Emotions play a major role in financial markets, leading to mispricing of risk.
Psychoanalyst David Tuckett conducted in 2007 detailed research interviews with 52 experienced asset managers, working in financial centres around the globe [108].
The interviewed managers collectively controlled assets of more than $500 billion in value. Tucker points out that it was immediately clear to him that financial assets are fundamentally different from ordinary goods and services (p. xvi).
Financial assets, he argues, are abstract objects whose values are largely dependent
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on expectations of traders of their future values (pp. 20–25). This created an environment for decision-making that is completely different from that in other markets. It was an environment in which there is both inherent uncertainty and inherent emotional conflict (p. 19). Traders’ behaviour therefore is predominantly influenced by emotions and imaginations. These emotions are continuously in flux, which adds to the volatility of markets. Traders seek protection by developing groupfeeland shared positions (p. 173). This explains the well-documented herd behaviour in financial markets that plays a major role in contagion and instability [49, 93, 98]. In short, logical indeterminacy arising from self-reference makes emotions and herding dominate financial markets, causing higher volatility and instability that cannot be accounted for by formal models.
Coleman [32, p. 202] warns that “The real risk to an organization is in the unanticipated or unexpected–exactly what quantitative measures capture least well”.
Quantitative tools alone, Coleman explains, are no substitute for judgment, wisdom, and knowledge (p. 206); with any risk measure, one must use caution, applying judgement and common sense (p. 137). David Einhorn, founder of a prominent hedge fund, wrote that VaR was “relatively useless as a risk-management tool and potentially catastrophic when its use creates a false sense of security among senior managers and watchdogs” (cited in Nocera [79]).
Overall, ignoring the limits of quantitative measures of risk is dangerous, as emphasized by Sharpe and Bernstein [10,11], among others [74]. This leads to the principle of Peter Bernstein: a shift away from risk measurement to risk manage- ment [16]. Risk management requires principles, rules, policies, and procedures that guide and organize financial activities. This is elaborated further in the following section.
7 Applications, III: Dealing with Uncertainty
If uncertainty is irreducible and fundamentally unquantifiable, even statistically, what to do about it? In an uncertain environment, the relevant strategy is one that does not rely on predicting the future. “Non-predictive strategies” refer to strategies that are not dependent on the exact course the future takes. Such strategies call for co-creating the future rather than trying to predict it and then act accordingly [105,113]. Non-predictive strategies might imply following a predefined set of rules to guide behaviour without being dependent on predicting the future.
Heiner [58] argues that genuine uncertainty arises due to the gap between agents’ competency and environment’s complexity. In a complex environment, agents overcome this gap by following simple rules (or “coarse behavior rule”;
see Bookstaber and Langsam [18]) to reduce the complexity they face. As the gap between agents’ competency and environment’s complexity widens, agents tend to adhere to more rigid and less flexible rules, so that their behaviour becomes stable and more predictable. This can be verified by comparing humans with different kinds of animals: as the competency of an animal becomes more limited, its
Is Risk Quantifiable? 297 behaviour becomes more predictable. Thus, the larger the gap between competency and complexity, the less flexible will be animal’s behaviour, and thus the more predictable it becomes. The theorems of Gửdel and Turing show that the gap between agents’ competency and environment’s complexity can never be closed;
in fact, the gap in some respects is infinite. The surprising result of this analysis, as Heiner [58, p. 571] points out, is that:
. . . genuine uncertainty, far from being un–analyzable or irrelevant to understanding behav- ior, is the very source of the empirical regularities that we have sought to explain by excluding such uncertainty. This means that the conceptual basis for most of our existing models is seriously flawed.
North [80] capitalizes on Heiner’s work, and argues that institutions, as con- straints on behaviour, develop to reduce uncertainty and improve coordination among agents in complex environments. Institutions provide stable structure to every day life that guide human interactions. Irreducible uncertainty therefore induces predictable behaviour as people adopt rules, norms and conventions to minimize irreducible uncertainty (see Rosser [90]).
Former Federal Reserve chairman, Ben Bernanke, makes the same point with respect to policy (cited in Kirman [61]):
. . . it is not realistic to think that human beings can fully anticipate all possible interactions and complex developments. The best approach for dealing with this uncertainty is to make sure that the system is fundamentally resilient and that we have as many failsafes and back- up arrangements as possible.
The same point is emphasized by former governor of Bank of England, King [60, p. 120]: “rules of thumb – technically known as heuristics – are better seen as rational ways to cope with an unknowable future”.
Constraints in principle could help in transforming an undecidable problem into a decidable one. Bergstra and Middleburg [8, p. 179] argue that it is common practice in computer science to impose restrictions on design space to gain advantages and flexibility. They point out that:
In computer architecture, the limitation of instruction sets has been a significant help for developing faster machines using RISC (Reduced Instruction Set Computing) architectures.
Fast programming, as opposed to fast execution of programs, is often done by means of scripting languages which lack the expressive power of full-blown program notations.
Replacing predicate logic by propositional calculus has made many formalizations decid- able and for that reason implementable and the resulting computational complexity has been proved to be manageable in practice on many occasions. New banking regulations in con- ventional finance resulting from the financial crisis 2008/2009 have similar characteristics.
By making the financial system less expressive, it may become more stable and on the long run more effective. Indeed, it seems to be intrinsic to conventional finance that seemingly artificial restrictions are a necessity for its proper functioning.
Another approach to avoid undecidability is to rely more onqualitativerather thanquantitativemeasures. This can be viewed as another kind of restrictions that aim to limit uncertainty. According to Barrow [4, pp. 222, 227], if we have a logical theory that deals with numbers using only “greater than” or “less than”, without referring to absolute numbers (e.g. Presburger Arithmetic), the theory would be
298 S. Al-Suwailem et al.
complete. So, if we restrict our models to qualitative properties, we might face less uncertainty. Feyerabend [43, pp. xx, 34] points out that scientific approach does not necessarily require quantification. Quantification works in some cases, fails in others; for example, it ran into difficulties in one of the apparently most quantitative of all sciences, celestial mechanics, and was replaced by qualitative considerations.
Frydman and Goldberg [47, 48] develop a qualitative approach to economics,
“Imperfect Knowledge Economics”, which emphasizes non-routine change and inherently imperfect knowledge as the foundations of economic modelling. The approach rejects quantitative predictions and aims only for qualitative predictions of market outcomes. Historically, several leading economists, including J.M. Keynes, were sceptical of quantitative predictions of economic phenomena (see Blaug [13, pp. 71–79]).
Certain restrictions therefore are needed to limit uncertainty. The fathers of free market economy, Adam Smith, John Stuart Mill and Alfred Marshall, all realized that banking and finance needs to be regulated in contrast to markets of the real economy [27, pp. 35–36, 163].
In fact, the financial sector usually is among the most heavily regulated sectors in the economy, as Mishkin [78, pp. 42–46] points out. Historically, financial crises are associated with deregulation and liberalization of financial markets. Tight regulation of the banking sector following the Word War II suppressed banking crises almost completely during 1950s and 1960s [21]. According to Reinhart and Rogoff [87], there were only 31 banking crises worldwide during the period 1930–
1969, but about 167 during the period 1970–2007. The authors argue that financial liberalization has been clearly associated with financial crises.
Deregulation has been visibly clear in the years leading to the Global Financial Crisis. The famous Glass-Steagal Act has been effectively repealed in 1999 by the Gramm-Leach-Bliley Act. Derivatives were exempted from gaming (gambling) regulations in 2000 by the Commodity Futures Modernization Act (see Marks [75]).
Within a few years, the world witnessed “the largest credit bubble in history”, as Krugman [65] describes it.
Sornette and Cauwels [99] argue that deregulation that started (in the US) approximately 30 years ago marks a change of regime from one where growth is based on productivity gains to one where growth is based on debt explosion and financial gains. The authors call such regime “perpetual money machine” system, that was consistently accompanied by bubbles and crashes. “We need to go back to a financial system and debt levels that are in balance with the real economy”
(p. 23).
In summary, the above discussion shows the need and rationale in principle for institutional constraints of the financial sector. The overall objective is to tightly link financial activities with real, productive activities. Regulations, as such, might not be very helpful. Rather than putting too much emphasis on regulations per se, more attention should be directed towards good governance and values that build trust and integrity needed to safeguard the system (see Mainelli and Giffords [74]).
Is Risk Quantifiable? 299
8 Conclusion
In his lecture at Trinity University in 2001 celebrating his Nobel prize, Robert Lucas [23] recalls:
I loved the [Samuelson’s]Foundations. Like so many others in my cohort, I internalized its view that if I couldn’t formulate a problem in economic theory mathematically, I didn’t know what I was doing. I came to the position that mathematical analysis is not one of many ways of doing economic theory: It is the only way. Economic theoryismathematical analysis. Everything else is just pictures and talk.
The basic message of this chapter is that mathematics cannot be theonlyway. Our arguments are based on mathematical theorems established over the last century.
Mathematics is certainly of a great value to the progress of science and accumulation of knowledge. But, for natural and social sciences, mathematics is a tool; it is “a good servant but a bad master”, as Harcourt [56, p. 70] points out. The master should be the wisdom that integrates logic, intuition, emotions and values, to guide decision and behaviour to achieve common good. The Global Financial Crisis shows how overconfidence in mathematical models, combined with greed and lack of principles, can have devastating consequences to the economy and the society as a whole.
Uncertainty is unavoidable, not even statistically. Risk, therefore, is not in principle quantifiable. This would have a substantial impact not only on how to formulate economic theories, but also on how to conduct business and to finance enterprises. This chapter has been an attempt to highlight the limits of reason, and how such limits affect our abilities to predict the future and to quantify risk. It is hoped that this contributes to a better reform of economics and, subsequently, to better welfare of the society.
Acknowledgements We are grateful to the editors and an anonymous referee for constructive comments and suggestions that greatly improved the readability of this text. FAD: wishes to acknowledge research grant no. 4339819902073398 from CNPq/Brazil, and the support of the Production Engineering Program,COPPE/UFRJ, Brazil. SA: wishes to acknowledge the valuable discussions with the co-authors, particularly FAD. The views expressed in this chapter do not necessarily represent the views of the Islamic Development Bank Group.
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