Having concluded in the previous chapter that behavioural factors, to a large extent, can help us account for cross-sectional anomalies and aggregate stock market puzzles time has come to look into some of the most famous financial bubbles. The jargon of finance contains numerous colourful expressions to denote a market-determined asset price at odds with any reasonable economics explanation. Such words as “mania”, “bubble”, “panic”, “crash”, and “crisis” immediately evoke images of frenzied and probably irrational speculative activity. Many of these terms have emerged from specific speculative historical episodes which have been sufficiently frequent and important that they underpin a strong current belief among economists that key capital markets sometimes generate irrational and inefficient pricing and allocation outcomes (Garber, 1990).

A bubble (or speculative bubble) is said to exist when high prices seem to be generated more by traders’ enthusiasm than by economic fundamentals. Notice that a bubble must be defined ex-post, i.e. at some point the bubble bursts and prices adjust downward, sometimes very quickly. Interestingly, hindsight bias then often kicks in. Many investors can be heard saying that they knew it all along, but if so why did they participate and, in some cases, lose vast sums of money? (Ackert & Deaves, 2010). We will look into some of the most interesting bubbles in the financial history and when possible include some behavioural finance comments to the events.


Tulipmania, a surge in the demand for tulip bulbs in the Netherlands in 1637, is the mother of all subsequent bubble narratives. The episode has long served as the epitome of the financial bubble. In his book, Shiller (2005) calls it “the most famous bubble of all. No asset bubble is too small or too large or its bursting will be compared to the Dutch tulip craze”. Indeed, references to tulips have come thick and fast in discussions of the current financial crisis.

The original story is often recounted based on Markay’s classical text from 1841 (Markay, 1841). In this description, the Netherlands became a center of cultivation and development of new tulip varieties after the tulip’s entry into Europe from turkey in the mid-1500s. They rapidly came to be prized above all other flowers, and the upper-class was willing to pay quite extraordinary sums to obtain the latest, most spectacularly coloured variety. Particularly prized were tulips with darker coloured stripes and “flames” on a lighterbackground. Many of the most valuable striations were created, as known now, by a virus. This was not understood at the time, however, hampering the systematic development of new varieties. Moreover, such “flaming” tulips were always a risky investment, since it was never certain whether their patterns would be as desirable, and valuable, from one year to the next. To minimize uncertainty, serious tulip collectors contracted to buy and sell bulbs while they were in bloom, making payment and taking delivery only later, once the bulbs were lifted from the ground.

Tulip prices were high because supplies of the most exciting new varieties were extremely limited. The supply of the most famous tulip of the 1620s, the Semper Augustus was in the hands of a single owner, who held on to the bulbs as prices offered rose from 1000 gulden per bulb in 1623 to over 3000 gulden per bulb in 1625. When a Semper Augustus bulb was finally sold some time later, the owner attached the stipulation that the buyer could sell neither the bulb itself, nor any of its offsets (i.e. smallbulbs that develop on the outside of the main bulb) without permission of the original seller. The problematic combination of uncertainty about the quality of a bulb’s next flower and mostly informal inevitably resulted in some failed transactions. Indeed, most of our reliable knowledge about tulip prices during this period derives from such transactions, since the disappointed party would often approach a notary to file an official complaint (van der Veen, 2009).

Through the 1620s and 1630s, professional growers and wealthy flower fanciers created a market for rare varieties in which bulbs sold at high prices. By 1636, the rapid price rises attracted speculators, and prices of many varieties surged upward from November 1636 through January 1637. In February 1637, prices suddenly collapsed, and bulbs could not be sold at 10% of their peak value. By 1637 the prices of all of the most priced bulbs of the mania had fallen to no more than 1/200 of 1% of their peak price. Figure 4 summarises the available data on tulip prices with a quality-weighted price index over this short time interval. Since the relevant tulip bulbs are regularly planted in the fall and only dug up in the spring, the relevant prices here are the prices that appear in contracts for future delivery. Given the acknowledged absence of basic economic shocks over this short span of time, the unmistakable bubble pattern appears to speak for itself. The story concludes by asserting that the collapse led to economic distress in the Netherlands for years afterwards (Thompson, 2006).

Figure 4: A standardised price index for tulip bulb contracts, created by Earl Thompson. Thompson had no price data between February 9 and May 1, thus the shape of the decline is unknown. The tulip market is known, however, to have collapsed abruptly in February (Based on Thompson, 2007)

Both the famous discussion of Mackay (1841) and the famous academic discussion by Posthumus (1929) point out a highly peculiar part of the episode. In particular, they tell us that, on February 24, 1637, a large organisation of Dutch florists and planters, in a decision that was later ratified by Dutch legislatures and courts, announced that all contracts written after November 30, 1636 and before the re-opening of the cash market in the spring possessed provisions that were not in the original contracts. The new provisions relieved their customers of their original unconditional contractual obligations to buy the future tulips at the specified contract price, but demanded that they compensate the planters with a fixed percentage of their contract prices. The provisions, in effect, converted the futures prices in the original contracts to exercise prices in options contracts. The corresponding option price paid to the planters was only later determined. In particular, after over a year of political negotiation, the legislature of Haarlem, the center of tulip-contract trade during the “mania”, determined the compensation to the sellers to be only 3.5% of the contract price for those contracts made between November 30, 1636 and the spring of 1637 (Thompson, 2006).While it may be argued that expectations were not rational, that the traders were unaware of the conversion of futures to option contracts, Mackay (1841) emphasises the public nature of the extensive negotiations over the details of the contract conversions since almost the beginning of the upturn.

So was the Tulipmania a speculative bubble? In spite of numerous analyses through time, authors still disagree whether or not Tulipmania in fact is an example of a bubble phenomenon after all. Van der Veen concludes that the Dutch Tulipmania of 1636-1637 clearly qualifies as a bubble, in the sense that prices were not sustained by private estimations of value. Claims that market fundamentals explain the observed price patterns cannot be sustained, despite their popularity in recent years. Nor do arguments about inadequate government regulation offer much explanatory leverage. Authorities remained largely uninvolved in the bubble (although the States did consider taxing tulip profits in 1636-1637) and they mostly refused to be drawn into the adjudication of individual cases, at least until a year later. However, this does not mean that there was no regulation: the market was regulated by the rituals and norms that developed around the tulip trade, as well as by the broader social connections within which it was embedded.

Forbes (2009) calls the Tulipmania the classical bubble story in many ways. He argues that Tulipmania is an iconic story and that such stories form an important social and rhetoric purpose in our history and are open to strategic, if not manipulative, use by those opposed to the diffusion of the markets process. Several other authors have challenged the Tulipmania’s iconic status as a financial bubble. Garber (1989, 1990) in a series of papers has argued that the Tulipmania story constitutes just such a handy myth which propagates even to this day. First, Garber (1989, 1990) has suggested that the pattern of price changes observed during the tulip craze matches the pattern one would expect for novel luxury goods in limited but gradually growing supply. His argument is widely cited but rarely examined closely. More recently, Thompson (2006) has claimed that the dramatic increases in tulip prices during the winter of 1636-1637 were due to a shift in market instruments from standard futures contracts to options. If either author is correct, the literature on financial bubbles may be forced to make do without this popular episode.

It probably cannot be known for sure whether or not Tulipmania is an example of an economic bubble, but Ackert & Deaves (2010) conclude that no matter what there is evidence that people bought tulips because they believed that others would pay even more. According to behavioural finance theories you buy an asset that you realise is overvalued because you think there is a foolish individual out there who will pay even more. Thus, you might really know the tulip bulb is not worth anywhere near 3000 gulden, but you think someone else will pay more to get it. One should perhaps not assume irrationality too quickly. Perhaps there is another interesting interpretation for the Tulipmania as suggested by Ackert & Deaves (2010). Tulips come in many varieties and colour patterns and many are truly rare. Is a tulip fancier who pays a high price for a bulb any more irrational than an art collector who pays millions of dollars for a painting? Ackert & Deaves (2010) conclude that as odd as it might seem to us today, the high values associated with tulip bulbs could have been rationally based on people’s preference at that time in history. The bubble bursting could have been due to a sudden change in preferences, unlikely as it seems but not impossible.

The South Sea bubble

The South Sea bubble in 1720 was a great economic bubble leaded by speculation ofstock in the South Sea Company. During the War of the Spanish Succession, a largeamount of the British government debt was issued, and the government wanted tocut off the interest rate of the debt to relieve its financial pressure. At the sametime, the stock of South Sea Company was very popular because it was granted amonopoly to trade in Spain’s South American colonies as part of a treaty during theWar of the Spanish Succession. The company would like to hedge its risk by buying the debts with its highly evaluated stocks and get stable income from the government.Under this circumstance, the South Sea Scheme was activated exactly the same asour discussion above. This scheme was considered to be a win-win trading. As aconsequence, the public started to buy the stocks of South Sea Company and theillegal actions from the company (fraud, lending money to the buyers to enable theirpurchase of the stocks, etc.) escalated the irrational behaviour (Yan, 2011). As figure 5 shows,the share price had risen from the time the scheme was proposed: from £128 inJanuary 1720 to £1,000 in early August, followed by a dramatic fall down to about 100pounds within several months. Hundreds of people lost a huge amount of money,including Sir Issac Newton. When he was asked about the continuance of the risingof South Sea stock, he answered: “I can calculate the movement of the stars, butnot the madness of men” (Taylor, 2004).

Figure 5: The development in stock price for the South Sea Company (from Shea, 2007)

The literature on the South Sea Bubble has emphasised irrational behaviour as the dominant behaviour in financial markets, at least as far as explaining the spectacular rise in South Sea equity values, in 1720.

The literature largely predates, however, the usage of the term “irrational” as it appears in the writings on behavioural finance discussed in the earlier chapters. The literature moreover says nothing about the limits on arbitrage that could have limited irrational pricing of South Sea and other shares. The literature does present us with individual cases of successful and unsuccessful speculation, but the evidence does not clearly point to any instance of what the modern economist would call arbitrage. As tantalising as the evidence is that some individuals were engaged in what we would now call international risk arbitrage, the evidence is not conclusive. There also appears in the literature stories of individuals and institutions that made money in the South Sea Bubble simply by selling steadily into the rising market of 1720 (Shea, 2007).

The 1929 stock market crash

The sharp rise and subsequent crash of stock prices in late 1920s is perhaps the most striking episode in the history of American financial markets (De Long & Shleifer, 1990). Known as “the Great Depression” this episode was preceded by stock market booms that crashed inthe U.S. and U.K. in the late 1920s. A series of banking panics in the U.S. beginning in October 1930 were not successfully allayed by the Federal Reserve and this turned the situation from bad to ugly. The depression was transmitted around the world by the fixed exchange rate links of the gold exchange standard and numerous protectionist measures. Many countriesacross the world were finally hit by debt and currency crises (Yan, 2011).

Before the crash, hundreds of thousands of Americans invested heavily in the stock market in the belief that the development of utility would lead to a “new” economy, and a significant number of them were borrowing money to buy more stocks. The rising share prices encouraged more people to invest, which created a positive feedback loop. A massive bubble was generated by such kind of speculation. The bubble began to deflate, and October 24, 1929, which became known as “Black Thursday”, marked the beginning of the “Great Crash”. This crash is one of the most devastating stock market crashes in the history of the United States. It triggered the 12-year Great Depression that affected all Western industrialized countries and that did not end in the U.S. until the onset of American mobilisation for World War II at the end of 1941 (Yan, 2011).

Some observers have interpreted the 1929 price pattern as reflecting changing fundamentals in the economy. Fisher (1930), for example, argued throughout 1929 and 1930 that the high level of prices in 1929 reflected an expectation that future corporate cash flows would be very high. Fisher (1930) believed this expectation to be warranted after a decade of steadily increasing earnings and dividends, rapidly improving technologies, and monetary stability. According to this interpretation, the run-up of stock prices before the crash reflected shifts in expectations of the future that were ex-post faulty but ex-ante rational. The crash and the subsequent slide of stock prices then reflected a rational, and in this case an ex-post correct, revision of beliefs, as investors recognized the approach of the Great Depression and the end of the Roaring Twenties (De Long & Shleifer, 1990).

Other students of the Great Crash, notably Galbraith (1954), have argued that even though fundamentals appeared high in 1929, the stock market rise was clearly excessive. Galbraith (1954) cited margin buying, the formation of closed-end investment trusts, the transformation of financiers into celebrities, and other qualitative signs of euphoria to support his view. Over the past three decades, Galbraith’s position has lost ground with economists, especially with financial economists, as the efficient-market hypothesis has gained.

Much following work sides with Fisher’s interpretation of 1929. Sirkin (1975), for example, examined the revisions of long-run growth forecasts required for shifts in stock yields in 1929 to reflect shifts in perceived fundamental values. He found that, compared to actual post-World War 2 yields and stock returns, the implied growth rates of dividends were quite conservative, and in fact lower than postWorld War 2 dividend growth rates. Santoni & Dwyer (1990) failed to find evidence of a bubble in stock prices in 1929. Along similar lines, Barsky & De Long (1990) argued that, if the long-run growth rate of dividends were thought to be unstable and if investors projected recent-past dividend growth rates into the future, then large swings in stock prices, such as those of the 1920s and 1930s, would be the rule rather than the exception. Barsky & De Long (1990) found that year-to-year movements in stock prices appear to have been no more sensitive to changes in current real dividends in the late 1920s and early 1930s than in the remainder of the twentieth century.

The dot.com/tech bubble

For many, the tech bubble of the late 1990s is probably the most prominent example of a stock market boomand bust. In the late 1990s it was common to believe the Internet and the knowledge economy more generally had fundamentally transformed both society and the productive possibilities open to mankind. In its most extreme form this sort of belief imagined a sort of “digital sublime” which promised almost unlimited wealth. As the internet and information technology spread throughout society, investors became ever more optimistic about the growth prospects and profit potential of companies involved in IT. Indeed this new age Zeitgeist served an important unifying role for those engaged in the investment and development of the new economy almost regardless of its truth (Forbes, 2010).

During the late 1990s there was a bull market, particularly in technology stocks. During the bullmarket, individual investors increased their levels of trading. Investors allocated higher proportions oftheir portfolios to shares, invested in riskier (often technology) companies, and many investorsborrowed money in order to increase their shareholdings (Barber & Odean, 2001).The bubble and crash was particularly clear in the case of technology stocks. The NASDAQ index,which focuses on technology stocks, rose more than six-fold between 1995 and early 2000 (see figure 6). It then lostmore than three quarters of its peak value by late 2002 (Redhead, 2008).

Figure 6: The tech bubble at the end of the 1990s (Redhead, 2008)

Best (2005) investigated the dot.com stock bubble, which occurred in the late 1990s and burst in 2000, in a behavioural finance framework. One conclusion was that internet stocks acquired a form of celebrity status. Their prices exceeded fundamental value just as the earnings of celebrities appear to surpass the talent of the individuals concerned. Just as the perception of celebrities has an emotional dimension, investors in internet stocks were seen as having an emotional attachment to them. Just as the media promotes celebrities, and thecult of celebrity, the media promoted internet investing and a culture of internet investing. Part of the reasoning of the analysis provided by Best (2005) is similar to the familiarity heuristic of behavioural finance. As explained in previous chapters, the familiarity heuristic leads people to prefer to invest in things they think they know and understand. At the time of the internet stock bubble large numbers of people were beginning to use the internet, which therefore felt familiar to them. The internet was new, exciting and appeared to offer huge potential. It is possible that internet stocks, by association with the internet itself, came to be seen as exciting investments with huge potential.

The role of momentum in the development of the high-tech bubble was particularly significantaccording to Boswijk et al. (2007). They divided investors into two groups. One group comprised fundamentalists (price traders) who believed in the mean-reversion of stock prices towards a true (fundamental) value such that deviations from true values would be corrected. The other group consisted of trend followers (herd traders) who believed that a direction of price movement would continue. The proportions of investors in the two groups vary over time. The researchers found that in the late 1990s almost all investors were trend following, and that the dominance of trend followers persisted for several years. This is consistent with strong momentum in the formation of the bubble. The studies by Best (2005) indicated that high technology (including dot.com) stocks were particularly susceptible to momentum trading (i.e. trend following).

Taffler & Tuckett (2002) provided a psychoanalytic perspective on the technology-stock bubble andcrash of the late 1990s and early 2000s, and in so doing gave a description of investor behaviour totallyat odds with the efficient markets view of rational decision-making based on all relevant information.They made it clear that people do not share a common perception of reality; instead everyone has theirown psychic reality. These psychic realities will have varying degrees of connection with objective reality. Decisions are driven by psychic reality, which is a realm of feelings and emotions. Reason maybe secondary to feeling. Feeling affects the perception of reality. People are seen as engaging in wish fulfilment wherein they perceive reality so that it accommodates to what they want. People see what they want to see. Unpleasant aspects of reality may be subject to denial, which is the pretence that unpleasant events and situations have not happened. Denial reduces the ability to learn from unpleasant experiences, since unpleasant experiences are removed from conscious awareness.

The U.S. housing boom and bust

Few markets have had such a skyrocketing rise, followed immediately by an equally steep plummet to new depths, as the housing market in the U.S. has had in the early years of the twenty-first century. U.S. home prices increased from 1997 to 2006 by approximately 85% (see figure 7), adjusted for inflation, fostering the largest national housing boom in the nation’s history. From 2000 to 2005 alone, the median sales price of American single-family homes rose by one-third. In some places, the rise was even sharper. Over those same years, the median home price in New York rose 79%, in Los Angeles 110% and in San Diego 127%. In costal California, the rise was especially sharp and so was the later fall (Sowell, 2009). The cost of owning houses relative to renting them increased dramatically from 2003 to 2006, suggesting the existence of a bubble, where home prices greatly exceeded their intrinsic values. Home prices have subsequently fallen by more than 30 percent (Shiller, 2007).

Figure 7: The U.S. Real housing prices from 1975 to 2006 (Based on Sowell, 2009)

In the aftermath of the global financial crisis and the Great Recession, research has sought to understand the behaviour of house prices. Before 2007, countries with the largest increases in household debt relative to income experienced the fastest run-ups in house prices (Glick & Lansing 2010). Within the United States, house prices rose faster in areas where subprime and exotic mortgages were more prevalent (Mian & Sufi 2009; Pavlov & Wachter 2011). In a given area, house price appreciation had a significant positive impact on subsequent loan approval rates (Goetzmann et al., 2012). Many studies have attributed the financial crisis of 2007-09 to a credit-fuelled bubble in the housing market. The U.S. Financial Crisis Inquiry Commission (2011) emphasized the effects of a self-reinforcing feedback loop in which an influx of new homebuyers with access to easy mortgage credit helped fuel an excessive run-up in house prices. This, in turn, encouraged lenders to ease credit further on the assumption that house prices would continue to rise.

By contrast, to explain the boom, others have used theories in which house prices were driven mainly by fundamentals, such as low interest rates, restricted supply, demographics, or decreased perceptions of risk. A recent paper (Favilukis et al, 2012) argues that the run-up in U.S. house prices relative to rents was largely due to a financial market liberalization that reduced buyers’ perception of the riskiness of housing. The authors develop a theoretical model where easier lending standards and lower mortgage transaction costs contribute to a substantial rise in house prices relative to rents. But this is not a bubble. Rather, the financial market liberalisation allows rational households to better smooth their consumption in the face of unexpected income declines, thus reducing their perception of economic risk. Lower risk perception induces households to accept a lower rate of return on the purchase of risky assets such as houses. A lower expected return leads to an increase in the model’s fundamental price-rent ratio.

A large number of authors have argued psychological factors rather than fundamentals play the keyrole in house price dynamics. The earliest academic papers on the role of psychology on realestate prices focused on unexplained serial correlation in real estate prices (Case & Shiller, 1989). Of course, serial correlation itself is not necessarily evidence of irrational markets if underlying rent growth is also serially correlated. Yet data on rents is very hard to obtain, confounding tests of market efficiency. Meese & Wallace (1994) obtained detailed rental data from advertisements and estimated an asset pricing model on houses in the San Francisco area.The authors concluded that the run-up in prices in the late 1980s was not fully justified by fundamentals. Both papers concluded that pricing inefficiencies are due to high transaction costs that limit arbitrage opportunities for rational investors.

Psychology, too, may affect how households set their expectations of future price appreciation. Case & Shiller (1988) surveyed recent home buyers in four cities about their expectations of future house price growth. Recent buyers in Los Angeles, a market with strong house price appreciation in the 1980s, reported that they expected much higher long-term house price appreciation than households in a control market, Milwaukee, where house prices were flat in the 1980s. In a subsequent survey (Case& Shiller, 2004), recent buyers in Milwaukee raised their reported expected appreciation in-line with the national housing boom. By 2006, recent home buyers in both Milwaukee and Los Angeles had lowered their reported expected appreciation for the next year, although they did not adjust down their 10-year expected appreciation rate as much (Shiller, 2007). Shiller (2007) cites the survey evidence and other case studies to support his conclusion that a psychological theory, that represents the boom as taking place because of a feedback mechanism or social epidemic that encourages a view of housing as an important investment opportunity, fits the evidence betterthan fundamentals such as rents or construction costs.

A second psychological theory proposed by Brunnermeier & Julliard (2007) argues that households cannot fully disentangle real and nominal changes in interest rates and rents. As a result, when expected inflation falls, home owners take into account low nominal interest rates when making housing purchase decisions without recognizing that future appreciation rates of prices and rents will fall commensurately. They argue that falling inflation leads to otherwise unjustified price spikes and housing frenzies and can help explain the run-up in U.S. and global prices in the 2000s. As evidence, Brunnermeier & Julliard show that inflation is correlated with the residuals of a dynamic rational expectations model of house prices.

Probably the most direct evidence on the importance of psychology in real estate markets focuses specifically on loss aversion in downturns (Engelhardt, 2003). Yet loss aversion may have a hard time explaining the current housing boom or even excess volatility in downturns. Since loss averse sellers set higher asking prices when house prices are falling, this particular psychological factor actually leads to lower volatility over the cycle, making the puzzle of possibly excess volatility in cycles an even more difficult problem to explain (Mayer & Sinai, 2007).

Shiller (2007) argue that the boom in the housing markets from 2000 onwards was largely driven by extravagant expectations of further price increases. Using data from questionnaires surveys for two majorUS cities he found that in times and places of high price changes, expectations of future price increases werehigher. Moreover he shows that as the rate of price increases changes, the expectations of future pricesincreases are also altered in the direction of the change. Further, he argues that the declining standards in lending and the proliferation of complex mortgage backedsecurities were a result of the institutional changes that resulted during the boom and concludes that there isa“coordination problem with psychological expectations” during periods of boom in that people find ithard to alter their expectations of future price increases since they find it difficult to coordinate on a time toalter their expectations inferring from the expectations of other investors.In line with previous arguments Shiller (2007) attribute the boom in the housing market to a “social contagion of boom thinking” and “new era stories” in the belief that home prices would continue to riseforever, this belief being further strengthened by the media with its overly optimistic stories around theprice increases. He calls this a “price-story-price” feedback loop that takes place repeatedly during aspeculative bubble.

Some behavioural finance thoughts on the present financial crises

The financial crisis of 2008, which started with an initially well-defined epicenter focused on mortgage backed securities8 (MBS), has been cascading into a global economic recession, whose increasing severity and uncertain duration has led and is continuing to lead to massive losses and damage for billions of people. Heavy central bank interventions and government spending programs have been launched worldwide and especially in the U.S. and Europe, with the hope to unfreeze credit and bolster consumption. One general overall conclusion regarding the fundamental cause of the unfolding financial and economic crisis is the accumulation of several bubbles and their interplay and mutual reinforcement leading to an illusion of a “perpetual money machine”allowing financial institutions to extract wealth from an unsustainable artificial process (Sornette & Woodard, 2008).

It has been argued that the immediate cause for the financial crisis is the bursting of the house price bubble principally in the U.S. and the U.K. and a few other countries including Denmark, leading to an acceleration of defaults on loans, translated immediately into adepreciation of the value of mortgage- backed security (Doms et al., 2007). After a peak inmid-2006, the real-estate market in many states reached a plateau andthen started to decrease. A number of studies have shown indeed a strong link betweenhouse price depreciation and defaults on residential mortgages (Demyanuk, 2009). In particular, Demyanyk & van Hemert (2008) explain that all along since 2001 subprime mortgages have been very risky, but their true riskiness was hidden by rapid house price appreciation, allowing mortgage termination by refinancing/prepayment to take place. Only when prepayment became very costly (with zero or negative equity in the house increasing the closing costs of a refinancing), did defaults took place and the unusually high default rates of 2006 and 2007 vintage loans occurred (Sornette & Woodard, 2008).

It is clear to all observers that banks have acted incompetently in the recent MBS bubble by accepting package risks, by violating their fiduciary duties to the stockholders, and by letting the compensation/ incentive schemes run out of control (Sornette & Woodard, 2008). From executives to salesmen and trading floor operators, incentive mechanismshave promoted a generalized climate of moral hazard.

Justified by the principles of good corporate governance, executive compensation packages have a perverse dark side of encouraging decision makers to favour strategies that lead to short-term irreversible profits for them at the expense of medium and long-term risks for their firm and their shareholders. Even if the number of CEOs facing forced turnover has increased 3 to 4-fold during the past 20 years while, simultaneously, most contractual severance agreements require the forfeiture of unvested options, lumpsum payments and waiving forfeiture rules often compensate for such losses. There is something amiss when the CEOs of Citibank and of Countrywide walk out of the mess they created for their firms with compensation packages. It is often the case that firms finally turn out losing significantly more when the risks unravel than their previous cumulative gains based on these risky positions, while the decision makers responsible for this situation keep their fat bonuses. As long as the risks areborne by the firm and not equally by the decision makers, the ensuing moral hazardwill not disappear. It is rational for selfish utility maximisers and it will therefore remain a major root of future financial crises. Herding effects amplify the moral hazard factor discussed in previous chapters. Indeed, performance is commonly assessed on the basis of comparisons with the average industryperformance. Therefore, each manager cannot afford to neglect any high yield investment opportunity that other competitors seem to embrace, even if she believes that, on the long run, it could turn out badly. In addition, herding is often rationalizedby the introduction of new concepts, e.g. “the new economy” and new “real ption” valuation during the Internet bubble. And, herding provides a sense of safety in the numbers: how could everybody be so wrong? Evolutionary psychology andneuro-economics inform us that herding is one of the unavoidable consequences of our strongest cognitive ability, that is, imitation. In a particularly interesting study using functional magnetic resonance imaging on consumption decisions performedby teenagers, Berns et al. (2009) have recently shown that the anxiety generated bythe mismatch between one’s own preferences and others motivates people to switch their choices in the direction of the consensus, suggesting that this is a major forcebehind conformity.

Greed, anxiety, moral hazard and psychological traits favouring risk taking in finance were prevalent in the past and are bound to remain with us for the foreseeable future. Therefore, the question whether greed and poor governance was at the origin of the crisis should be transformed into the question of timing, that is, why these traits were let loose to foster the development of anomalous excesses in the last few years.

Credit rating agencies have been implicated as principal contributors to the credit crunch and financial crisis. They were supposed to create transparency by rating accurately the riskiness of the financial products generated by banks and financial actors. Their rating should have provided the basis for sound risk-management by mortgage lenders and by creators of structured financial products. The problem is that the so-called AAA tranches of MBS have themselves exhibited a rate of default many times higher than expected and their traded prices are now just a fraction oftheir face values.

To provide the rating of a given Collateralized debt obligations (CDO) or MBS, the principal rating agencies (e.g. Moodys, Fitch and Standard & Poors) used quantitative statistical models based on Monte Carlo simulations to predict the likely probability of default for the mortgagesunderlying the derivatives. One problem is that the default probabilities fed into the calculations were in part based on historical default rates derived from the years 1990-2000, a period when mortgage default rates were low and home prices were rising. In doing so, the models could not factor in correctly the possibility of a general housing bust in which many mortgages are more likely to go into default. The models completely missed the possibility of a global meltdown of the real estatemarkets and the subsequent strong correlation of defaults. The complexity of the packaging of the new financial instruments added to the problem, since rating agencies had no historical return data for these instruments on which to base their risk assessments. In addition, rating agencies may have felt compelled to deliberately inflate their ratings, either to maximise their consulting fees or because theissuer could be shopping for the highest rating (Berg & Bech, 2009). Recently, Skreta& Veldkamp (2009) showed that all these issues were amplified by one single factor, the complexity of the new CDO and MBS. The sheer complexity makes very difficult the calibration of the risks from past data and from imperfect models that had not yet stood the test of time. In addition, the greater the complexity, the larger the variability in risk estimations and, thus, of ratings obtained from different models based on slightly different assumptions. In other words, greater complexity introduces a large sensitivity to model errors, analogous to thegreater sensitivity to initial conditions in chaotic systems. If the announced rating is the maximum of all realised ratings, it will be a biased signal of the asset’s true quality. The more ratings differ, the stronger are issuers’ incentives to selectively disclose (shop for) ratings.

Skreta & Veldkamp (2009) argue that the incentives for biased reporting of the true risks have been latent for a long time and only emerged when assets were sufficiently complex that regulation was no longer detailed enough tokeep them in check. Note that the abilities of ratings manipulation and shopping toaffect asset prices only exist when the buyers of assets are unaware of the games being played by the issuer and rating agency. This was probably true until 2007, when the crisis exploded.

The different elements described above are only pieces of a greater process that can be aptly summarised as the illusion of the “perpetual money machine.” This term refers to the fantasy developed over the last 15 years that financial innovations and the concept that “this time, it is different” could provide an accelerated wealth increase. In the same way that the perpetualmotion machine is an impossible dream violating the fundamental laws of physics, it is impossible for an economy which expands at a real growth rate of 2-3 per cent per year to provide a universal profit of 10-15 per cent per year, as many investors have dreamed of (and obtained on mostly unrealised market gains in the last decade). The overall wealth growth rate has to equate to the growth rate of the economy. Of course, some sectors can exhibit transient accelerated growth due to innovations and discoveries. But it is a simple mathematical identity that global wealth appreciation has to equal GDP growth.

The lack of recognition of the fundamental cause of the financial crisis as stemming from the illusion of the “perpetual money machine” is symptomatic of the spirit of the time. The corollary is that the losses are not just the downturn phase of a business or financial cycle. They express a simple truth that is too painful to accept for most, that previous gains were not real, but just artificially inflated values that have bubbled in the financial sphere, without anchor and justification in the real economy. In the last decade, banks, insurance companies, Wall Street as well as Main Street and many of us have lured ourselves into believing that we were richer. But this wealth was just the result of a series of self-fulfilling bubbles. As explained in more details above and elsewhere, in the U.S. and in Europe, we had the Internet bubble (1996-2000), the real-estate bubble (2002-2006), the MBS bubble (2002-2007), an equity bubble (2003-2007), and a commodity bubble (2004-2008), each bubble alleviating the pain of the previous bubble or supporting and justifying the next bubble.

Bubbles: Past, Present and Future

As apparent from the previous subsections a common element of many speculative bubbles, if not scams, is the belief that the world has entered a new bright dawn of history which will liberate man from his life of want and struggle (Forbes, 2010). Despite the wide range of assets that have witnessed bouts of irrational exuberance (tulips and equities to name but a few), bubbles seem to follow a similar pattern. As Marx (Montier, 2007) noted, history repeats itself, first as tragedy, second as farce. This section attempts to outline the anatomy of an asset price bubble.

A number of authors have looked into common characteristics of bubbles. Band (1989) argued that market tops exhibited the following features:

  • Prices have risen dramatically.

  • Widespread rejection of the conventional methods of share valuation, and the emergence of new ‘theories’ to explain why share prices should be much higher than the conventional methods would indicate.

  • Proliferation of investment schemes offering very high returns very quickly.

  • Intense, and temporarily successful, speculation by uninformed investors.

  • Popular enthusiasm for leveraged (geared) investments.

  • Selling by corporate insiders, and other long-term investors.

  • Extremely high trading volume in shares.

The most famous model of bubbles is one promoted by Kindleberger (1989, 2005). It is largely the result of work carried out by the economist Hyman Minsky. A diagrammatic outline of the bubble stages is presented below (Montier, 2007):



Credit creation




Critical stage / financial distress



Displacement is generally an exogenous shock that triggers the creation of profit opportunities in some sectors, while shutting down profit availability in other sectors. As long as the opportunities created are greater than those that get shut down, investment and production will pickup to exploit these new opportunities. Investment is likely to occurin both financial and physical assets. Essentially a boom is engendered. In the tech. bubble in the U.S. equity market, the exogenous shock was clearly the arrival of the internet. Here was something capable of revolutionising the way in which so many of us conducted our businesses (and lives more generally).

In the credit creation state the boom is then further exacerbated by monetary expansion and/or credit creation. Effectively, the model holds money/credit a send ogenous to the system, such that for any given banking system, monetary means of payment may be expanded not only within the existing system of banks, but also by the formation of new banks, the development of new credit instruments and the expansion of personal credit outside the banking system. Sooner or later demand for the asset will out strip supply, resulting in the perfectly natural response of price increases. These price increases give rise to yet more investment (bothreal and financial). A positive feed-back loop ensues: new investment leads to increases in income which, inturn, stimulate further investment. Monetary and credit creation in the U.S. hightech bubble were largely the result of overly accommodative monetary policy (Montier, 2007)

Euphoria is the term given when speculation for price increase is added to investment for production and sales. Effectively this is momentum trading or the “greater-fool-theory” of investment as previously presented. Adam Smith referred to such developments as “overtrading” (Montier, 2007). Kindle berger correctly notes that over trading is anebulous concept. However, he notes that over trading may involve purespeculation, an over estimate of prospective returns or excessive gearing. The U.S. experience between

and 2002 certainly fit sall three of these elements. The massive popularity of such creations as aggressive growth funds testifiestothelargepurely speculative elements at work with in the U.S. equity market. Analysts clearly had excessive over estimates of the prospective return satleast in terms of the long-termearnings potential of U.S. corporate (Redhead, 2008).

Given that analysts and corporate tend to work so closely, these estimates essentially receive sign off from the companies as well. As such, they reflect the ridiculous levels of over optimism that infected corporate managers during the late 1990s. Further reflections of over optimism among corporate managers can be witnessed by the scale of recent good wil lwrite-downs. After all, a good will write-down is nothing more than an admission that a company over paid for its acquisitions.

The bubble phase leads to share prices reaching unrealistic levels. These are share price levels far in excess of what can be justified by fundamental analyses using dividend discount models or price-earnings ratios (see the chapters on dividend discount models and ratio analysis). Indeed one feature of bubbles, identified by Kindleberger (1989, 2005), is the emergence of “new age” theories. New age theories are ad hoc theories that seek to justify why prices should be far in excess of what conventional share valuation models suggest.

Eventually social mood passes its peak and cognitive rationality comes to dominate social mood. Investors sell and prices fall. If social mood continues to fall, the result could be a crash in which stock prices fall too far. The situation is then characterised by an unjustified level of pessimism, and investors sell shares even when they are already under-priced. Investors’ sales drive prices down further and increase the degree of under-pricing. Fisher & Statman (2000, 2002) provided evidence that stock market movements affect sentiment. A vicious circle could develop in which falling sentiment causes prices to fall and declining prices lower sentiment.

There may then be an occurrence that causes prices to fall rapidly. One such occurrence might be the emergence of new companies. The new companies compete with existing ones and push down their profits. Also when the new companies float on the stock market, the additional supply of shares will help to depress share prices. Towards the end of the 1999-2000 technology stock bubble many new companies were issuing shares. This increased supply of shares overtook the growth in demand for shares. The result was that the prices of shares in the technology sector began to fall.

Rising interest rates could be another occurrence that leads to falling share prices. Bubbles often involve people borrowing money in order to buy shares. High interest rates could cause investors to sell shares in order to pay the interest. Such sales could set off a crash. In Japan in 1990 interest rates rose sharply. This was followed by collapses in the prices of both shares and property. Rising interest rates can also reduce the demand for shares by making alternatives such as bank deposits more attractive. Higher interest rates also reduce expenditure on goods and services and thereby lower corporate profits. Lower expected profits can cause a fall in share prices. Other factors that can precipitate share price collapses include share sales resulting from negative statements by people who are looked upon as experts. These may be genuine experts such as governors of central banks, or self-appointed experts such as newspaper gurus. Also prospective investors may stop buying because they deplete their sources of money. The flow of new investors on to the market will eventually stop. These factors can start a crash by increasing sales of shares and decreasing purchases. Cassidy suggested that a crash could be precipitated by a random event, or have no apparent catalyst, if stock prices have reached sufficiently unrealistic levels (Montier, 2007; Redhead, 2008).

According to Pepper & Oliver (2006) a long period of rising prices is associated with an accumulation of investors who need to sell (since their holdings of money are less than the desired levels). Such investors may delay asset sales while prices continue to rise. A continuing rise in prices is likely to attract speculative investors who do not plan to hold the investments for the long term; Pepper & Oliver (2006) refer to their investments as being loosely held. A long period of rising prices would lead to many investors needing to sell shares in order to raise money for other purposes, and to many speculators with loosely held shares.

When the expectation of price rises disappears, both groups of investors will sell. Share prices fall sharply.

The psychoanalytic view of Taffler & Tuckett (2002) sees the unconscious mind as excluding uncomfortable aspects of reality from awareness. When the bubble bursts, and prices fall, it becomes impossible to completely exclude unpleasant aspects of objective reality from awareness. Feelings of anxiety, loss, panic and shame emerge. Selling the shares as quickly as possible could then become part of the process of denial.

The fourth stage of the bubble process is labelled the critical stage or the financial distress stage. The critical stageis the point where as etofinsiders decide to take their profit sand cash out. Significant selling by insider shasbeen a hallmark of 2000/2001. The fact that, by 2002, insiders were still selling four times the amount of stock they were buying should tell you some thing about how confident they were over the prospect forequity appreciation over the following 12 months (Montier, 2007).

Financial distress usually follows straight on from the critical stage (indeed the two can be hard to separate, hence we have tacked them together). The term “financial distress” is borrowed from the finance literature where it refers to a situation in which a firm must contemplate the possibility that it may not be able to meet its liabilities. For an economy as a whole, the equivalent condition is a nawarenesson the part of a consider able segment of the speculating community that arush for liquidity (out of assets into cash) may develop. As the distresspersists, so the perception of crisis increases. Kindle berger (1989, 2005) notes: “The specific signal that precipitates the crisis may be a failure of a bank, or a firm stretched too tight, the revelation of a swindle”. The occurrence of swindling/fraud seems highly pro-cyclical and the role of swindles in bursting bubbles is intriguing.

Revulsion is the final stage of the bubble cycle. Revulsion refers to the fact that people are so badly scarred by the events in which they were embroiled that they can no longer bring themselves to participate in the market a tall. It is clearly related to that most dreadful of current buzzwords: “capitulation’.Capitulation is generally used to describe the point when the final bull admits defeat and throws in the towel. In the language of the Kindle berger/Minsky model, capitulation is described as degeneratepanic.Revulsion is obviously not exactly the same thing, since it can (andfrequentlydoes)occurpost-capitulation. Interms of the 2002 market we saw no signs of capitulation. Most strategists were still amazingly bullish. Perhaps more significantly volumes remained very high (Redhead, 2008).

The degenerate panicends when one of three events occurs (Montier, 2007):

  • Prices fall so low that investors are tempted to move back into the asset.

  • Trade is cut off by setting limits on price declines.

  • Lender of the last resort steps in.

So let’s assume we get a degenerate panic. Which, if any, of these option swill present it self as a potential escape route from the markets’ declines? Well, equity price shave a considerable down side before we can start to claim valuation support. At the very least, 30% declines in prices are likely to be required before valuations look tempting to us. These condroute provides only temporary release from panic. Ahalton trading may allow people to reassess, however, it may simply result in the market dropping instages in the face of persistent panic. The third route is perhaps them ostappealing-alender (or rather buyer) of the last resort emerges. This is a favourite of the current market rumours that the U.S. authorities are buying equities (The World Bank, 2012).

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