PART A : ARTICLE
ADAPTIVE MARKETS
In his seminal book Adaptive Markets, Andrew Lo says markets are efficient under certain conditions and inefficient under other conditions. He argues that we need a more complete framework for thinking about financial markets that reflects the fear factor as well as rational behaviour. As he says, "In the same way that no blind monk is able to figure out the elephant by himself, we need to piece together insights from multiple disciplines to get the full panoramic picture of how financial markets work and why they fail." The book Adaptive Markets, a tour de force by one of the most brilliant financial economists of our times, provides that panoramic view.
Andrew Lo believes that we need a new way of thinking about financial markets and human behaviour. He calls it the Adaptive Markets Hypothesis. As he says, "The term 'adaptive markets' refers to the multiple roles that evolution plays in shaping human behavior and financial markets, and 'hypothesis' is meant to connect and contrast this framework with the Efficient Market Hypothesis, the theory adopted by the investment industry and most finance academics."
The Adaptive Markets Hypothesis is based on the assumption that investors and markets behave more like a biological system, comprising a population of living organisms competing to survive, and less like a physical system comprising of inanimate objects subject to immutable laws of motion. This basic insight has far-reaching implications. It implies that the principles of evolution (competition, innovation, adaptation, and reproduction) explain better the workings of the financial industry than the physics-inspired principles of rational economic analysis.
As Lo says, "It implies that changing business conditions and adaptive responses are often more important drivers of investor behavior and market dynamics than enlightened self-interest – the wisdom of crowds is sometimes overwhelmed by the madness of mobs." The oscillation of financial markets between wisdom and madness is not a pathology. It is just a reflection of human nature.
Financial markets, a product of human evolution, follow biological laws instead of economic laws. As Lo says, "The same basic principles of mutation, competition, and natural selection that determine the life history of a herd of antelope also apply to the banking industry, albeit with somewhat different population dynamics."
The book is organized into twelve chapters and each chapter is replete with novel insights and perspectives. What follows is a very succinct presentation of its key insights and perspectives:
Italian mathematician Girolama Cardano offered some sound advice on speculation in 1565: "The most fundamental principle in all gambling is simply equal conditions, e.g., of opponents, of bystanders, of money, of situation, of the dice box, and of the die itself. To the extent to which you depart from that equality if it is in your opponent's favour, you are a fool, and if it in your own, you are unjust." This notion of a "fair game" came to be known as martingale. The outcome of a martingale will be a random pattern of losses and gains. Since the random price movements in a market were martingales, Louis Bachelier, a French mathematician concluded, "the mathematical expectation of the speculation was zero."
Louis Bachelier proposed the notion of a "fair game" or a random walk. While Bachelier explained the how of the random walk model, Paul Samuelson explained why market price movement was a random walk in a seminal 1965 article that summarizes his main idea: "Proof that Properly Anticipated Prices Fluctuate Randomly." Today it is better known as the Efficient Markets Hypothesis.
Almost simultaneously, Eugene Fama developed the Efficient Markets Hypothesis. He wrote in 1965, "In an efficient market, on the average, competition will cause the full effects of new information on intrinsic values to be reflected 'instantaneously' in actual prices." This was like a bombshell dropped on Wall Street. As Lo says, "In one fell swoop, Fama dismissed the work of Wall Street's technical analysts, fundamental analysts, proprietary traders, and hedge fund managers as a complete waste of time."
Paul Samuelson and Eugene Fama, two economists with very dissimilar styles of thought, reached the same conclusion. As Lo says, "Fama's fascination with computers, data, and statistical analysis led him down a very different path to the Efficient Markets Hypothesis than Samuelson's elegant, simple-minded, physics-inspired version." He adds, "The Efficient Markets Hypothesis follows a simple chain of economic logic to its counterintuitive conclusion. Cardano's martingale, Bachelier's random walk, Samuelson's proof, and Fama's statistics all lead to the same place: prices must reflect all available information."
Harry Markowitz's optimal portfolio theory, William Sharpe's Capital Asset Pricing Model, Eugene Fama and Samuelson's Efficient Markets Theory, and Fischer Black, Myron Scholes, and Robert C. Merton's option pricing formula were part of a new quantitative movement in finance. These discoveries which appeared in a relatively short span of time illuminated aspects of market behaviour that remained a mystery for centuries.
Taken to an extreme, any virtue can become a vice. The mathematization of finance, based on the model of physics, was no exception. As Lo said, "Finance isn't physics, despite the similarities between the physics of heat conduction and the mathematics of derivative securities, for example. The difference is human behavior and the role of evolution in its development." As Richard Feynman observed, "Imagine how much harder physics would be if electrons had feelings!"
Warren Buffett referred to derivatives as "financial weapons of mass destruction." However, this metaphor can be turned on its head. As Lo said, "The same science that gave us actual weapons of mass destruction, nuclear physics, is also responsible for many positive discoveries, such as nuclear power, MRI, and anticancer radiation treatment." So, in the financial world just as in nuclear physics, how powerful technologies are deployed makes all the difference. So we need Adaptive Markets Hypothesis which provides a new narrative that helps to make sense of the wisdom of crowds, the madness of mobs, and evolution at the speed of thought.
Frank Knight made an important distinction between risk and uncertainty. Risk refers to a situation where the possible outcomes and the probabilities associated with them are known fairly objectively whereas uncertainty refers to a situation where the possible outcomes are not known, let alone the probabilities associated with them. While people have no problem in taking risks in their day-to-day activities, they become more cautious and conservative in face of uncertainty. As Andrew Lo put it, "Fear of the unknown is one of the most potent kinds of fear there is, and the natural reaction is to get as far from it as possible. This may not be mathematically correct but it's hardwired human nature."
In industries with Knightian risk the random element can be measured. So forces of competition eventually eliminate excess profits and the business becomes commoditized. However, in industries characterized by Knightian uncertainty (unproven technologies), randomness cannot be measured. Hence commoditization is not possible. Given the unknown most people shun the game. But these are also the circumstances where exceptional rewards are reaped (as happened to Mark Zuckerberg of Facebook). As the adage goes, fortune favours the brave.
Humans are predictions machines. Planning ahead and forward-looking behaviour are the most powerful human abilities and the primary reason for the domination of the planet by Homo Sapiens. Many behavioural biases stem from our natural tendency to forecast and plan ahead but applied to the wrong environment. The human propensity to predict everything is both a blessing and curse. It appears that human cognition isn't adapted to make probabilistic inferences.
The orthodox economic approach assumes that people behave like a perfectly rational Homo economicus. Since these assumptions are contrary to our subjective experience, it is surprising that economics can model human behaviour. But as Lo says, "The miraculous thing about economics is, most of the time, these assumptions do explain most economic behavior reasonably well. In fact, they capture human behavior that most economists instinctively reach for explanations that use these orthodox assumptions over ones that don't."
Behaviouralists are still outnumbered by the votaries of the efficient market hypothesis. Why? Because there is no compelling alternative to the EMH as of now. After all, it takes a theory to beat a theory. J.M. Keynes could only explain human behaviour by invoking "animal spirits" which he described as "a spontaneous urge to action rather than inaction and not as the outcome of weighted average of quantitative benefits multiplied by quantitative probabilities." The term "animal spirits" is an evocative metaphor. However, as an explanation it is nonscientific and not a compelling alternative to homo economicus.
Psychology is based on empirical observation and clinical practice. So psychologists do not feel the need to integrate all their theories into a single, unified, and mutually consistent framework. Economists, on the other hand, pride themselves in their ability to explain a broad range of phenomena using a single, self-consistent, mathematically rigorous framework.
The brain has only one way to feel pain and fear but seems to have a "reward system" with many different pathways. Interestingly, different rewards such as food, money, music, sex, and love involve the same neurochemical, viz., dopamine. According to neuroanatomists, there are eight different dopamine pathways in the brain including those associated with complicated behaviours such as learning and attention. As Andrew Lo observed, "It is tempting to speculate that the multiplicity of uses and pathways of dopamine in the brain reflects the many ways we feel pleasure, while we have only one way to feel fear."
A monetary reward stimulates the ventral tegmental area, which releases dopamine in the reward system and the nucleus accumbens. As Andrew Lo says, "In the case of cocaine we call it addiction. In the case of money we call it capitalism. Our most fundamental reaction to monetary gain is hardwired in human physiology. Apparently we know it instinctively: greed is good."
While all traders react to significant market moves, less experienced traders are physiologically more sensitive to short-term market movements. As Lo said, "Moreover, in the aftermath of these extreme market moves, the emotional arousal of more experienced traders quickly returned to normal, whereas the less experienced traders showed higher levels of emotional arousal that lasted much longer." Traders who experience more intense reactions to both losses and gains perform significantly worse than others. Further, those who score higher on a measure of "internality" (the tendency to believe that various events in their lives are caused by their own doing rather than random chance) perform much worse than those who score lower on this scale. Good traders have more controlled emotional responses and an ability to refrain from blaming (or lauding) oneself too much for the outcomes of their trading decisions.
Why do people have difficulty in being rational about money? It is because the brain applies the same neural circuitry of fear and greed to financial experiences as it does to everything else. As Andrew Lo says, "Mother nature, that great economizer, often reuses an existing biological solution to address new challenges."
Emotions helps in improving the efficiency of learning. As Andrew Lo said, "What we consider to be 'rational' behavior is actually a complex negotiation among multiple components of the brain. If these components become imbalanced – for instance, too little fear or too much greed- we observe imbalanced behavior, which we call irrational." He added, "But these imbalanced behaviors aren't random. They're merely inappropriate for the environment in which they are exhibited, like the shark on the beach." It appears that traders who exhibit too little or too much emotional response tend to underperform traders in the happy medium. The neurophysiological basis of their performance provides a deeper and richer understanding of rationality and irrationality.
The price discovery process in the market requires its participants to engage in a certain kind of cause-and-effect reasoning like this: "If I do this, then others will do that, and in response I will…" Such a chain of logic implies that individuals have what psychologists call a theory of mind that enables them to understand the mental state of another person. Giacoma Rizzolatti showed that the "theory of mind" is a neurophysiologically hardwired feature of the brain. Using the neuroimaging technique of PET scans he found that humans, like apes, have "mirror neurons," which activate when we observe the actions of others. Mirror neurons allow us to experience the actions of others in a direct way. So the phrase "I feel your pain" is more literal than imaginary.
There is a limit to this mirror of intentions. While a four-year child can see one mirror deep into the hall of mirrors of intention, a seven-year child can see two mirrors deep. Most humans can delve only four levels deep into the hall of mirrors of intention. This means that there is a biological limit to our rationality. So EMH and rational expectations theory, which require an infinite chain of reasoning, cannot hold at all times and in all contexts.
Neurologist Roger Sperry and his collaborators deduced that the left hemisphere specialises in language, speech, logic, mathematics, and problem solving (referred to commonly as "intelligent" behaviour) whereas the right hemisphere is associated with facial recognition, spatial ability, and emotion. For his pioneering work, Sperry received a Nobel Prize in 1981. Subsequent research, however, discovered that the brain is more complicated. In the event of a trauma, many of the functions that Sperry thought were localised to one hemisphere can be rewired elsewhere in the brain. The brain is highly malleable or "plastic."
In his fascinating book Human, neurologist Michael Gazzaniga writes about the human brain. Based on his experiments, he concluded that the right hemisphere of the brain just records facts. It is responsible for the who, what, when, and where of reality. The left hemisphere of the brain interprets the how and why by constructing a narrative, for which it has a natural penchant. Central to what we call intelligence is our ability to construct a narrative. As Andrew Lo says, "We interpret the world not in terms of objects and events, but in sequences of objects and events, preferably leading to some conclusion, as they do in a story." He adds, "Our ability to choose an optimal behaviour appears related to our ability to come up with the most plausible-sounding explanation of the world: the best narrative."
Most of the uniquely human traits such as language, complex planning, logic, mathematical reasoning, and self-control reside in the prefrontal cortex. Called the "executive brain" the prefrontal cortex comes closest to the notion of Homo economicus. As Andrew Lo said, "If economic agents really 'maximize expected utility subject to budget constraints' or 'optimize portfolios via quadratic programming' ... or any other arcane behavior that economic theories such as rational expectations or the EMH predict, they will be using the prefrontal cortex to do so." However, the prefrontal cortex, like any organ of a living being, is subject to biological limitations. As Andrew Lo put it, "As impressive and unique as it is, it can't operate instantaneously or indefinitely. It can't multitask very well, contrary to popular belief and desire. It has problems planning several moves ahead or at several degrees of theory of mind. It will sooner construct a plausible story than admit ignorance." Stress impairs the performance of prefrontal cortex. Further it does not function under strong emotions.
The human brain contains a narrative prediction machine that forecasts the future. One would expect that if these forecasts turn out to be true we will continue our behaviour. Otherwise, we will revise our predictions. But there is a twist here. As Lo says, "However, our reliance on narrative to predict the future has a subtle flaw. Our brains will consciously use our narrative expectations to shape our behavior to make the predicted outcome more likely."
Intelligence implies a good ability to provide accurate cause-and-effect descriptions of reality. Scientific theories are a special case of cause-and-effect description. Albert Einstein's theory of relativity is a very complex scientific theory (a narrative). Einstein took years to generate it. Jeff Hawkins, author of On Intelligence, argues that there are two features of intelligence, viz, memory and prediction.
The theory of evolution has been confirmed by modern genetics and molecular biology. It is remarkable that Charles Darwin was able to generate his theory without them. One way to assess the power of a scientific theory is by the number of correct predictions it makes. By this measure, the theory of evolution has made many correct predictions in a wide range of contexts such as the behaviour of microscopic organisms to the mass extinctions of entire ecosystems. According to the evolutionary biologist Theodosius Dobzhansky, "Nothing in biology makes sense except in the light of evolution."
A common misconception about evolution is that it is designed to direct progression toward some optimal goal or higher form of being. Natural selection does tend to eliminate individuals that are less fit reproductively, but it does so by a passive process of attrition. Strictly speaking, it is not a process of selection but a process of elimination through trial and error. Evolution is not merely a process of elimination. It also involves mutation (which is quite ubiquitous) and what some biologists call differential reproduction. As Lo said, "Even very slight differences in reproductive success can lead to a genetic trait becoming common in an evolutionarily short amount of time." Through natural selection a species can become so well adapted to its environment that its existence is jeopardized by a change to that environment.
Human behaviour seems to fall largely between nature and nurture. While a third of our savings behaviour can be attributed to genetics, the balance two-third is not genetic, and can be attributed to factors like environment, culture, education, logical deliberation, and public policy.
The capacity to imagine and create complex scenarios seems to be unique to our species and is our most important evolutionary advantage. Darwinian evolution applies to ideas as it does to living things. However, there is an added difference. As Lo explained, "We can use our brains to test our ideas, in mental models, and to reshape them if they're found lacking. This is still a form of evolution, but it's evolution at the speed of thought." This ability separates us from other species and enables us to dominate our world. Herbert Simon introduced the notions of bounded rationality, satisficing, and heuristics which were at variance with the assumptions of neoclassical economics.
In 1947, two seminal works were published. Paul Samuelson's Foundations of Economics Analysis and Herbert Simon's Administrative Behavior. Both Samuelson and Simon won the Nobel Prize in economics. Samuelson's work became a cornerstone of modern mathematical economics. He assumed that individuals are rational who always maximize their expected utility and find mathematically optimal way to do this. Samuelson's work was inspired by mathematical physics- many physical phenomena optimize themselves, such as the path of a light through different transparent materials. Simon introduced the notions of bounded rationality, satisficing, and heuristics that were at variance with the assumptions of neoclassical economics. Since these challenged the establishment, they drew a lukewarm response. Simon reused his ideas in his research on artificial intelligence where they became central to the field.
Studies in behavioural finance and neuroscience have unearthed numerous departures from the standard economics view of human rationality. Are these departures just the exceptions that prove the rule of rationality or do they mean more? Lo argues, "We aren't rational actors with a few quirks in our behavior – instead, our brains are collections of quirks. We're not a system with bugs; we're a system of bugs." He adds, "These quirks aren't accidental, ad hoc, or unsystematic; they are the product of brain structures whose main purpose isn't economic rationality, but survival." Under certain conditions, our system produces behaviour that is considered "rational" by economists, but under other conditions they produce behaviour that is considered "wildly irrational" by economists. Nature favours a diversified bet as it increases the probability of survival. Likewise nature favours risk-aversion to risk–neutrality as it confers greater reproductive success.
Efficient markets hypothesis (EMH), a cornerstone of neoclassical finance, is physics–friendly. In contrast, the adaptive markets hypothesis (AMH), proposed by Lo, is biology–driven. As Lo said, "AMH realizes that despite the evolutionary pressure to maximize, they might not lead to optimal behavior. An evolutionarily successful adaptation doesn't have to be the best; it only needs to be better than the rest." The most important difference between the biology–driven AMH and the physics–friendly EMH stems from the fact that biology has a single, powerful unifying principle (Darwin's theory of natural selection) whereas physics today has numerous contenders for a "theory of everything," which are of limited relevance to economics.
Evolutionary theory was rescued by a remarkable synthesis between biology and statistics known as population genetics, first proposed by R.A. Fisher, a British mathematician. He showed that it was possible to mathematically simulate natural selection in a population by looking at its numerical population of genes. According to Lo, "Fisher's development of population genetics was the key innovation that made the advances of modern mathematical evolutionary theory possible, spurring a host of new ideas including sociobiology and evolutionary psychology."
In 1984 a group of physicists started the Santa Fe Institute (SFI) to promote interdisciplinary research in which scholars with diverse backgrounds (physics, economics, biology, and other sciences) would explore chaos and complexity. The mandate of SFI was to do pioneering work in a new field called complex adaptive system, using a branch of mathematics called "nonlinear dynamic systems." Computer simulation has become a favourite research tool at SFI. It helps in generating market dynamics using computer–simulated economic agents, subject to adaptive pressures. Many of the criticisms of economic orthodoxy made by the SFI school are not very different from those of the AMH. But there is an important difference. As Lo explained, "However, the AMH puts a much greater weight on past environments and adaptations to explain market behavior, and like Darwin's theory before it and Farmer's ecosystem of trading strategies the AMH doesn't predict any trend or end-state as inevitable."
Only time will tell whether the AMH will survive or perhaps be replaced by an even more compelling theory in future. However, even at this early stage it is evident that AMH can resolve many contradictions between the EMH and its alternatives. An efficient market represents the steady–state limit of a market in an unchanging financial environment. You need a theory to beat a theory. Adaptive Markets Hypothesis (AMH) is a theory that challenges the EMH.
D.E. Shaw focused on detecting and exploiting even the smallest anomalies. However as market dynamics were changing D.E. Shaw & Co had to work harder for its profits. As Shaw said, "Anomalies that had previously generated significant profits stopped making money, and you had to discover other, more complex effects that people had not found. The market is never completely efficient, but it certainly has a tendency to become more efficient over time." In evolutionary terms, markets adapt.
The remarkable growth of hedge funds in the modern financial environment is similar to the evolution of a successful species in a changing biological environment. As Andrew Lo says, "There are false starts, bursts of speciation and diversity, mass extinctions, adaptations, and innovations in the life history of hedge funds, just as in the evolutionary history of any animal." However, unlike biological evolution, financial evolution occurs at the speed of thought.
Technology has played a critical role in the evolution of markets, a good illustration of that being high frequency trading. An excellent low–tech illustration of this dynamic was the significant change in Kerala fish markets when cell phone coverage was extended to Kerala. Thanks to cell phones, Keralan fisherman could call the local beach markets from their boats to know what the demand was and then direct their boats where they could get the highest price. As a result, "The local price of sardines quickly stabilized, the volatility in the beach markets dropped, and waste fell nearly to zero … The market became significantly more efficient due to a simple technological change in the environment."
Technology has the potential to create unintended consequences. As Lo observed, "Increasing speed means more malfunctions, spikes, failures, and frauds. The benefits of modern computer performance can quickly be offset by the costs of Murphy's Law – anything that can go wrong, will go wrong."
The core principles of the traditional investment paradigm spawned by the Efficient Markets Hypothesis are as follows:
Principle 1: The Risk/Reward Trade-Off – Risk and reward are positively related for all financial investment – higher the risk, higher the reward.
Principle 2: Alpha, Beta, and CAPM – The expected return on an investment is a linear function of its risk and is expressed by an economic model called the Capital Asset Pricing Model (CAPM).
Principle 3: Portfolio Optimization and Passive Investing – Based on statistical estimates derived from Principle 2 and the CAPM, diversified long–only portfolios of financial assets can be constructed to offer investors attractive risk adjusted rates of return at low cost.
Principle 4: Asset Allocation – Asset allocation is far more important than market timing and security selection. It is the key for managing risk.
Principle 5: Stocks for the Long Run – While equities are more risky in the short run, they are less risky in the long run.
The above five principles have become the foundation of the investment management industry. They have influenced most of the products and services offered by investment professionals and have benefited millions of investors over the years. But these principles are not like the laws of physics and applicable forever. Rather they may be viewed as heuristics whose usefulness depends on the validity of their underlying assumptions.
The usefulness and accuracy of the above principles depends on the following technical assumptions:
Each of these assumptions can be challenged on theoretical, empirical, and experimental grounds. The relevant question is not whether these assumptions are literally true- rarely are economic assumptions true- but whether departures from them matter.
According to Lo, from the mid–1930s to the mid–2000s, a period of relative stability in the U.S financial markets and regulations, these assumptions were reasonable approximations. He calls this period as a period of Great Modulation. Lo has argued that these assumptions can be contested on theoretical, empirical, and experimental basis and the approximation errors associated with them have increased. As he said, "The emerging narrative from the perspective of the Adaptive Markets Hypothesis, is that these errors used to be small, but have grown considerably in recent years."
Although the Adaptive Markets Hypothesis is still in its infancy, a new investment paradigm is emerging. The five principles of the traditional investment paradigm may be restated from the perspective of Adaptive Markets hypothesis as follows:
Principle 1A: The Risk/Reward Trade-off – There is a positive association between risk and reward among all financial assets during normal market conditions. However, when most investors face extreme financial threats, they can act irrationally and behave like a herd. In such a case, risk is penalised and such periods can last for months or even years.
Principle 2A: Alpha, Beta, and the CAPM – The economic and statistical assumptions underlying the CAPM may not be satisfied in certain market environments. Hence, understanding the environment and population dynamics of market participants may be more important than any factor model.
Principle 3A: Portfolio Optimization and Passive Investing – When stationarity and rationality are good approximations to reality, portfolio optimization and passive investing are helpful. Due to technological advances, the notion of passive investing is changing. Even for passive index funds, risk management should be a high priority.
Principle 4A: Asset Allocation – Macro factors and new financial institutions are creating links and contagion across previously unrelated assets. So the boundaries between asset classes are getting blurred. As a consequence, managing risk through asset allocation is no longer as effective today as it was during the Great Modulation.
Principle 5A: Stocks for the Long Run – Equities do provide attractive returns over the very long run. However, few investors can afford to wait it out. Over more realistic horizons, the probability of loss is significantly higher. Hence investors have to be more proactive about managing their risk.
Yale sociologist Charles Perrow argued in his 1984 book Normal Accidents that complexity and tight coupling in combination are a recipe for potential disaster in many contexts.
Complexity refers to a system that has many components which are related to each other in a highly non-linear and difficult–to–comprehend manner. Tight coupling means that, for a proper functioning of the system, each component has to perform flawlessly – even if one component malperforms, the entire system comes to a grinding halt.
One can view the financial system as complex and tightly coupled and consider the savings and loans crisis of 1980s and 1990s, the debacle of Long Term Capital Management, and Lehman bankruptcy as normal accidents. However, in a 2010 article titled, "The Financial Meltdown Was Not an Accident," Perrow unequivocally rejected this application of his theory to the financial crisis. Instead he attributed the financial crisis to human behaviour. He wrote, "… I argue that the case does not fit the theory because the cause was not the system, but behavior by key agents who were aware of the great risks they were exposing their firms, clients, and society to… Complexity and coupling only made deception easier and the consequences more extensive." Perrow makes an important point as there is no dearth of bad behaviour in the financial industry.
Technological innovation and financial innovation have been intimately interconnected. Here are some examples:
The symbiosis between technology and finance has accelerated the pace of financial markets. A conspicuous example of this phenomenon comes from the options market. The Chicago Board Options Exchange (CBOE), the first of its kind, was set up in 1973, just before Fischer Black, Myron Scholes, and Robert Merton published their seminal papers on option pricing. In 1975 Texas Instruments introduced the SR-52, the first programmable, handheld, calculator, capable of handling the logarithmic and exponential functions of the Black–Scholes/Merton formula.
Shortly after the debut of SR-52, Irwin Guttag, one of the founders of the CBOE, asked his teenage son John to program the Black–Scholes/Merton formula for it. So finance professionals had an easy way to use the formula and this triggered a rapid growth of the CBOE. The convergence of science (Black-Scholes/Merton formula), technology (calculators and computers), institutions (like CBOE), and environment (the economic turmoil of the mid-1970s required risk mitigation on a large scale) led to an explosive growth of exchange–traded options, over-the-counter structured products, and credit derivatives. As Andrew Lo put it, "In the modern history of all the social sciences, few ideas have had such impact on both theory and practice in such a short time."
Advances in computing, telecommunications, data storage have greatly increased the speed of trade execution and order processing. This has lowered costs throughout the entire trading process. This evolution of financial technology is a manifestation of Moore's Law.
As Andrew Lo put it, "The emergence of automated, algorithmic, and online trading, mobile banking, crypto–currencies like Bitcoin, crowd funding, and financial robo-advisers are all consequences of Moore's Law." Technology has facilitated an interconnected global system that has increased the availability of capital to businesses and consumers around the world and lowered the cost of capital. However, these same interconnections also make the global financial system vulnerable to financial contagion of the kind witnessed in 2008.
When finance behaves badly, culture and behaviour are the main culprits. Technology too may be considered a key accomplice in many recent financial pathologies. As Andrew Lo put it, "In short, Moore's Law has to contend with Murphy's Law, 'whatever can go wrong will go wrong.' And when computers are involved, they usually go wrong faster, bigger, and are harder to fix."
To resolve the conflict between Moore's Law and Murphy's Law, we require a revised version of the financial system. This calls for an entirely new way of thinking and a different tool kit. The challenges posed by technology reflect a broader trend toward increasing complexity. According to Andrew Lo: "As the financial system grows more complex, it becomes harder and harder to understand, never mind manage. In fact, our attempts to regulate this complex system by layering rule on rule have actually increased complexity and uncertainty." As Debbie Lucas remarked, "Government is a source of systemic risk."
According to Perrow's theory of normal accidents, complexity and tight coupling are a recipe for systemic disruptions. Even though Perrow doesn't believe that his theory applies to financial crisis, complexity has certainly played a role in the crisis and it would be difficult to prevent future crises unless we have adequate expertise to prevent it.
Complexity may be a euphemistic way of saying "ignorance." Researchers in complex systems often cite simple nonlinear mathematical relationships which can produce tremendously complicated graphs. A slight change in the starting point can make it impossible to forecast where the graph will end up just a few steps later. The classic illustration of this phenomenon is the "butterfly effect" – given the complexity of the weather system, the flapping of a butterfly's wings in Mumbai can cause a hurricane in Florida several weeks later.
According to the Adaptive Markets framework, complexity means that we don't have a good narrative for the system. So we have to develop a deeper understanding of the underlying structure of the system. The Adaptive Markets framework also points to a second problem associated with complexity: the potential divisiveness of knowledge and the possibility of conflict. Those who have special knowledge are in an advantageous position and can exploit others. This is the tyranny of complexity – knowledge is power and power can be abused.
The abuse of knowledge may not be as bad as Lord Acton's warning that "power tends to corrupt, and absolute power corrupts absolutely." But the elitism that stems from deep expertise may be viewed by non–elites as arrogance, paternalism, and even intellectual despotism. So the excluded non–elites may reject such expertise. As Andrew Lo noted, "We're beginning to see evidence of this rejection in various contexts, financial and otherwise- budget cuts to regulatory agencies despite an increased workload post–crisis, decreased government funding for basic science and universities, the outright denial of the science of climate change by certain politicians and their constituents." If this trend is not reversed, it will become harder and harder to understand and manage a complex system like the financial system.
Adaptive regulation is regulation that is not prone to the same behavioural biases as human regulation. An example of such regulation is the use of countercyclical buffers which require that capital requirements vary inversely and automatically with the business and credit cycles. The financial system of the future will require a narrative that is scientifically accurate and more closely aligned with human values. As Lo says, "We'll need to describe chains of financial causation accurately and in a testable manner, using the scientific method, not political discourse. We'll need to create new feedback loops to monitor the financial environment, analogous to the human sense of fear and pain."
By using secure multiparty computation tools, it is possible to compute aggregate measures of risk without disclosing individual private or proprietary components.
Psychologist Philip Zimbardo has studied how good people can be influenced to do evil things by their surrounding culture. He suggests various behaviours that he believes will minimize the impact of a destructive culture. Some of them are: individual values of honesty, responsibility, and independence of thought; willingness to admit mistakes; refusal to respect unjust authority; and ability to consider future rather than the immediate present.
Irrespective of what the future challenges would be for our survival (climate change, pandemics, and so on) or what technological innovations will help us over those challenges, finance will play a pivotal role.
We need humble, competent and automated ways of managing world's wealth. Lo believes that sophisticated dynamic indexes will be the way forward. As he says, "Just as John Bogle's index fund democratized the world of finance in an unprecedented way, dynamic indexes can continue the process. The most sophisticated versions of these strategies will take into account your particular objectives, your constraints, your current and likely future circumstances, and all sorts of other personal attributes."
We can harness the power of financial markets to tackle some of the biggest challenges facing humanity. As Lo says, "I'll make a bold prediction: with the right financial structures, and by tapping into global financial markets, we can solve some of the world's most intractable problems within the next two decades. Problems like cancer, global poverty, and the energy crisis." One of the major achievements of modern finance has been to transform uncertainty into risk. According to the AMH, as uncertainty is converted into risk, investors adapt and capital follows.
As Lo says, "We seem to be at an inflection point in human evolution. We have the means to save ourselves, and perhaps colonize other planets, or destroy ourselves." He adds, "Large societal challenges require unprecedented levels of collaboration and collective intelligence. Finance is the most efficient means for facilitating this type of collective intelligence ever discovered. We are not just motivated by fear and greed. We are also guided by a sense of fairness and propelled by our imaginations."
As Lo says, "The German statesman Otto von Bismarck once called politics 'the art of the possible'. Along those lines, finance as 'the facilitator of the possible.' Through finance, we can change what is within the grasp of our politics, making the previously impossible attainable." He adds, "With the right expectations, the right financing, and the right vision, we can accomplish amazing things."
Although luck often plays a commanding role in our affairs, we often downplay its role. Why? There are three reasons:
According to Manish Dangi, one of the most celebrated bond fund managers in India, there are four kinds of fund managers in the Indian fixed income markets.
Elephants: Many fund managers are extremely risk-averse. They remain so despite the poor returns caused by their over-cautiousness.
Hawks: Hawks ignore caution in general and are always in a risk–on mode. Understandably they are often trapped in large incorrect moves. Nearly one half of fund managers are hawks.
Foxes: Foxes are flexible and adapt themselves to changing market conditions. They have thermostats to modulate risk taking. These managers are investor's favorites.
Turkeys: Turkeys pretend to have thermostats, but in reality they don't. Investment with them generates a random profile of returns. Nearly a quarter of fund managers are turkeys.
Stock exchanges allow some extremely large 'high frequency' traders to pay for faster access to the exchange. Computers of such traders can watch the order flow and send their own orders microseconds ahead of other traders. In essence they can jump the queue and get to the market before the price moves against them. Such behaviour is referred to as 'front running'. It promotes investment in expensive technology not to reflect new economic information in market prices but to exploit information about the orders of the other people.
One way to eliminate the disadvantage suffered by ordinary investors is to change the systems of trading on organised exchanges to electronic auctions held once a minute, or once an hour, or even once a day, depending on the nature of the stock being traded.
Two bulls, an old and a young, were in an enclosure with a low wall all around. They were ogling at a cow grazing outside. The younger bull was excited and sought the advice of the older one who said, "Run a few paces and then jump over the wall." The younger one ran a few paces but returned nervously. He asked the older bull, "Why don't you do that." The older bull said, "I tried that long time back and damaged my testicles. Since then I have become a consultant."
A 6th standard student was fond of learning new words and flaunting them to others. One day he learnt the word forefather. To impress his classmates he said, "My forefathers were great." They didn't get him. He repeated thrice, but even then they didn't get. Exasperated, he shouted, "My four fathers were great."
"To keep a fire burning brightly there's one easy rule: keep the two logs together, near enough to keep each other warm and far enough apart – about a finger's breadth – for breathing room. Good fire, good marriage – same rule."
"Almost all our faults are more pardonable than the methods we think up to hide them." — La Rochefoucauld