011) for a discussion of the optimal global equity investment framework.
8
responsibility to be keenly aware of the limits, boundaries, and risks of quant
techniques and to know when to turn away and seek input elsewhere. This task
becomes more challenging as finance grows increasingly complex.
The shortcomings of active alpha offer a compelling reason to dispense with
ineffective approaches to active quantitative management and refocus attention on the
ways that modeling can be effective. In this way, quants can gain a competitive edge
while supporting client goals.
The Future of Active Quant Investing
Active portfolio management is at an inflection point. The recent market
turbulence has reinforced the notion that active managers are not worth their high
fees. The active quant community has seemingly suffered more than most. Adding to
these troubles, investors are increasingly turning to passive and semi-passive, low-
cost index funds. We predict that such index strategies (in particular, in the form of
exchange-traded funds) will continue to absorb market share in the coming years,
especially from the active quant side, because such strategies in practice today are
easily replicated in a growing roster of fund offerings.
These issues have certainly not gone unnoticed by the active quant community,
which continues to try to improve the effectiveness and efficiency of traditional quant
models, mostly through active signal innovation. Although likely to meet with some
success, this additive approach, in our view, does not go far enough. As discussed
earlier, adhering to a set of fixed factor weights (established as the average of various
market cycles) will lead to unacceptable performance periods. Designing flexible
models with an eye toward quantifying big-picture issues warrants considerably more
attention (see Exhibit 1). Model construction should thus emphasize empirically
based evidence that includes specifications to appropriate risks and the nimbleness to
cope with market turbulence. Models should work well in circumstances that matter.
Exhibit 1
Macro–Risk Factors
9
Inflation
Credit
Country
Currency
Size/Style Rotation
Liquidity
Momentum
Volatility
Economic Growth
The myriad of challenges facing the active quant community notwithstanding,
we foresee a dynamic future for active quant investing. This (not altogether) novel
breed of active quant asset management will exhibit an interdisciplinary culture
focused on extending and updating (not abandoning) the existing core competencies
held by the typical active quant firm. In light of the challenges to achieving investment
success, the need for an agile, top-down, active asset management approach that
integrates both quantitative and fundamental insights is clear. Managers should take
risks if the returns appear to represent fair compensation. At any given time, some
asset classes may offer an acceptable or even generous compensation while others
may offer an unacceptable trade-off. The idea is to take full advantage of time-varying
risk premiums, driven, in large part, by investors’ cycling between risk aversion and
risk adoration.
On this last point, capturing risk premiums cannot be accomplished with
overfitted static models developed via back testing. A forward-looking orientation will
emphasize an eclectic framework with the flexibility to capture the top-down risk
dynamics of markets. In practice, this approach means broadening the inputs to
include global macroeconomic views, which will play a more prominent role in asset
management. The prevailing framework thus switches from static to dynamic asset
allocation and takes into account time-varying macroeconomic forecasts that are
overridden by qualitative judgment, as necessary. With such skill comes the ability to
recognize when a new structural cycle has begun.
In other words, successful macrofactor timing requires the transmission of
informed, empirically based judgment into the portfolio. Moving away from a narrow
focus (e.g., on the convergence to the norm of company-specific factors) and toward
broader macrofactors can be achieved only through a more qualitative approach. The
10
weak statistical significance of many macrovariables may give pause to some in
adopting macrovariables into the formal model framework. But given the frequent,
systemic, turbulent events affecting markets, such insights may indeed be highly
economically significant. They should certainly not be ignored. A judgment-oriented
approach based on empirical evidence will enable investors to better guide risk taking
and thus help clients succeed over the long haul. Obtaining a statistically significant,
but economically insignificant, alpha when the total portfolio is substantially down
does little to help investors achieve their goals.8
Dynamic global asset allocation will necessarily be complemented with a
rigorous, bottom-up assessment of assets, as always. In this connection, an innovative
strategy may well set aside parsimony in favor of a looser, more qualitative approach—
for example, one that mixes fundamental with active quant investing and that
integrates bottom-up stock selection with top-down global macroinvesting. Such a
rich, integrated process that explores the full range of possibilities could offer the best
chance of performance success—say, in recognizing when a new world order is upon
us. Active investment managers could turn macro-driven market cycles into a
competitive advantage—admittedly a delicate task, calling for a competency in
dynamic global asset allocation.
Our recommended framework is consistent with Xiong, Ibbotson, Idzorek, and
Chen (2010), who found that about 80 percent of total return variation is dominated
by changes in the general market, with the remainder evenly split between specific
asset allocation and security selection. In other words, passive asset allocation and
active asset allocation together account for the bulk of return variability. Our
suggested approach is also consistent with Markowitz (2005), who found that the
market portfolio is not necessarily an efficient portfolio.9
Our top-down approach also places dynamic risk management at the fore.
Attention will be paid to much more than market risk. Investors must also give ample
attention to liquidity risk, counterparty risk, systemic risk, and the effects of leverage
8 Several statistical issues also inhibit the effectiveness of pure top-down quant approaches. First, modeling extreme
events yields very small sample sizes and thus little chance at statistical significance even when estimated over a
long historical sample. Second, return modeling generally requires fixed intervals such as monthly, quarterly, or
annual intervals. Such fixed modeling intervals cannot successfully capture the turning points of market cycles.
Maybe for this reason, NBER can only mark the end of a recession long after it has ended.
9Specifically, Markowitz (2005) suggested that the inefficiency of the market portfolio could be
so substantial that it would not be arbitraged away even if some investors could borrow
without limit.
11
This idea appears to be gaining traction. Witness the recent surge of interest in
gaining a better understanding of fat tails (which imply a more frequent occurrence of
extreme events), dynamic correlations, and systemic risk (see, e.g., Kritzman and Y. Li
2010; Sullivan et al. 2010; Xiong and Idzorek 2011). These models provide a
framework that illuminates the changing nature of risk and helps guide and manage
risk decisions.
In recent years, investors have turned their attention to greater diversification
of asset classes in order to generate higher returns and protect assets from market
volatility. We certainly agree that a broad array of asset classes can improve portfolio
efficiency and should thus be included in portfolios. This notion is consistent with
Tobin (1958) and Sharpe (1964), who suggested that portfolios be formed with a
combination of the risk-free asset and the market portfolio of all risky assets.
Nevertheless, many investors undertook asset allocation under the false premise that
certain asset classes would yield equity-like premiums while being largely
uncorrelated with the overall market. But, when evaluated carefully in reality—and as
many learned painfully during the GFC—the uncorrelated asset class is a myth (Ennis
2009). The lesson here is simple: Collecting a risk premium means that you must bear
the risk—there is no free lunch in asset allocation.
Altogether, this suggests that the primary focus should be on that which plays
the biggest role: the asset allocation decision—that is, the process of deciding how best
to adjust asset allocation in accordance with a sensible assessment of the risk–return
trade-off that the various markets are offering. Again, the decision to invest—whether
to take risk—is the most important investment decision. How much market risk to
take entails the two aspects of asset allocation (policy allocation to the general market
and specific asset allocation). The powerful intuition behind our approach is that
proper portfolio construction is an ongoing, dynamic process, one of calibrating from
the top down the set of risk and return expectations for each asset (asset class,
country/region, industry, and security) against current and expected macroeconomic
and investment conditions. Although challenges remain, the upside is a compelling,
flexible framework, one that integrates the time-varying macropicture with
microspecific analysis. Such an approach is within our grasp.
Although obstacles to implementing our suggested approach to portfolio choice
undoubtedly exist, the evidence makes clear that successful investing is dominated by
12
value-driven active asset allocation. As Peter Bernstein (2003) deftly observed, the
traditional strategic approach of fixed-asset allocation is outmoded. The challenge of
portfolio choice is much more than merely selecting for inclusion uncorrelated asset
classes that constitute significant economic exposure and then specifying a fixed
proportion of each.10
As noted by Sharpe (2010), the only portfolio that all investors can hold
simultaneously is the market-weighted portfolio of all assets. So, holding the market
portfolio is best for those without superior knowledge about the relative attractiveness
of asset classes. Anything else is an active strategy. This observation plainly suggests
that the traditional approach of rebalancing to prespecified weights represents an
active contrarian strategy. But, as we have shown, no single fixed-weight strategic
asset allocation is best for all environments.
11
For all these reasons, active quantitative investment management appears to be
on the verge of important change, which is not necessarily a bad thing. At the very
least, it suggests an opportunity. An approach that incorporates a nimble and
dynamic global asset allocation is more likely to yield positive results for all interests
(both principals and agents) over the long haul. Although ours is perhaps not an
entirely novel approach, it is certainly not the current dominant one.
This discussion leads us back to our
main thesis: An industry shift is needed, with a focus on the capacity to understand
the trade-off between asset classes from a macro–point of view—an eclectic approach
that adapts to the market situation. With skillful insight and wide breadth, algorithms
can go beyond a systematic narrative of what has merely happened in the past. This
approach means implementing all relevant quantifiable and qualitative factors,
whether or not those factors can be systematically incorporated.
Conclusion
Active equity management has become increasingly narrow and complex, but
not necessarily wiser. As a result, investors will likely continue to embrace passive and
semi-passive low-cost index strategies and shun high-fee, hidden-beta strategies. For
these reasons, we are at an important juncture in active quantitative investment
10For further discussion on this topic, see Sullivan (2008).
11Of course, managers should regularly compare their asset allocations with current market
proportions.
13
management. To navigate this juncture successfully, the skills of quants can be
tapped for a competitive advantage, but only in the right circumstances.
We envision a flexible and nimble investment approach, one that will more
likely deliver performance success over both the intermediate term and the long haul.
We see quantitative asset management turning to an eclectic framework that
accommodates a wider array of possible outcomes and that copes with the frequent
occurrence of extreme events (fat tails). The distinguishing feature of our technique is
the recognition that investors must negotiate turbulent periods. Today’s standard
active quant models that use static linear methods will, by their very nature, prove
inadequate in such markets. Therefore, expert models must dynamically reflect all
portfolio risk exposures, not merely those represented by typical conditions and
captured in static models. Qualitative judgment based on empirical evidence must
meaningfully accompany any quant-driven, decision-making process.
In sum, we call on the active quant investment community to broaden its focus
beyond the standard bottom-up, systematic model by incorporating a dynamic, top-
down (macro-driven) approach to investment management, one with the flexibility to
capture shifts in risk and return expectations across an array of asset classes and
market environments. Quant methods can be highly useful when accompanied by
qualitative reasoning. Given the extreme events that markets frequently experience,
modelers must take seriously their responsibility to engage deeply.
Endnote: This article represents the views of the authors and does not represent the
official views of the authors’ employers.
14
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