2 The course of a typical speculative episode
2.1 Japan and the Lost Decade
2.2 Irrational exuberance in the US
3 Common theory and quantifiable facts
3.1 Efficient market hypothesis
3.2 Eqilibria, bifurcations and chaos theory
3.3 Agent based financial market models
4 Structural factors triggering, amplifying and pricking economic bubbles – a reality check
4.1 Bubble catalyst - displacements
4.2 Magnifying mechanisms
4.2.1 Financial liberalization, the growth of credit, the quality of debt, supportive monetary and fiscal policy
4.2.2 Feedback loops, contagion and the role of the media
4.2.3 Rating Agencies
4.3 The big bang the other way around – the slowdown
5 Psychological factors triggering, amplifying and pricking economic bubbles – a reality check
5.1 Animal Spirits
5.1.4 Money illusion
5.2 Bubble catalyst – psychological anchors for the markets that may trigger speculation
5.2.1 Quantitative anchors
5.2.2 Moral anchors
5.3 Magnifying mechanisms
5.3.1 Public attention to the markets and herding
5.3.2 New-era talk and short financial memory
5.3.3 Cultural changes
5.4 The big bang the other way around
6 Policy implications
6.1 Policy implications of a central bank
6.1.1 The “targeting long-run fundamentals” and “leaning against the wind” strategy
6.1.2 Reduced interest rates and the credit target
6.1.3 Other possible central bank measures
6.2 Policy implications of a government
6.2.1 The Tobin tax
6.2.2 Saving plans and retirement
6.2.3 Bretton Woods
6.3 Lender of last resort
“There is protection only in a clear perception of the characteristics common to these flights into what must conservatively be described as mass insanity.”
Repeatedly bubbles have occurred during times of “extended investments in infrastructure such as canals or railroads” or around technological inventions that are made available for the general public such as cars, electricity, phone–lines and the internet. They go hand in hand with financial inventions, financial liberalization and excess leverage. The relatively new and striking feature of recent bubbles is that they expand to real estate and stock markets shortly after the occurrence of a speculative episode in either one of the markets. Further they seem to occur in shorter time spans while accompanied by larger amplitudes. Examples are the Japanese asset price and real estate bubble of the late 1980ies and early 1990ies, the dot-com bubble,1997–2000, followed by a housing boom in the US, merging into the recent financial crisis of 2007-2008. Frequently these bubbles are fueled by the overoptimistic outlook not only of the so-called experts or gurus but also by the extremely positive perception of the general public resulting in a “this-time-is-different feeling”, “new-era talks” or the “it-won’t-happen-to-us believe”. Most of the time these bubbles are self-feeding processes. Historical levels of the stock and real estate markets, especially where the level of fundamentals should really be are completely ignored. The recent magnitude of booms, particularly the magnitude of the subsequent crashes appears to be unrelated to any external event that may have caused such unsustainably high levels in both the stock and real estate market. Obviously the hikes and crashes last seen cannot be explained under any rational assumptions. Besides economic variables like growth and trade figures, inflation or interest rates other forces must be at play when driving prices so far away from their fundamentals. Market psychology and the inherit feedback mechanisms must play a major role when prices surge and excitement about economy, technology, houses or great investment opportunities spreads through social ranks, even crossing borders infecting other markets at high speed. This paper tries to detect circumstances and components that repeatedly cause markets to slide towards extremes. Particularly the influence of psychological factors will be considered.
The paper is structured as followed:
Section two gives a brief overview of the Japanese asset price bubble as well as the dot-com bubble and the subprime crisis. Section three will cover theoretical approaches in explaining market movements, discussing the appropriateness of the efficient market hypothesis, statistical approaches and agent based financial market models. Section four and five consider structural and psychological factors leading to the emergence, propelling and the burst of speculative bubbles. Throughout section six policy measures will be analyzed. Section seven concludes.
2. The course of a typical speculative episode
Speculation: “The act of trading in an asset, or conducting a financial transaction, that has a significant risk of losing most or all of the initial outlay, in expectation of a substantial gain.” 
Speculative episodes seem to be a reoccurring phenomenon not only in financial markets but also in real estate and commodity markets. Historically, asset price bubbles in industrialized countries are somewhat of a rare occurrence. However, there have been at least four distinct asset price bubbles in the recent past starting with the Tokyo asset price bubble in the 1980s, the real estate and stock price bubble in the Nordic countries of Europe at around the same time, the Asian financial crisis in the 1990s and the dot-com bubble followed by the subprime crisis in the late 1990s and early 2000s. Not only did these bubbles occur in relatively short time they also involved both the stock and the real estate market. Keeping this in mind a short description of the two bubbles with the greatest momentum will be given.
2.1 Japan and the Lost Decade
After the devastations of World War II Japan, made great technological progress during the 1950s and 1960s. By the 1980s it was the second leading industrial power dominating the global electronics industry. The Japanese market was highly regulated and protected by the government. Entering the promising Japanese market was extremely hard and sometimes even impossible for foreign companies. The Ministry of Finance maintained low interest rate ceilings for deposits and loans, government officials identified Japanese companies that were to be preferred when it came to granting these low-interest credits. Since inflation was greater than interest rates on deposits, investment seemed to be limited to real estate and stock, where real returns had been positive. With greater economic prosperity demand for housing as investment vehicle rose resulting in an unmistakable upward trend in the value for homes. Low interest rates and increasing international demand for Japanese high – tech products caused business investment to surge, stock prices increased rapidly and the Japanese yen appreciated. Kindleberger and Aliber (2005) identify three factors that ultimately led to the emergence of the Japanese real estate bubble. Firstly, the real (inflation corrected) return on Japanese real estate had been positive for thirty years making it look great as investment when compared to deposits, where real returns, as already mentioned, where pushed into the negative by higher inflation. Secondly, international pressure during the mid 1980s led to deregulations of the Japanese markets resulting in its financial liberalization. Interest rate ceilings on deposits and loans were raised, governmental guidance was less extensive, restrictions on foreign investment for Japanese firms were relaxed and banks were permitted to increase their foreign branches. Furthermore, banks became free to increase real estate loans as share of their total loans. And thirdly, to avoid further appreciation of the yen to maintain a competitive advantage, the Bank of Japan opted for expansionary monetary policy, increasing the money supply and further lowering interest rates. As money supply increased, loans for real estate became relatively cheaper. The optimism about the future and the feeling of financial superiority as described by Galbraith (1994) in combination with high returns led to a seemingly unstoppable inflow of funds into Japan. With increasing real estate prices and a soaring economy many Japanese felt richer they tried to diversify their new wealth turning to stocks. A second argument for the increased investments into the stock market may be that while prices for homes increased at a rate of 30% p.a. they became exorbitant for many Japanese who, looking for investment opportunities started to invest into the stock market. Economic growth remained rapid during the 1980s, heightened international demand for Japanese stocks, decreasing lending standards and the anticipation of ever increasing real estate and stock prices with possibly huge returns led to the inflation of both markets. Japan’s stock prices increased in real (inflation – corrected) terms by 275.6% from 1982 to 1987 as the savings rate declined. Market participants seemed to not evaluate historical price movements and did instead concentrate on recent developments of the prices, extrapolating the trend. This behavior of trend extrapolation in the short term is supported by the findings of Shinji Takagi who, through a survey among investors found, that expectations in the short run are formed based on recent price movements supporting the prevailing trend. Traditionally, Japanese banks owned large amounts of real estate and stock. With increasing prices in both, the real estate and stock market, the banks’ capital also increased, giving them the opportunity to further increase real estate loans. New financial intermediaries were established to meet the great demand for loans. Financial innovations in the form of convertible bonds emerged. Formerly not eligible borrowers were now accepted and granted loans as their perceived wealth in the real estate or stock market constantly increased while lending standards constantly decreased. With increasing stock prices firms could raise needed funds more easily and at very little cost. Real estate prices increased faster than rents. Initially investors wanted to meet the interest payments for highly leveraged real estate through the expected rents. As rental income dropped below the costs for the interest payments the needed cash could be made available through further borrowing against the real estate or through selling the property. However, this option of finance was only available as long as real estate prices increased at a rapid rate. As the bubble grew, Japanese banks became some of the biggest banks in the world, Japanese firms acquired companies and prestigious properties in Europe and the US, the consumption of luxury goods surged and the inflow of capital from Europe and the US seemed endless. “..The Japanese had all the money – and they were spending it … as if they were very rich…”. By the end of 1989 home prices were so high that “…Banks had developed one – hundred – year, three generation mortgages.” The Nikkei index peaked at 38,916 in December 1989. The market value of Japanese stocks was twice the value of all US stock, the GDP of Japan however was less than half of the US GDP. Land per capita in Japan was four times as much as land per capita in the US, but income per capita in Japan was only 60% - 70% of that of the US. The new governor of the Bank of Japan worried about the social atmosphere in the country and brought new banking regulations on the way. The monetary policy was tightened, raising the discount rate from 2.5% in May 1989 to 6% in August 1990 to stabilize the markets and the yen. Japanese banks were instructed to limit the growth rate of real estate loans as compared to other loans. Rental income however was still not sufficiently large for the owners to meet the monthly mortgage payments. And with a slowing growth rate of real estate loans the cash needed to meet these obligations could no longer be obtained. Investors became distressed sellers of real estate and stock causing the prices to drop. Stock and real estate prices started to decline in January 1990. Investors looking for more promising opportunities began to transfer their funds away from Japan to other Asian countries as well as to the US. Kindleberger and Aliber (2005) argue that this shift of funds towards the Asian countries and towards the US stock market may have been the foundation for the following speculative episodes in these countries. Stock prices declined by 30% in 1990 and another 30% in 1991. They hit bottom in February 2009 with the Nikkei being at 7,568, although the global crisis at that time may also have had a negative impact. A decline in stock and real estate prices also meant that the Japanese bank capital was declining at a fast pace. Lower bank capital and the new regulations had a tight grip on banks which were more and more reluctant to lend. Investment declined not only as a result of increasing interest rates and changed attitudes of banks regarding the availability of loans. Anticipated profits had also been revised downward while producing companies had significant excess capacity. As people became worried about the future, household spending slowed and increased at a much smaller rate than formerly expected. The perceived wealth decreased and, to be able to meet the interest payments, the savings rate increased and peaked in January 1991 at about 34% of GDP. Bankruptcies increased and so did the loan losses for the banks and non-bank institutions putting them further to the edge. Banks became increasingly cautious, what once was thought to be conservative – accepting real estate as collateral for loans – had become a highly risky endeavor. Internationally concerns about the solvency of Japanese banks increased and resulted in increased interest rates for Japanese banks. The immediate impact was that many loans made by offshore Japanese banks were no longer profitable and were called in. Furthermore, the increased premium for Japanese banks acted as a warning signal for sensitive investors who further withdrew and re – allocated their funds to non – Japanese banks, causing increased outflows of capital from Japan. With the recession of 1991 Japan entered the era of what became known as the Lost Decade where despite extremely low interest rates, below 1%, the economic growth was lavish, oscillating around 0%.
2.2 Irrational exuberance in the US
The US stock market had been increasing since it hit bottom in 1982. The first cell phone system was developed at that time with the use of cell phones growing exponentially over the years. The World Wide Web became available to the public in 1994. Both innovations had a major impact on how people were able to transmit and store information. Both changed the everyday lives of millions of people dramatically. The great and fast advancements of technology could be experienced firsthand by everyone. In this atmosphere of a new technological and live changing era, the stock market began its spectacular hike which ended in 2000 merging into a housing boom that ended with the subprime crisis in 2007. Impressive US corporate earnings growth, 36% in 1994, 8% in 1995 and 10% in 1996, coincided with the emergence of the new technologies and were linked to them by the public. With a fast growing IT (internet technology) industry, coupled with a weak dollar in the early 1990s international demand for US capital and technological products increased rapidly. The US dollar increased which made foreign goods relatively cheaper for the US. The US economy boomed during the 1990 with an inflation rate declining from 5.2% in January 1990 to 1.67% in January 1999, the unemployment rate declined from 8% at the beginning of the 1990s to less than 4 % at the end of the 1990. The market value of US stock increased to 300% of US GDP in 1999. The personal saving rate declined from 11% in 1981 to 2% in 1999. Starting in the 1990s people focused their investments predominantly on IT stocks. As stock prices kept increasing, high rates of return for many short-term oriented venture capitalists (VCs) attracted more and more investors who were willing to make a quick fortune themselves. According to Kindleberger and Aliber (2005), the capital available to VCs as a group surged by a factor of five during the boom phase. Initial public offerings (IPOs) that usually resulted from entrepreneurship supported by VCs promised significant capital gains for everyone involved. In most cases the share prices were significantly higher after the first day of trading. This demonstration of ever increasing stock prices, the unstoppable demand for IT stock, overoptimistic experts and the constant exposure to news and stories about extremely successful investment made by average people convinced even those who had not yet believed in the new era talk. However, most people seemed to have ignored the fact that many of these new IT start-ups had not yet made any profit. But expectations about possible future gains, or even worse, the prospect of having missed out on a great money-making opportunity, made most investors blind to this uncomfortable truth. As already mentioned, when it comes to short-term investments, market participants extrapolate the trend forming expectations about prices, projected revenues and the like based upon recent price movements. In times of a boom or euphoria, this strategy is extremely dangerous and helps further blowing up the bubble. The FED (Federal Reserve System) under Alan Greenspan did nothing to stop or at least dampen the speculation. In 1995 interest rates were increased last until August 1999. According to Shiller (2005), the market’s interpretation of statements made by the FED was that interest rate increases were over by March 1995 and that the FED instead prepared for a soft landing. Kindleberger and Aliber (2005) point out that the market value of the New York Stock Exchange in 1998 had increased by 40% and that the NASDAQ Composite (NASDAQ), the market where all the new technology stocks were traded, had increased by 90% in only twelve months. They convincingly draw a connection to the imploding Japanese asset price bubble around that same time. International as well as US investors restructured their portfolios and started to increasingly invest into the soaring US economy. The surge of capital inflow into the US resulted in an increase in domestic investment and a decrease in the US savings rate, which is equivalent to an increase in investment and consumption. The US trade deficit increased and the constantly increasing capital inflow led to an appreciation of the US dollar. Foreign goods became relatively cheaper for the US, dampening inflation. According to Kindleberger and Aliber (2005) most of the inflowing funds were used to either buy more securities or to increase consumption as wealth objectives were achieved, through the perceived greater wealth through investments in the stock market. Investors felt richer and started to diversify their portfolios turning to housing. The FED began to increase interest rates starting in August 1999 from below 5% until January 2001 to over 6%. The NASDAQ peaked at 4,572 in January 2000 before declining and bottoming in June 2002 at 1,172. The Dow Jones Industrial Average (Dow), where traditional companies like Ford or General Motors are traded, peaked in January 2000 at 11,723 falling until March 2003 to 7,674. With the stock market bubble deflating the FED was fast in aggressively lowering interest rates beginning in January 2001, putting real interest rates well into the negative, paving the way for the housing boom. The Dow started its race to new heights in 2003, picking up speed during 2006 reaching its new high in October 2007, peaking at 14,164. With the stock market folding at high speed, with real interest rates being negative and with the proclamation of the “ownership society” under the Bush administration, investors fled the stock market turning to real estate as investment option instead. Home prices started to increase in 1998 and picked up speed in 2000. Prices for homes increased by 52% from 1997 to 2004 much faster than income increased during that same period. According to Shiller (2005), people were afraid that houses soon could be too expensive for them to ever be able to afford one. Short-term gains and the rush out of the stock market with the belief that real estate was a safe investment were just two of many possible psychological causes justifying the new found love for houses. Moreover, already during the stock market boom in 1990s, there seemed to be declining standards among managers who were more and more interested in creative accounting and who appeared to be more concerned with their short-term benefits rather than long-term profitability of the companies they worked for. During the housing boom lending and mortgage standards seemed to be decreasing at a rapid rate. Lending standards in the US had changed in a way that formerly not eligible customers were now able to obtain a mortgage for their homes. The structure of mortgages had also changed, where traditional mortgages were being replaced by adjustable-rate mortgages (ARMs), especially after 2003. With ARMs, initial interest rates are only temporarily low and increase after some time. With mortgage rates likely to increase in the future, possible payment problems especially when interest rates increase, are deferred to a later date. However, under these ARMs, more homeowners qualified for a mortgage, especially the lower income, minorities and less educated people. These mortgages constituted the market for subprime mortgages. The emergence of a shadow banking system supported by financial innovations to circumvent regulation is another indicator for decreasing lending standards. Home prices peaked in 2006 followed by a drastic decline of 29-30% through January 2013 as many borrowers were not able to make their mortgage payments. Increased foreclosure rates in 2006/2007 led to the subprime mortgage crises in the US in 2008 which later morphed into a worldwide financial crisis leaving most countries in a recession.
3. Common theory and quantifiable facts
“The most challenging difficulty in the study of a financial market is that the nature of the interactions between the different elements comprising the system is unknown, as is the way in which external factors affect it.” 
In this section I will give a quick overview of approaches to markets. It will show how basic theories have changed over time and evaluate the efficiency of the respective theory in trying to explain extreme events in these markets.
3.1 Efficient market hypothesis
According to the efficient market hypothesis (EMH), known through Eugene Fama, all publicly available information is already priced into financial assets. Any possible up- or downward movements in equity prices are anticipated. For this reason EMH assumes that financial assets are always priced correctly which means that the fundamental value and the value of the respective asset are equal. Only new information can change the prices which causes a random walk for assets in financial markets. Prices therefore will always be unpredictable. “The most efficient market of all is the one in which price changes are completely random and unpredictable.” Correspondingly it should not be possible for any investor to beat the efficient market. After EMH no investor can buy cheap and sell dear and make a good profit consistently. Referring to Shiller (2005) EMH assumes that performance and brilliance do not have any influence when it comes to investing. This may also be the reason why many people believe they do not have to pay attention to whether stock prices are over – or underpriced and therefore ignore the usual valuation of the markets. Sornette (2004) states that the more intelligent and the harder working investors are, the more random the price changes will be under EMH. Warren Buffet is the most prominent and probably most successful example of an investor doing what according to EMH seems implausible if financial markets were efficient, investing smart, buying cheap, selling dear and making a good profit. Sornette (2004) found that in order for stock markets to work properly there have to exist some sort of noise trades which are less or not informed at all next to the informed investors. If all investors had the same information then all prices would be the same as well. If that is the case then obviously there is no incentive to trade. Further the hypothesis assumes that all investors act rationally, making any decision by evaluating all available information correctly. It seems unrealistic for all investors in the financial market to have the same information on all stocks at all times making decisions only based on rational calculation considering that the average daily turnover only on the New York Stock Exchange in 2003 was greater than forty million US dollars, including more than three million registered trades per day. Trying to explain economic bubbles seems to be a fruitless attempt through EMH since upswings in prices caused by speculation should not exist. Proponents of this theory argue that investors who are not as informed could cause deviations away from the fundamental value without reasoning. However, the availability and the processing of the same information by all market participants is a crucial corner stone of EMH and should hold. Social interaction and possible feedback loops caused by this interaction are totally ignored. If markets were frictionless and would exist in an idealized world with no transaction costs then EMH could possibly hold. However reality shows that especially stock markets violate the efficient market hypothesis and that only some fraction in stock market fluctuations can be justified by new information. Nevertheless EMH has had a huge influence on policy makers over the past decades while deregulating markets. Therefore, in analyzing recent economic and financial evolution in combination with the emergence of respective bubbles the hypothesis has to be kept in mind.
3.2 Equilibria, bifurcations and chaos theory
Through this very mathematical approach scientists consider economical questions concerning the existence of (market) equilibria, their uniqueness and their stability, the adjustment path towards the equilibrium, its economic properties and alternative system states. The analysis of differential equations representing a possible market system nearly always starts out with testing for any equilibrium. Many multidimensional systems consist not only of one but multiple equilibria, each having a different influence, positive or negative, on the system. An equilibrium can be locally or globally stable. If the state variable converges towards the equilibrium irrespectively of the initial value, then the system is considered globally stable. If however the state variable converges only when close to the steady state, it is locally stable. Globally stable equilibria imply that the system only has one equilibrium. Local stability implies unique local equilibria. This assumption cannot be met if there exists a continuum of equilibria. Then, any deviation away from the equilibrium entails no convergence toward the steady state at any point in time. Five different stability cases are defined for linear systems. In an unique globally stable equilibrium, the state variable converges back towards the steady state, irrespectively of the initial value. A continuum of instable equilibria implies that none of the equilibria is locally or globally stable. Any equilibrium can only be reached from its own position. If no equilibrium exists, then the state variable moves monotonously toward ±∞. The initial value defines any double cycle. The state variable alternates between the initial value and some other value respectively. The steady state is unstable. If a system is uniquely unstable, then the same accounts for the steady state. The system is defined either by a monotonous or by an alternating divergence. In summary there are two possible scenarios in a dynamic system, either convergence or explosion, with them being monotonous (for linear systems only), alternating or oscillating. Continuous endogenous movements, either oscillating or alternating mark exceptional cases. Indicators for the steadiness of a dynamical system are the amplitude and frequency. The amplitude defines the maximum displacement from the steady state. The frequency is defined through , where the period length describes the length of one cycle, that is its oscillation from maximum to maximum. The larger the frequency, the shorter the length of a cycle. This observation can be made in financial markets, where the amplitude and the frequency seem to have increased over the past decades. This phenomenon also has an effect on the emergence of economic bubbles which seem to be occurring in shorter time spans being more severe. An early supporter of the use of differential equations to describe markets and their movements was Samuelson with his multiplier-accelerator model (1939). In his linear model the steady state is consistent with the findings of Keynes, it implies that an equilibrium always exists. The multiplier and the accelerator do in fact cause oscillation, however not continuously. In reality this means that the economy would need constant exogenous shocks for it to develop and be healthy. The model only considers a closed economy, only works with persistent endogenous oscillation and may be unstable. Nevertheless it did motivate modern business cycle theory and is still used today as basic approach for further evolved models.
Non-linear differential equations defining a system are closer to reality than linear systems when it comes to describing (financial) markets. However, they can only be solved in a timely manner with the help of computer programs. These systems may have no equilibrium, one or multiple stable equilibria. They may have one or more unstable steady states or a continuum of equilibria, so called quasi-periodic oscillations. They may also be defined by chaos. Moreover there may emerge different combinations like the LAS (locally asymptotic stable) equilibrium or chaotic movements like the random walk for example. With a globally stable steady state the system will reach its equilibrium irrespectively from the initial value. If there exist multiple equilibria, then the steady states of y1 and y3 will be locally asymptotic stable, y2 will be instable being equal to the initial value. If there does not exist any equilibrium then the state variable moves toward ±∞. In order to test a non-linear system for its locally asymptotic properties, the system has to be converted into a linear system. The generated linear system can only approximate the non-linear system well if close, that is local, to the steady state. The logistic map, is a unimodal function and is a great example of how a seemingly simple and linear equation can, with increasing , turn the system into chaos. Chaos here means that the timeline will not explode, the system is sensitively dependent on its initial state, also known as the butterfly effect, and the movements in the system are irregular but deterministic.
Abbildung in dieser Leseprobe nicht enthalten
For there exists one stable steady state at . For exist two locally stable (LAS) equilibria. For there also exist two equilibria, however both of them unstable resulting in a double-cycle. This can be seen in the bifurcation above where at the bifurcation point after the line parts in two, representing a double-cycle. At one can observe another partition resulting in a cycle with a length of four, later eight and then, at chaotic behavior. Bifurcations are a nice way to illustrate what effect small changes in parameters of formerly stable systems can have. From one stable steady state, the system can turn into one where it jumps around between two or more equilibria which are more or less stable. Similar behavior can be seen for example in the stock markets. If changes in the parameters are big enough, then the system may end up in deterministic chaotic behavior. A recent paper in the field of complex dynamical systems also suggests that there may exist “generic early-warning signals” just before the state of the system tips when approaching a critical threshold. Critical slowing down occurs just before the stable system enters the bifurcation-phase, which may lead to cycles or chaos later on. A phenomenon seen especially during times of highly leveraged speculation like in Japan or the US as earlier discussed. A slowing down in the formerly rapidly increasing prices for real estate and stock resulted in trend reversal and the tipping of the system. Moreover, spatial patterns can emerge before a critical transition. It can be observed that in systems consisting of different units, connected units tend to take on similar states, increasing the cross-correlation before a critical event. This phenomenon could be observed in real estate markets around the world in the early 2000s. Homes seemed to not only be overpriced in the US but also in the Nordic countries of Europe for example. Housing prices also surged regionally in Hawaii when prices of real estate were extremely high in Japan. Moreover, international stock price movements as well as the recent financial crisis seem to support this theoretical finding. Power-law structures such as fat tails in returns may vanish as a critical transition is approached. Recapitulatory, increased trade volatility, or a volatility calm, systematic relationships between variance and first-order autocorrelation and increased spatial coherence are, according to Scheffer et al. (2009) all possible warning signals for approaching critical transitions in financial markets. However, the paper stresses that “…there is no one-size-fits-all spatial pattern announcing critical transitions”.
Nevertheless, these models, linear or non-linear, are not fully capable of explaining market movements, especially during times of speculation. Mostly due to the fact that market psychology does not seem to play too big of a role here, if it does at all. Data is observed, steady states are calculated and their stability checked. Although this is an important part of coming to understand markets, especially the financial ones, it leaves out one if not the greatest manipulator, the people participating in the markets itself.
3.3 Agent based financial market models
With increasing computer capacity, this branch of research has evolved from the non-linear models discussed earlier. Scientists approach financial markets with the help of stochastic models by imitating complex dynamical systems. Stylized facts of financial markets like speculative bubbles and their inevitable burst, high volatility, fat tales in returns (leptokurtosis), martingales, autocorrelation of returns and volatility clustering are included in these models to replicate financial market movements as precisely as possible. One specific interest lies in modeling the dynamical interactions of different market participants (agents) and their consequences for financial markets. The financial market models are designed in so called computer labs and are capable of painting a very close picture of reality. They can be used to find possible causes for upswings in the markets or to check which impact for example the introduction of a tax on financial transactions (Tobin tax) may have, without actually disturbing the market. The assumption is that movements in the markets are caused by the interaction of heterogeneous, boundedly rational agents. Typically, agents are divided into chartists and fundamentalists. Chartists use technical analysis (TA), fundamentalists use fundamental analysis (FA). Technical analysis assumes that all relevant information is already included in the market price. The study of recent price movements is indispensible. It is assumed that prices move in accordance with trends (“the trend is your friend”). Hence, chartists are particularly interested in identifying these trends in order to take advantage of them (trend extrapolation). If prices increase, chartists believe that markets are undervalued and will continue to go up. They will therefore start to buy. But if prices decrease, chartists are convinced that markets are overvalued which is the equivalent to a sell signal. Further, TA assumes that certain structures in the markets are repeated. On that account chartists will look out for certain formations, like trend lines, head-shoulder formations, triangles or flags for example to determine their strategy. Besides these graphical approaches there also exist quantitative methods that help chartists decide on their market strategy. These include moving average or the Japanese candlesticks. However, there seem to exist innumerable rules, which make chartists’ explicit choice appear arbitrary and random. Generally, chartists cause greater disturbances in the market. Through trend extrapolation their actions will intensify any upward or downward trend, regardless of possible fundamentals in the market.
 See: Galbraith, J.K.,”A Short History of Financial Euphoria” (1994), p. 2.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p. 10.
 See: for example Kindleberger, C. P. / Aliber, R. (2005), Akerlof, G.A. /Shiller, R. J. (2010), Reinhart, C.M. / Rogoff, K.S. (2011) or Galbraith, J.K. (1994).
 See: http://www.investopedia.com , retrieved 05/13/13.
 See: for example Kindleberger, C. P. / Aliber, R. (2005), Akerlof, G.A. /Shiller, R. J. (2010), or Reinhart, C.M. / Rogoff, K.S. (2011).
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p. 145 ff..
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p. 149.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p. 152.
 See: Schiller, R. J., “Irrational Exuberance” 2nd edition, (2005), p.141.
 See Takagi, S., “Exchange Rate Expectations: A Survey of Survey Studies“, (1991).
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p.152.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p.147.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p.153.
 See: http://finance.yahoo.com, retrieved 05/14/2013.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p.146.
 See: Schiller, R. J., “Irrational Exuberance” 2nd edition, (2005), p.224.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005),p.154.
 See: http://finance.yahoo.com, retrieved 05/14/2013.
 See: http://www.tradingeconomics.com, retrieved 05/14/2013.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p.153 ff..
 See: http://www.tradingeconomics.com, retrieved 05/14/2013.
 See: Schiller, R. J., “Irrational Exuberance” 2nd edition, (2005), p.37 ff..
 See: Schiller, R. J., “Irrational Exuberance” 2nd edition, (2005), p.39.
 See: http://www.multpl.com/inflation/table, retrieved 05/15/2013.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005), p.158.
 See: http://research.stlouisfed.org/fred2/graph/?s [id]=PSAVERT, retrieved 05/15/2013.
 See: Kindleberger, C. P./ Aliber, R., “Manias, Panics and Crashes” 5th edition, (2005),p.162.
 See: http://www.tradingeconomics.com, retrieved 05/17/2013.
 See: http://finance.yahoo.com, retrieved 05/15/2013.
 See: http://www.fedprimerate.com, retrieved 05/16/2013.
 See: Schiller, R. J., “Irrational Exuberance” 2nd edition, (2005), p.41.
 See: http://www.fedprimerate.com, retrieved 05/16/2013.
 See: Schiller, R. J., “Irrational Exuberance” 2nd edition, (2005),p.12.
 See: http://www.consumerfed.org, as cited by Schiller, R.J, “Irrational Exuberance” 2nd edition, (2005),p.211.
 See: http://www.standardandpoors.com, retrieved 05/16/2013.
 See: http://arxiv.org/pdf/cond-mat/9905305v1.pdf, retrieved 05/24/2013.
 See: Sornette, D., “Why Stock Markets Crash”, (2004), p.41.
 See: http://www.nyse.com, retrieved 05/23/2013.
 See: http://online.wsj.com, retrieved 05/23/2013.
 See: Sornette, D., “Why Stock Markets Crash”, (2004), p.41.
 See: Schiller, R.J., “Irrational Exuberance” 2nd edition, (2005),p.193.
 See: Scheffer, M. et al., ”Early-warning signals for critical transitions”, (2009).
 See: Scheffer, M. et al., ”Early-warning signals for critical transitions”,(2009).
 Market surveys have found that investors equally use TA and FA when deciding on a strategy in the short-term. For longer horizons FA seems to be preferred. See for example Taylor M.P./ Allan H. (1992).
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