What Will We Tell The Clients?

This year's events have shocked many clients, and caused many investment managers to question the conceptual basis, and perhaps legitimacy, of their profession. Many are also wondering if their own knowledge and skills are up to the challenges they and their clients will face over the next few years. With many of our readers brooding this Christmas about what to write in their end-of-year client letters, or anxiously waiting to read them, we offer our thoughts on three critical questions:

• Can strategy add value in the financial management business?
• Why were so many investors and advisers surprised by this year’s events?
• How do we expect the industry to adapt in 2009 and beyond?

Can Strategy Add Value?

Let's start with a concise definition of strategy: it encompasses the ends, ways, and means to achieve long-term goals in the face of uncertainty. To elaborate a bit further, long-term goals typically include (a) survival; (b) some measure of economic well-being; (c) achievement of some non-economic purpose; and (d) some type of constraint on a strategy's maximum chance of failure.

The ends of a strategy are the sequence of the objectives it is intended to achieve, along with a clear statement of why they are important - e.g., how they relate to achieving the long-term goals. In the military, this is known as the commander's intent. Long-time students of strategy often point out that accurately framing the problem (e.g., initial sensemaking about the elements, relationships, and dynamics in the system of interest) and getting the sequence of objectives right is more critical to long term success than most people realize, and consequently is too often given inadequate attention (for evidence of this from studies of business manager performance, see "Mental Models, Decision Rules, Strategies and Performance Heterogeneity" by Gary and Wood, and "Pitfalls in Managerial Decision Making: A Systematic Perspective" by Maani and Li).

The means of a strategy are the resources that are available to implement it, whether they are material, financial, informational, psychological or social (e.g., the existence of alliance partners). "Ways" are what most people think of when they hear the world "strategy" - the conceptual approaches and methodologies that enable the achievement of specified ends with available means within any specified constraints (e.g., maximum chance of failure).

Critically, strategy operates in the realm of uncertainty, where for certain critical variables, the range of possible future outcomes, much less their associated probabilities is unknown. High levels of uncertainty typically characterize complex adaptive systems like the economy and financial markets, in which effects have multiple causes, which often operate over different time scales, are non-linear in their impact, and evolve over time. Situations involving high uncertainty stand in contrast to those involving only "risk", where the range of possible outcomes and their associated probabilities are either known or can be estimated with high confidence.

In this sense, strategy can be contrasted with planning. Strategy is a synthetic, creative process that operates over time horizons characterized by high levels of uncertainty (e.g., due to the ongoing evolution of a complex adaptive system such as the economy or financial markets). Planning is an analytic, deductive process that operates over shorter time horizons that are primarily characterized by risk.

If the dominant goal of strategy is effectiveness, the dominant goal of planning is efficiency - the conservation of resources while achieving specified objectives. In his excellent paper on strategy, ("Refining the Art of Command for the 21st Century"), retired General Huba Wass de Czege notes that in the original Greek, the "Strategos [army leader] invents strategema, or stratagems for maneuvering forces into the most advantageous position prior to battle....However, even the most brilliant Strategos could not win without clever, clear-headed and brave Taktikos (literally, "those fit for arranging or putting in order")....to make many crucial arrangements to implement the strategema and carry the day during the heat of battle...The Strategos gains understanding from inductive reasoning and synthesis, the Taktikos from deductive reasoning and analysis. The former takes an ill-structured problem and gives it conceptual structure; the other transforms that conceptual structure into concrete results."

While the Greeks understood that the roles of both the Strategos and Taktikos are important to an organization's success, many people today seem to confuse the two, and indeed prefer planning to strategy, perhaps because of the false sense of certainty it seems to offer. Consider a few examples from widely different areas. How often have you seen boards and investors express their disappointment (or worse) with a CEO who misses a quarterly earnings target? Even as the competitive environment has become more uncertain, boards and investors (and not a few CEOs) still seem reluctant to acknowledge this change, perhaps because they fear that acknowledging their lack of traditional control (in the planning sense) could be interpreted as a sleight on their competence, or worse, potentially expose them to litigation. This situation has many similarities to ones faced by intelligence and military officers in their discussions with political policymakers, many of whom fear the electoral consequences of acknowledging the extent of uncertainty we face. Similarly, many clients who turn to financial advisers seek a sense of control and security in predictions and plans, and shy away from confronting the more challenging requirements of success in the face of uncertainty. In the face of these normal human tendencies, Dr. Harry Yarger of the U.S. Army War College has written (in his book, Strategic Theory for the 21stCentury), "strategy formulation is not the domain for the thin of skin or self-serving. Detractors stand ever ready to magnify a strategy's errors or limitations. Even success is open to criticism from pundits who question it role, methods or continued validity. Furthermore, strategy achieves strategy consequences by the multiorder effects it creates over time - always a point of contention in a time-conscious society that values quick results and lacks patience with the long view. In the end, it is the destined role of the strategist to be underappreciated and often demeaned in his own time. Consequently, strategy remains the domain of the strong intellect, the life-long student, the dedicated professional, and the invulnerable ego."

Given uncertainty, the success of both strategy and plans also depends on successful adaptation to changed circumstances. In the case of planning, this usually takes the form of established contingencies - branches and sequels to existing plans that are executed as the outcomes of known risks are resolved over a relatively short time frame. Strategists, however, must not only prepare to deal with so-called "known unknowns" (i.e., identified uncertainties whose range of outcomes and associated probabilities cannot be estimated with an acceptable degree of accuracy), but also with "unknown unknowns" or simply "unknowables" - situations in which we are ignorant of the existence or importance of a variable until it bursts on the scene and has a substantial impact on the achievement of objectives and goals.

To deal with uncertainties, strategists take a variety of approaches, including developing better measurements and theories to move uncertainties into the realm of risk (see, for example, "The Known, the Unknown, and the Unknowable in Financial Risk Management" by Diebold, Doherty and Herring), employing so called "maxmin" conceptual approaches which are designed to achieve minimal acceptable objectives under the worst foreseeable circumstances (see, for example, Dr. Yakov Ben-Hami's book Information Gap Decision Theory: Decisions Under Severe Uncertainty), and identifying opportunities to hedge exposures to negative uncertainties at an acceptable cost. To deal with unknowables, strategists take three main approaches. First, they ensure clear understanding, at all levels of the organization, of the objectives being pursued and why they are important to achieving long-term goals. This clarity of "commander's intent" focuses and catalyzes adaptation to new circumstances. Second, strategists pay close attention to the development of an organization's learning and adaptation processes, including where it focuses its attention, how feedback loops work, and the ways it generates, selects, and develops new ideas. Dr. Anne Marie Grisogono of the Australian Defense Science and Technology Organization is one of the leading experts in this area, and has written a number of very interesting papers on adaptation (see, for example, "The Implications of Complex Adaptive Systems Theory for C2' and "Success and Failure in Adaptation"). She notes that strategy must take into account five different levels of adaption: (1) "Action in the World" - fine tuning the implementation of existing approaches and plans; (2) "Learning" - expanding or modifying existing approaches; (3) "Learning to Learn" - improving the effectiveness of our own learning processes; (4) "Defining Success" - improving the alignment of organizational fitness measures (e.g., that are used to select the investment options that will receive resources) with the ones that are actually used in the "market ecosystem" to determine which organizations survive over time; and (5) "Co-Adaptation" - consciously tuning our interaction with those other systems connected with our own organization.

Finally, successful strategists realize that increased efficiency is usually purchased as the cost of reduced adaptability. Hence, they aggressively defend apparently inefficient investment in a range of options or capabilities that is diverse enough to provide a rich set of potential responses when unknowables arrive with bang. To put it differently, strategists seek not optimal approaches, but rather ones that are sufficiently robust to ensure at least a minimal level of organizational resilience in the face of uncertainty and unknown unknowns.

It is clear that strategy, defined in this manner, is widely believed to add value in areas as diverse as business management and the employment of military power to achieve national goals. Can the same be said for the field of financial management? In theory the answer is clearly "yes." Financial professionals can work with clients (be they individuals or organizations) to clarify long-term goals, and to develop strategies that maximize the chance of achieving them in the face of uncertainty. They can translate these into short-term plans, and maintain what Grisogono calls an "adaptive stance" - an awareness of the uncertainties and conjectures with a potentially large impact on key decisions, and an active search for information that could disprove current assumptions in these areas.

However, it is also the case that in practice, reality often differs from this ideal. For example, career, geographic location, housing, borrowing, and risk management choices (broadly construed) should all logically be part of a financial strategy for achieving life goals, in addition to decisions about when to retire, post-retirement income and bequest targets, how much to save each year, how to minimize taxes, how to allocate one’s financial assets, and whether/how to use passive and active investment management approaches, as well as annuitization. Yet how many financial professionals are able - either individually or part of a network - to offer an integrated strategy to clients that covers all these areas? For example, how many advisers, when reading this, would say that career advice is for headhunters, and housing is for real estate agents? Essentially, this is the equivalent of telling clients that they must be their own strategist (or, to use a construction analogy, general contractor, who integrates the work of various specialists into a coherent finished product). To be sure, there are financial advisers who offer comprehensive approaches, and software companies that increasingly support their efforts, such as Economic Security Planner (www.esplanner.com). But overall, our impression is that too few are adding as much value as they could for their clients. Some will say this is due to those clients' inability to recognize the value of such advice, and unwillingness to pay for it. Our response is that if we are convinced of the value of comprehensive advice, and of some advisers' ability to provide it (either individually or more likely as part of a team), then fiduciary duty implores us to keep seeking new ways to help clients understand the value of a more strategic approach to their futures. But, to reiterate our answer to this section's initial question, we have no doubt that strategy can add substantial value for clients.

Why Were So Many People Surprised in 2008?

We'll begin our analysis of this question with a model that will help us identify the root causes of surprise. Every day, human beings with limited attention face a torrent of information and must decide what to take into their equally limited active memory. Part of this attention allocation process is under conscious cognitive control, and part is automatic, driven by emotional responses that were hard wired into human beings ages ago when they were fighting to survive on the East African savannah (e.g., we are programmed to pay heightened attention to potential threats to our survival). Researchers have also found that the relative balance between the rational and emotional direction of our attention is governed by our existing emotional state - for example, high anxiety leads to more attention to potential threats (see, for example, "Affective Influences and Selective Attention" by Fenske and Raymond; and "How Brains Beware: Neural Mechanisms of Emotional Attention" by Patrik Vuilleumier).

Other research has shown that social factors (and in particular, possible threats to our social standing in a group) also influence how we direct our attention (see "Conscious and Preconscious Selective Attention to Social Threat" by van Honk, et al, and "Thought and Behavior Contagion in Capital Markets" by Hirshleifer and Teoh). The impact of this on investor behavior and asset returns has been shown to be substantial by a number of researchers (see "All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Instititutional Investors" by Barber and Odea; "A Tale of Two Anomalies: The Implications of Investor Attention for Price and Earnings Momentum" by Hou, Peng and Xiong; "Bubbles, Rational Expectations and Financial Markets" by Blanchard and Watson; "Rational Herding in Financial Economics" by Devenow and Welch; "Information Diffusion Effects in Individual Investors' Common Stock: Purchasers Covet Thy Neighbors' Investment Choices" by Ivkovich and Weisbenner; and "Leading the Herd Astray: An Experimental Study of Self-Fulfilling Prophecies in an Artificial Cultural Market" by Salganik and Watts).

Once information inputs have been collected, our brains then attempt to extract meaning from them while using as little of our scarce cognitive capacity as possible. In broad terms, this processing is intended to produce three outputs: (1) rational and emotional categorization of the information in light of our conscious goals and usually unconscious needs (since processing aggregated categories requires fewer cognitive resources); (2) a set of possible actions (again, to save cognitive resources, we first attempt to use previously learned "condition-action" rules); and (3) a set of expectations about the results of different possible actions (including their emotional outcome). We call the results of these processes our thoughts and feelings. For the great majority of information we attend to each day, these processes operate unconsciously, as our existing mental models direct our attention and guide the processing of information we collect. In a small minority of cases, this processing happens in a partially conscious manner, in that we are aware of the slower cognitive manipulation of information inputs, but unaware of the much faster emotional (or, as it is also called, "affective") evaluation that is also occurring. In the next stage of the process, we choose which of the possible actions to execute, based on the range of internal and external, rational, emotional, and social incentives we face. And once again, this decision is not made in a wholly conscious manner (e.g., did you ever hesitate to walk down a street because you just had a "funny feeling" about it?). Once we act, uncertainties are resolved, random effects occur (which together we often call "luck") and we evaluate the results of our action using one or more metrics. Sometimes these results trigger conscious learning, more frequently they trigger unconscious learning, and most often they merely serve as new information inputs as the process enters a new cycle.

As you can see, processes through which we cognitively and emotionally interact with the world are complex and not wholly under conscious control. While at one level this fills us with awe, at another, it makes us realize there are a lot of things that can go wrong at every stage of the process. To limit the load placed on our brains by attention related processes, we subconsciously filter information, sorting it into recognized patterns (e.g., a tree). However, there is considerable evidence that we don't use all the available sensory data to construct these patterns, and instead to a great deal of "filling in" in order to conserve cognitive resources. A commonly used example of this is called the "Noah Illusion." Ask people "how many animals of each kind did Moses take on the Ark?" Most people will answer "two" without noting that it was Noah, not Moses, who built the Ark. Examples like this are more common than most people realize (see The Science of False Memory by Brainerd and Reyna) and contribute to the so-called "hindsight bias" in which people have a distorted view of the accuracy of their past perceptions, and hence fail to learn and update their mental models.

This is critical, because the conscious direction of our attention is guided by our existing mental models. For example, in January, 2006, Markit and CDS IndexCo launched the ABX.HE index, which tracks the pricing of credit default swaps on an underlying portfolio of bonds backed by subprime U.S. mortgages. The press release accompanying the launch noted that the index was intended to "give clients an efficient, standardized tool with which to quickly gain exposure to this asset class...on both the buy-side and the sell-side...by building liquidity and transparency." In retrospect, this was a critical turning point in the building housing crisis, as for the first time it enabled the aggregation of investors' views on the value of the underlying subprime securities. Yet until the crisis was well underway, relatively few investors attended to ABX prices, since their existing mental models did not deem it important.

A second example is the downgrade of General Motors and Ford corporate bonds in May 2005 (which in some ways mirrored the Russian and Long Term Capital Management crises of 1998). As documented by Acharya, Schaefer and Zhang ("Liquidity Risk and Correlation Risk: A Clinical Study of the General Motors and Ford Downgrades"), this episode highlighted the underlying relationships and risks that would later be at the heart of the September 2008 near-meltdown of the global financial system. Specifically, the authors show how the downgrade triggered a sell-off in the bonds and a widening in credit default swap spreads that in turn caused liquidity problems for large market makers, which drove a sharp rise in the correlation of price changes for a much wider range of bonds and CDS. Yet with the benefit of hindsight, it is clear that too many investors failed to adjust their mental models, and pay sufficient attention to the risk posed by the rising use of leverage by many large market players, and declining liquidity for many instruments - even after New York Federal Reserve Bank President Timothy Geithner reinforced these points in his May 15, 2007 speech on "Liquidity Risk and the Global Economy."

A third example is the continued buildup of imbalances in the global economy in recent years, which we have repeatedly highlighted in our writing. These include the growing U.S. current account deficit, foreign holdings of U.S. dollar denominated claims on the United States (the necessary counterpart to years of current account deficits), the replacement of foreign private purchases of U.S. assets by foreign official purchases (as private investors saw what was coming, forcing central banks to replace them in order to prevent - or, as it turned out, delay - a crisis), and the heavy dependence of world demand on the behavior of increasingly leveraged U.S. consumers. Either many investors were under the impression these imbalances could continue to grow forever, they had high hopes that increased domestic growth in China and other emerging markets would enable them to be gradually unwound, or, more cynically they thought they would be smart enough to get out before the inevitable crash (for evidence of this phenomenon, see James Montier's description of the technology bubble, "Running with the Devil: The Advent of a Cynical Bubble", and "Hedge Funds and the Technology Bubble" by Brunnermeier and Nagel). On all counts, the mental models that counseled either ignoring or minimizing the significance of growing imbalances proved to be deficient.

Clearly, inaccurate mental models of the economy and financial markets (i.e., "priors" from a Bayesian perspective) may have been one of the main causes of the surprise experienced by many investors in 2008. The underlying issue here is large, and, in our view, absolutely fundamental: are financial markets better described by the prevailing "efficient markets" hypothesis (EMH), or an alternative model based on complex adaptive systems theory? Which theory should we use to draw conclusions from the data we observe? Some type of efficient markets theory currently forms the basis for many investors' mental models, with its core assumptions that markets are usually in or close to equilibrium, and asset prices therefore are, at the margin, close to their true values because they are determined by the actions of reasonably rational investors who incorporate most available information. Because of its simplicity, EMH has been very successful as an idea (or, more specifically, a meme). Yet over the past twenty five years, a substantial body of evidence has accumulated that shows it is a far from perfect description of the way real markets work. One stream of thought has attempted to extend the EMH while staying true to many of its underlying assumptions (e.g., the three factor model of Fama and French, the four factor model of Carhart, and other models that add a fifth liquidity factor). In essence, these extensions assert that apparent asset pricing mistakes reflect a failure to take missing rationally priced risk factors into account, and are not evidence of more serious shortcomings of the underlying "rational agent" approach.

The alternative to this view is, as we have noted for years, an approach based on the application of complex adaptive systems theory to financial markets. In broad terms, complex adaptive systems (CAS) are populated by agents (e.g., investors) pursuing different goals, who adjust their strategies over time according to their perceived effectiveness. These agents have limited attentional and cognitive processing resources, and their behavior is also influenced by emotions and social considerations. In such systems, observed effects can have many causes, some of which evolve over time and some of which may be non-linear in their impact. The signature characteristic of a CAS is the inability to use knowledge of agents' decision rules to predict the evolution of system level effects and novel outcomes that emerge over time from agent interactions (see "Evolution of Behavior in the Prisoner's Dilemma" by Kristian Lindgren for a classic description of this process). Complex Adaptive Systems are seldom in equilibrium, yet are generally attracted to it (see, for example, "Do Asset Prices Reflect Fundamentals? Freshly Squeezed Evidence from the OJ Market" by Boudoukh et al). Yet while prices may significantly diverge from fundamental values, the evolving nature of agents' decision rules and relationships makes it very hard (but not impossible, when the system is in a relatively stable state), to predict these departures with consistent accuracy beyond simple luck. Moreover, this also implies that when dealing with a complex adaptive system, an analyst must constantly question and be ready to adapt his or her own mental models for making sense of its behavior. And as most readers already know, staying true to this admonition requires a degree of intellectual curiosity and humility that is too often lacking in the financial services world (e.g., the type of rigorous "after action reviews" and course of action critiquing techniques used by the military are notably absent in many corporations).

Finally, unlike more stable physical systems in which normal (Gaussian) distributions are a good statistical description of the range of possible outcomes, complex adaptive systems are more often characterized by power laws and statistical distributions with a higher percentage of more extreme outcomes. This is critical, since a substantial portion of current asset pricing theory is built on the back of the normal distribution. When industry professionals refer to their surprise at the repeated appearance of "ten standard deviation" returns (e.g., severe losses), the underlying assumption is that the process generating them should produce a normal distribution. While less convenient as an excuse, it seems much more likely that the people making these statements simply don't understand the underlying return generating process, which, in a complex adaptive system, is unlikely to produce normally distributed results. For example, Hyman Minsky's "Financial Instability Hypothesis" is but one description of how such extreme changes can come about, not through external shocks, but through the internal workings of the system itself.

The application of complex adaptive systems theory to financial markets is being led by a growing number of researchers who, up to now, have been viewed as outside the mainstream of economics and academic finance. They include W. Brian Arthur, J. Doyne Farmer, Blake LeBaron, Cars Hommes, Didier Sornette, Eric Beinhocker and Andrew Lo, who has coined the term "The Adaptive Markets Hypothesis" to describe this new paradigm (see his paper with the same title for an excellent overview). Some of these researchers, and others who share their views, (including, we immodestly note, ourselves), have been warning for quite some time about the crisis that has now arrived with stunning force. They include Stephen Roach, Jeremy Grantham, Wynne Godley, Raghuram Rajan, Bill White, Nouriel Roubini and Martin Wolf, among others. Yet theirs and other writers' warnings were largely ignored by the majority of investors. At a time when many clients now mowing beyond initial shock and disbelief, and beginning to angrily ask "why didn't anyone tell me this was going to happen?" it is critical to face the painful question of why warnings were disregarded. In our view, there are three main suspects.

Some investors (mainly individuals) may have simply been unaware of the warnings. It is not unusual for individual investors with many other things going on in their lives to pay little attention to financial news until a crisis has occurred and the time for value conserving action has passed. Nor is it unusual for people to dismiss their own private doubts about a course of action they see many other people confidently pursuing. So-called "rational herding" theory suggests this may be a reasonable course of action when people whose behavior is being copied are considered better informed or to have greater expertise in the area in question. And when their behavior meets with visible success, envy may further reinforce others' desire to imitate them. To put it bluntly, a lot of people with very busy lives may have thought, "hey, if the smart investment guys aren't worried, why should I be?"

Another obvious explanation is that investors were aware of the warnings that were offered, but didn't believe them because they were too much at odds with their existing mental models of how financial markets work, and the emotional and social cost of changing those models was too high. This is certainly a logical possibility; we have frequently written about the so-called "confirmation bias" that causes human beings to pay more attention, and give greater weight to evidence which supports their existing views compared to evidence that contradicts them (see, for example, our September 2008 article on "Possible Implications of Some Trends that Cannot Continue"). More recent research has found that this bias seems to be more powerful when investors are incurring losses than when they are experiencing gains (see "Persistence of Beliefs in an Investment Experiment" by Ko and Hansch). And other research has found that experts seem to find it particularly difficult to change their views, which are often closely intertwined with their reputations and self-image (see Expert Political Judgment: How Good Is It? How Can We Know? By Philip Tetlock). To put it differently, the extent of social and emotional obstacles to changing a mental model seem strongly related to the extent to which others perceive you, and you perceive (or, perhaps more accurately, your need to perceive) yourself, as an expert in a given area. However, many clients surely entrusted professional managers with the stewardship of their assets because they expected them to be less prone to these biases, and more willing to relentlessly examine their assumptions and adjust them where necessary. Undoubtedly with the benefit of hindsight, many investors now say they saw the crisis coming. Yet if this is true, why did do few apparently act on their changing views to protect the value of their clients' portfolios? And why, instead of ad hominem dismissals, did we so few rigorous responses to the arguments presented by those offering warnings about the crisis that lay ahead?

These awkward questions leads to the third possibility: many professional investment managers had strong incentives to ignore the warnings that were offered, even if they believed them to be generally true. On the research side, Raghuran Rajan's 2005 paper was particularly prescient on this issue of misaligned incentives and the systemic risks they were creating (see "Has Financial Development Made the World Riskier?"). On the practical side, however, this problem (if not its eventual 2008 result) has been clear to many industry participants for a much longer period of time. For example, as a young banker in South America in 1981, I walked into our country manager's office with a group of my peers to ask why we were still making loans when we could very clearly see a balance of payments (and, given the currency mismatch, credit) crisis on the near horizon. He had a succinct answer that I never forgot: "I have a wife, three children and a mortgage; head office wants us to make loans to meet corporate earnings goals, and if I don't hit my lending targets I'll be replaced with someone who will." He told us to keep making loans, while sharply increasing the collateral we required. And then the LDC debt crisis made it clear that developed country governments thought some financial institutions were "too big to fail." It wasn't long after that when a friend who was a bond trader told me over drinks why he had the greatest job in the world: "I get to bet with the firm's money. If I'm right, I get rich, and if I'm wrong all I lose is my job." It was, he said, a "call option on wealth" (in an ironic and enlightening twist, he later found himself managing a trading floor filled with young people who thought like he once had). In the years since this insight was offered early in the 1980s, the incentives for people working in financial services have become substantially more lucrative (think 2% of the assets under management and 20% of annual profits), while the enormous wealth (and equally visible consumption) accumulated by successful financial services players only heightened the willingness of others to take greater risks (and look the other way more often at questionable thinking and behavior) in pursuit of their own pile of riches. Underneath it all remained the belief that some institutions were too big to fail, and that if times got tough the Federal Reserve would, once again, bail everyone out with rapid money supply growth and interest rate cuts, to get asset prices rising again. This was one lesson the financial services industry had learned all too well over the past quarter century. Technically, this is called "moral hazard" risk; however, that seems too bloodless a way to describe the much more corrosive process that has been undermining individual and collective self-control in the financial services industry over the past quarter century.

To be sure, there were regulatory attempts to limit the risks created by this system, such as the Basel II risk capital guidelines that were issued in 2004. Unfortunately, this included mandatory use (by usually underpaid and under-respected risk managers) of a common Value at Risk methodology, which suffered from two glaring faults. The first was widely remarked upon, but tolerated in the absence of an agreed upon alternative: VaR models' critical assumption about future volatility was based either upon historical data (which might not resemble the future) or option implied volatility (essentially, a weighted average of what other players were using, and which could be an equally inaccurate guide to the future). The second fault became clear in 2008: falling asset prices caused volatility to rise and VaR models to prescribe reducing the size of trading positions, which forced more asset sales and thereby accelerated the process underway. In the last analysis, however, regulations have never been the ultimate guarantor of systemic financial stability. Rather, that role has been played by the sense of stewardship and probity felt and displayed by the men and women who lead the financial services industry.

It is hard to say when this began to erode. Perhaps it was the revelations of the LDC debt crisis. Perhaps it was Salomon Brothers ceasing to be a partnership, and becoming a public company via its acquisition by Phibro in 1981. Perhaps it was the epic traders versus bankers battle in 1983 that saw Pete Peterson resign as Lehman Brothers CEO, leaving Lew Glucksman in control of the firm. Maybe it was the saga of Drexel, or when John Weinberg stepped down as senior partner of Goldman Sachs in 1990. Maybe it was when Jeff Vinik was driven from the helm of the Magellan Fund for his belief that prudence was the best course of action in the face of what he correctly identified as a growing bubble in technology stocks. Maybe it was the bailout of Long Term Capital Management in 1998. Or maybe it was the rise of private equity and hedge funds, with their supercharged incentives for delivering high returns. I honestly don't know when the tipping point was reached - but I do know that when Goldman went public in mid-1999, we had reached the end of an era.

Sadly, the results are all too easy to see, from the internet bubble (and its subsequent revelations about the unethical and occasionally illegal behavior of too many financial services professionals), to the housing bubble, to what may yet turn out to have been a private equity bubble, and to the growing number of hedge fund managers who, with results well below their high watermarks (which must be made up before their 20% of the profits kicks in again), are choosing to close their funds instead of trying to earn back the money their investors have lost. Once again, there are exceptions - not everyone in the financial services business has behaved badly (people like Jack Bogle and many fee-based investment advisers come to mind). Yet it remains depressingly clear that, even after necessary changes to incentives are made, it will be a long-time before the financial services industry regains the trust and respect of clients who now feel acutely betrayed. I'm not sure where the new leaders will come from, but it is apparent they will be desperately needed in what is sure to be a radically changed environment for financial services companies.

So what are we to conclude? A lot of smart people on the buy-side undoubtedly got a very nasty surprise in 2008. In some cases, it was because they weren't paying attention. In more cases, it was due to a flawed mental model of how the economy and financial markets work, and either an inability to learn or a mistaken belief that, as Keynes once put it, they could "beat the gun." But in too many other cases, particularly on the sell-side, I suspect there was precious little surprise, and instead a cynical satisfaction that their bet that the government would ultimately save them from the full consequences of their self-destructive urges has once again paid off. As Walter Bagehot wrote so long ago, in great financial crises, "avaricious people get hurt, but it is in the nature of crashes that they are not the ones who get hurt most." Then again, that may not be true this time around. On December 1, 2008, in the Federal District Court, Judge Mariana Pfaelzer rejected a motion by Countrywide Financial executives to dismiss a lawsuit brought against them alleging they violated securities laws in connection with their actions related to the subprime loan crisis. The judge's opinion noted "a complex series of misrepresentations and omissions over a long period of time" by the Countrywide defendants, and that "Countrywide's practices so departed from its public statements that even [vague] terms like 'high quality' [which are normally not legally actionable] became materially false and misleading." And if financial executives are found to be party to a fraudulent scheme, their earnings from the scheme (i.e., past bonuses) could be subject to forfeiture. In short, the Countrywide litigation bears watching, because if the plaintiffs there win their case, many other financial services executives could find themselves the target of similar lawsuits.

How Will the Industry Adapt?

In her research, Anne Marie Grisogono has found that successful organizations adapt on five different levels. The financial services industry should be no exception to this rule, so we will use her framework to offer our conjectures for what lies ahead.

The first level of adaptation is finding better ways to execute the current strategy. At this level, we would expect to see greater emphasis on maintaining adequate liquidity reserves, the use of asset allocations that more explicitly include positions to hedge against adverse states (e.g., use of government bonds, timber and volatility that perform relatively well in a high uncertainty state, and property, real return bonds and commodities to hedge against inflation), greater use of regime switching models to support these allocations, and more attention given to rebalancing, both on an automatic basis and when normal asset class valuation ranges are significantly exceeded. We also believe that the value of adding even high quality non-government fixed income instruments to portfolios will increasingly be called into question. As we have noted in the past, we have not included high yield or emerging market bonds in our model portfolios because of their high correlation with equity returns. To put it differently, they all seemed to have similar return generating processes and downside risks, while one (equity) offered significantly greater upside returns. Today, similar questions can be asked about many investment grade corporate credits. Over the long term, these investor concerns may force a change in the ways corporations finance themselves, with greater reliance on equity rather than debt, at the cost of potentially lower, yet more stable returns.

We also believe that the 2008 crisis will cause many more investors to agree with us on the importance of making distinctions between alleged "alpha generating" active strategies that are based on simply adding leverage, earning insurance fees (from selling out of the money puts) or holding illiquid assets, and alpha that is based on true skill in identifying undervalued assets and/or forecasting the future behavior of other investors. Finally, in the wake not just of the Madoff fraud, but also the failure of so many expensive strategies to protect against downside risk, we expect that advisers will place renewed emphasis on understanding the return generating process that underlies the claims made by active managers. In particular, any promise of higher returns with lower risk will seem more suspect than before, and more advisers will demand evidence of the consistent investor valuation errors and persistent barriers to arbitrage (and copying the strategy) upon which such claims must rest.

The second level of adaptation involves the use of different approaches, rather than modifications of the current strategy or plan. This is the level at which we expect to see the most far reaching changes. This includes more attention being given to clarification of long-term goals and the establishment of a sequence of objectives to achieve them. For example, many people will need to reconfigure their trade-offs between time to retirement, savings levels, target retirement incomes and bequests, and acceptable levels of investment uncertainty. It seems inevitable, that, faced with these challenges, professional financial advisers will have to integrate career and housing decisions into their objectives in a more explicit manner than they have before. And given the large roles that consumption and social comparison have played in many clients' lives over the past twenty years, advisers will have to become comfortable with and effective in discussing painful changes in these areas with their clients.

It also seems clear that the events of 2008 will accelerate the paradigm shift from the efficient markets to the adaptive markets view of the world. This is likely to have more far reaching effects than most people realize. As Brian Arthur points out in his excellent overview of this subject ("Out of Equilibrium Economics and Agent Based Modeling"), the Adaptive Markets Hypothesis is part of a larger shift from looking at systems in equilibrium to looking at systems that are out of equilibrium, in which "standard equilibrium behavior becomes a special case. It follows that out-of-equilibrium economics is not in competition with equilibrium theory; it is merely economics done in a more general, generative way...If heterogenous agents (or strategies) adjust continually to the overall situation they together create, then they change that ecology...Because out-of-equilibrium economics is by its nature evolutionary, it resembles modern evolutionary biology more than it does 19th century physics" [which, with its constant behavioral laws and highly mathematical proofs, has become the intellectual model for equilibrium economics].

One consequence of moving from the Efficient to the Adaptive Markets Hypothesis is a shift from a risk-based view of the world to one that explicitly incorporates uncertainty. This is important, because the theoretical basis for decision making in the face of uncertainty is far less developed than it is for decision making in the face of risk (see, "Probability and Uncertainty in Economic Modeling" by Gilboa, Postelwaite, and Schmeidler; "Investing in the Unknown and Unknowable" by Richard Zeckhauser; "Knightian Decision Theory" by Truman Brewley; and "Reflections on Decision Making under Uncertainty" by Paul Kleindorfer). For example, the last paper notes one study which found people much more willing to pay to avoid uncertainty when they knew they would have to later explain their decision to a group. Brewley finds that inertia plays an important role in decision making under uncertainty, as it causes people to avoid acting unless doing so seems preferable to the status quo under a wide range of assumptions about the future. Yet another study found that investors using fundamental value based strategies are less uncertainty-averse than those using momentum based strategies that are based on the anticipated reactions of others (see "Uncertainty Aversion in an Agent-Based Model of Foreign Exchange Rate Formation" by Kozhan and Salmon). Clearly, there is still much to learn in this area.

Closely related to this is the issue of loss aversion. Recent neurobiology research has found that loss aversion has very deep roots in our evolutionary past and is "hardwired" into our cognitive and emotional processing systems (see "Neural Correlates of Adaptive Decision Making for Risky Gains and Losses" by Weller, Levin, Shiv and Bechara and "On the Evolutionary Origin of Prospect Theory" by McDermott, Fowler and Smirnov). This aligns with research by Paul Slovic on the way people categorize hazards. He and his colleagues found that what they termed "risk perception" was driven by two factors: (a) "Dread Risk", which was related to lack of control and potential for catastrophic and unequally distributed consequences, and (2) "Unknown Risk", which captured the extent to which hazards were new, observable, and understood. Loss aversion is a critical issue because other researchers have found that it has a significant impact on asset prices and volatility (see "An Agent Based Approach to Financial Stylized Facts" by Shimokawa, Suzuki and Misawa - which finds the impact of loss aversion is magnified by a lack of liquidity - and "From Boom 'til Bust: How Loss Aversion Affects Asset Prices" by Berkelaar and Kouwenberg). We expect that increased recognition of the importance of loss aversion will lead to a shift away from the use of the variance/standard deviation of returns as a proxy for risk, and towards the use of various downside risk measures.

We also expect that recent events will lead to a much greater emphasis on incorporating information/uncertainty and liquidity risk into asset pricing and risk management (e.g., Value at Risk) models. Clearly, recent events have heightened investor awareness of the potential severity of these risks, which should inevitably lead to their demanding higher compensation for bearing it. Some good work has been done in this area in the past (see, for example, "Asset Pricing with Liquidity Risk" by Acharya and Pedersen; "The Time Varying Liquidity Premium: Speculator Hesitation in Liquidity Shocks" by Peter Blaustein -- which finds the small company premium is highly correlated with the liquidity premium; and "Ambiguity, Information Quality and Asset Pricing" by Epstein and Schneider), and more is now being published (e.g., "Informtion, Liquidity and Asset Prices" by Lester, Postelwaite and Wright; "Moral Hazard, Collateral and Liquidity" by Acharya and Viswanathan, "Cross Section of Stock Returns in the U.K. Market: the Role of Liquidity Risk" by Hwang and Lu, and "Liquidity and Valuation in an Uncertain World" by Easley and O'Hara, which draws on Trewleys work noted above to explain the disappearance of bond market liquidity in September 2008). However, a definitive work that integrates liquidity into an asset pricing and risk management model that applies to multiple asset classes has yet to be written.

As the Adaptive Markets Hypothesis becomes more widely accepted, we will see more research into two areas: pattern recognition and, at a more fundamental level, agent based modeling. While consistently accurate prediction of events in complex adaptive systems (i.e., empirical time series analysis) is extremely difficult (due to the complex, non-linear, and evolving relationships between causes and effects), it remains possible to gain a so-called "coarse grained understanding" of their main dynamics, and to recognize the recurring patterns in higher level outcomes that they tend to produce. Given this, successful investment in an adaptive market may owe more to superior pattern recognition skills than to superior forecasting abilities. For example, recent research has found that value and momentum effects exist in multiple asset classes, and across time as well (see "Global Tactical Cross-Asset Allocation: Applying Value and Momentum Across Asset Classes" by Blitz and van Vliet, "Value and Momentum Everywhere" by Asness, Moskowitz, and Pedersen, and "Price Momentum in Stocks: Insights from Victorian Age Data" by Chabot, Ghysels and Janagnnathan). Other research has found that markets dynamics become more stable as the percentage of fundamental value oriented investors goes up, and less stable as the percentage of momentum traders rises. Taking another approach, Didier Sornette and other "econophysics" researchers have found that changes in market returns follow a power law, with large changes that relieve a substantial amount of system stress preceded by a larger number of smaller ones as that stress accumulates, much as earthquakes are often preceded by smaller tremors.

The underlying cause of these patterns is changes in the strategies employed by investors, and in their interactions with each other. Developing realistic agent based models that involve investor learning and interaction over time, and which can reproduce observed economic and financial market patterns is the holy grail of the adaptive markets research program (see J. Doyne Farmer's paper "Toward Agent Based Models for Investment" and "Statistical Physics of Social Dynamics" by Castellano, Fortunato, and Loreto). The military (and its collaborators in the commercial gaming industry) has been at the forefront of agent based modeling research, and in particular efforts to incorporate emotional, social and learning factors into agent behaviors. For example, "Close Combat: Modern Tactics" is a commercial version of a U.S. Marine Corps squad leader training simulation that includes realistic modeling of fighters' physical and emotional states, experience and unit cohesion. Earlier this year, the U.S. National Research Council published an overview of the state of this research program (Behavioral Modeling and Simulation: From Individuals to Societies by Zacharias, MacMillan and Van Hemel). The authors note that "unrealistic expectations are often based on a misconception about what sort of prediction a human behavior model can actually produce. In most situations of interest, there is a range of plausible behaviors, and within the same situation, different people will behave differently, and the same person may behave differently at different times. Rather than generating a single definitive prediction of behavior, a good human behavior model will instead identify a space of possible outcomes, given probability assessments for these behaviors, and specify some of the factors that could alter these probability assessments...[Hence] the value of these models should be measured in terms of the reduction in uncertainty they achieve."

An excellent recent example of where this line of research is headed in the financial area is found in a recent paper by Harras and Sornette ("Endogenous versus Exogenous Origins of Financial Rallies and Crashes in an Agent-Based Model with Bayesian Learning and Imitation"). Their model's agents "form opinions and invest, based on three sources of information: (1) public information, i.e. news; (2) information from their friendship network, promoting imitation; and (3) private information. Agents use Bayesian learning to adapt their strategy according to the past relevance of the three sources of information." Simulations with their model show that "rallies and crashes occur as amplifications of random lucky or unlucky streaks of news" which generate superior performance for some agents that triggers "collective transient herding regimes...A positive feedback loop is created by two dominating mechanisms, Bayesian learning and social imitation, which, by reinforcing each other, result in rallies and crashes."

Relatively speaking, this research program is still in its infancy; however, it has the potential to produce some real breakthroughs in the future. One example of this would be a better understanding of the different factors that underlie the return generating processes for different asset classes. Ideally, an investor would like to be able to diversity his or her portfolio across these factor exposures; in practice, however, this is still extremely difficult to accomplish. While principal component analysis can identify independent statistical factors driving asset class returns, it cannot tell us what these factors correspond to in the real world - e.g., Industrial production? Exchange rates? Price levels? Productivity changes? The percentage of assets being managed using momentum strategies? The level of uncertainty among investors who play central roles in large social networks? Those are questions that agent based modeling may one day help us to answer. In the meantime, we expect to see far greater use of alternatives to traditional mean/variance optimization analysis in the construction of investor portfolios, with its unrealistic assumption of normal distributions of asset returns that can be forecast with minimal uncertainty (and equally unrealistic assumption of quadratic investor utility, for more technically minded readers). Instead of MVO, we expect to see wider adoption of the approach we have been using for years, with greater focus on regime switching models (that produce more realistic return distributions), integrated rebalancing strategies, and stochastic search techniques to identify "robust" asset allocation solutions intended to achieve minimum investor objectives over a wide range of possible circumstances. We also expect to see greater recognition that, in a complex adaptive market, extreme overvaluations are possible, and for that reason asset allocation and portfolio management must be on guard against them and the large downside returns they can cause when bubbles pop. To put it differently, we expect more people to recognize that asset allocation will always be as much of an art as a science.

Regardless of the underlying theories upon which they are based, another adaptation we expect to see is greater emphasis on rigorously assessing the quality of the models that underlie much of modern finance. Specifically, we expect to see a migration into financial services of a body of work that has recently emerged from the U.S. nuclear weapons laboratories, and their work on the validation and verification of complex models used to test new bomb designs. "The Good, The Bad, and the Ugly of Predictive Science" by Hemez and Ben-Haim provides an excellent (and minimally quantitative) introduction to this research, and explains the inescapable trade-off between three conceptions of a "good model." The first is fidelity to data. This matters, "because no analyst will trust a numerical simulation that does not reproduce the measurements of past experiments or the information contained in historical databases." The second is robustness to uncertainty, or the range of different settings for a model that produce no more than a given level of prediction error. A model's "robustness to uncertainty minimizes the vulnerability of decisions to uncertainty and lack of knowledge." In our work, a good example of this is our use of broadly defined asset classes, which have relatively low correlations of returns with each other. This results in our asset allocation solutions being relatively robust to significant errors in our estimates of future rates of return and volatility of the asset classes we use. The third conception of a good model is termed "confidence in prediction" by Hemez and Ben-Haim. It is a function of the range of predictions for a given set of outputs made by different models. "To have confidence in predictions, there should be as much consistency as possible between the predictions provided by equally credible [models]...from expert judgment to high-fidelity simulations." Unfortunately, it is not possible to have a model that simultaneously presents high fidelity to data, robustness to uncertainty, and confidence in prediction. The authors show how this trade-off is caused by "robustness to uncertainty decreasing as fidelity to data improves; confidence in prediction increasing as robustness improves; and an improvement in fidelity reducing confidence in prediction."

Grisogono's third level of adaptation is "learning to learn." In addition to an increased openness to the Adaptive Markets Hypothesis and new approaches like agent based modeling, we also expect a heightened focus on competitive analysis, questioning the conventional wisdom, and more closely examining outcomes that substantially differ from expectations. In sum, we expect the financial services industry to adopt many of lessons learned by intelligence agencies and the military in the wake of surprises they have experienced in the past.

The fourth level of adaptation - what Grisogono terms "fitness measures" is another area where we expect to see major changes occur. Some of these changes will be imposed by regulators (e.g., limits on leverage, increased disclosure, and more severe penalties for failing to carry out fiduciary responsibilities) and others by shareholders and investors, who will probably focus on realigning compensation incentives. We also expect to see the trend towards "liability driven investing" gain more momentum, as more investors realize that funding their long term goals is what counts, and not simply having their manager achieve higher annual returns than an index or a peer group. In turn, this should strengthen the focus on managing downside risk and creating robust strategies. However, we don't think the move toward the Adaptive Markets Hypothesis will dim the potential attraction of uncorrelated alpha delivered by active management. In fact, the AMH may actually make some active managers' lives easier, as there is growing evidence that human beings have widely varying skill when it comes to effectively operating in a complex adaptive environment, and a rare few seem particularly talented in this area (see, for example, The Logic of Failure by Dietrich Dorner, which has inspired something of a cottage industry among German researchers in the study of this issue).

On the other hand, the events of 2008 will surely accelerate the move away from long-only active funds that provide a high cost mix of passive and active exposures, and a more jaundiced view of active strategies whose "alpha" is due to leverage, insurance premiums or liquidity risk, rather than investment management skill. To put it differently, when making decisions in the face of uncertainty, there is an unavoidable trade-off between so-called Type 1 errors (rejecting a true hypothesis, such as "this investment will generate alpha") and Type 2 errors (accepting a false hypothesis, such as "Bernie Madoff is a great active manager"). In recent years, the incentives facing asset managers seem to have been strongly oriented toward minimizing the chances of making Type 1 errors (i.e., rejecting investments in successful actively managed strategies). However, that has inescapably come at the price of accepting a very high probability of making Type 2 errors, and investing in too many active strategies that failed to deliver their anticipated results. The financial effects of this trade-off are today painfully visible in the carnage in the hedge fund sector and many investor portfolios. In the future, we expect a realignment of incentives that leads to a better balance between the chances of making Type 1 and Type 2 errors.

Grisgono's fifth level of adaptation is "Co-Adaptation", which she defines as "consciously tuning our interaction with those other systems connected with our own organization." Clearly, this will happen with respect to regulators. However, for professional advisers, it may also happen in their relationships with clients, where effective financial strategies - which have now become more critical - will likely require more coordination with a wider range of professionals and organizations, and better information technology to facilitate these interactions.

In sum, we expect the painful events of 2008 to catalyze a large number of changes in the asset management industry, which will ultimately improve its intellectual, ethical, and behavioral underpinnings and in so doing result in much more value being delivered to its clients.

| 2008 Year End Double Issue: Key Points | This Month's Letters to the Editor: Commodies: Supply, Demand and Equilibrium; Construct of DJAIG; Benefits of ENM in Model Portfolios; Liquidity Reserves; and the Purpose of our Monthly Asset Valuation Update | Global Asset Class Returns | Asset Class Valuation Update | What Will We Tell The Clients? | 2008 Year End Situation and Methodology Update | Product and Strategy Notes: How to Deal with Real Debt Burden; Why He Madoff with Their Money; Great Writing Not to be Missed; Interesting Data Returns; Thought Provoking Research; and New Products | 2007-2008 Benchmark Portfolios - All Currencies |