RELATIVISTIC QUANTUM ECONOPHYSICS – NEW PARADIGMS IN COMPLEX SYSTEMS MODELLING V. Saptsin and V. Soloviev

"Econophysics, or physical economics, already mentioned as a relatively young scientific school, recently celebrated its tenth anniversary. Of course that doesn’t mean that there were no works on the boundary of economics and physics before the econophysics was officially born, howewer the new direction is usually formed only when the certain conditions appear and the necessity to concentrate the scientific forces arises. Quantum econophysics is not an exception. That is why, though the first work according to Gonsales [18], which can be related to the application of quantum mechanical ideas to the economic phenomena, appeared in 1990 [55], we can speak about the birth of the new scientific direction called econophysics only nowadays."

[18] C. P. Goncalves, An Evolutionary Quantum Game Model of Financial Market Dynamics - Theory and Evidence, April 14, 2007, URL http://ma.utexas.edu/mp arc/c/07/07- 89.pdf .

[55] T. Vaga, The Coherent Market Hypothesis, Financial Analysts Journal, November/December, 36–49 ( 1990).

Bubble Hunter

Click on the title to visit the Bubble Hunter Blog, written by:

PIOTR CHWIEJCZAK
I am 33. I graduated from Warsaw University Economist Department in 1996 (M.A.). I spent few years working in several banks as economist/strategist. I was also working as a economic advisor to official bodies Poland and outside Poland. In year 2003/2004 I spent 9 months in Iraq where I was working as advisor to the central banker.

Recent posts include:

The Chinese equity bubble - UPDATE

provides link to more detailed paper by Sornette.

World stock market: approaching trend reversal?

Stanislaw Drozdz and Pawel Oswiencimka in short paper predict that core stocks indexes will face significant correction.

Sugar bubble ready to burst.



"I analyzed sugar#11 future time series between September 2007 and September 3 2009. The y axis is logarithmically scaled so that the exponential function would appear as a straight line. LPPL fit exhibit upward curvature which is clear evidence that the prices were growing “super-exponentially”. The projected crash dates are September 5-15 .It must be noted that a good fit of the model to the data series is not a 100% certainty for a crash, but it clearly points at a bubble formation."

Shanghai Bubble Bursts

According to New Scientist:

"WITH 20/20 hindsight, financial crashes seem inevitable, yet we never see them coming. Now a team of physicists and financiers have bucked the trend by successfully predicting a steep fall in the Shanghai Stock Exchange.

Their model, which employs concepts from the physics of complex atomic systems, was developed by Didier Sornette of the Financial Crisis Observatory in Zurich, Switzerland, and Wei-Xing Zhou of the East China University of Science and Technology in Shanghai."

Are the Financial Markets Becoming More Efficient?

With the advent of negotiated commissions in 1975 and growing use of increasingly powerful computer based trading systems, the markets appear to be increasingly efficient. One way to measure market efficiency is by examining conditional returns: if conditional returns are trend persistent, profits can be made by betting with the trend; if conditional returns are mean regressive, profits can be had by betting on trend reversals. If conditional returns are too small to profit from, then the markets are efficient.

Our Bifurcation Parameter (BP) is a measure of the degree of trend persistence (when positive) or mean regression (when negative). It is defined as the 200 day sum of daily returns, R(t+1) after prior day returns in the interval 0.5% < R(t) < +3.5% minus the sum of daily returns after previous day returns in the interval -3.5% < R(0) < -0.5%. When this measure is greater than +10%, we consider the market to be trend persistent; when less than -10%, the market is mean regressive. Between these levels, the market is in a relatively efficient state.

Figure 1 illustrates the NASDAQ BP dating back to 1971. For much of this period, the NASDAQ BP was highly trend persistent, and hardly ever mean regressive with respect to daily returns. However, beginning roughly in the year 2000, the NASDAQ has become more efficient and more recently mean regressive, a highly volatile, disordered market state.


Figure 1. The NASDAQ has become more efficient over the past decade and more recently has become mean regressive. (Click on chart to expand).

Figure 2 summarizes the returns for each key market state. The mean regressive state has has the least data and is not statistically significant at the 95% level. The bifurcated bull and bear states are highly statistically significant. Statistical significance is based on excluding the probability that the returns in a particular state are the same as for the efficient state.


Figure 2. The NASDAQ returns in the bull and bear state are statistically significant. (Click on chart to expand).

The Dow Jones Industrial Average has also become more efficient since about 1975. Figure 3 summarizes the Bifurcation Parameter dating back to the Crash of 1929. During the post World War II period the markets were highly trend persistent as the economy boomed. However, in the post 1975 period, the DJIA BP has also steadily declined and currently remains at levels not seen since the Crash of 1929.


Figure 3. The DJIA has become more efficient since 1975 and has recently become highly mean regressive. (Click on chart to expand).

Figure 4 summarizes the returns and their statistical significance for key DJIA market states. The mean regressive state is not statistically significant due to its high volatility and relatively little data. However the DJIA bull and bear states are highly statistically significant.


Figure 4. The DJIA returns in the bull and bear state are statistically significant. (Click on chart to expand).

Japan's NIKKEI Index provides an example of what to expect from an efficient market. It has been efficient on average since about 1991 (based on a quadratic fit to the NIKKEI Bifurcation Parameter). Figure 5 summarizes the NIKKEI Bifurcation Parameter dating back to 1984.


Figure 5. The NIKKEI has been fairly efficient since 1990. (Click on chart to expand).

Figure 6 summarizes the returns and their statistical significance for key NIKKEI market states. The mean regressive state is not statistically significant due to its high volatility and relatively little data. The DJIA bull and bear states are also not statistically significant. Therefore as the markets become more efficient, there will be fewer profitable trading opportunities.


Figure 6. The NIKKEI returns in the bull and bear state are not statistically significant. (Click on chart to expand).

Dow Jones Industrials Remain in Over Reaction, Mean Regressive State

The Bifurcation Parameter (BP) for the Dow Jones Industrial Average (DJIA) remains in negative territory at -38%. This market has been in an over reaction, mean regressive state that has often accompanied crisis markets. The BP is defined here.

Figure 1 summarizes the DJIA BP dating back to the Crash of 1929. For most of this period the BP has been indicating a bifurcated market in which investor sentiment is prone to under react and price is trend persistent. However, with the advent of computerized trading and negotiated commissions in 1975 the markets have become more efficient. An efficient market is defined here as one in which there is neither trend persistence nor mean regression is large enough to provide significant trading opportunities.


Figure 1. The Dow Jones Industrial Average Bifurcation Parameter suggests that the market has become more efficient since 1975. (Click on chart to enlarge).

The average return for the DJIA as a function of the average value of the BP for each market state is summarized in Figure 2. The t-test for each state provides the probability that the returns for a given state are equivalent to those from the efficient market state (when -10% < BP < +10%). Note that the crisis state (BP < -10%) is not statistically significant at the 95% level due to the limited amount of data, the recent market rally and the high volatility of this state. In contrast, the bifurcated bull state (BP >= +10% and R(0) >= 0) is statistically highly significant (p = 1.6E-9). Likewise the bear state (BP < -10% and R(0) < 0) is highly significant (p = 1.5E-5). However, if the markets have become more efficient, then these trend persistent states will be evident less frequently.


Figure 2. The Dow Jones Industrial Average market returns for the bull and bear state have been highly statistically significant. (Click on chart to enlarge).

NASDAQ Remains in Mean Regressive State

The Bifurcation Parameter for the NASDAQ Composite Index slipped back to -22% over the past few weeks (white arrow on the chart). Figure 1 summarizes the average daily return expected from each of the four key market states expected from the Bifurcation Parameter (and prior day return, R(0)).

NASDAQ Composite Index Returns for States Predicted by the NASDAQ Bifurcation Parameter (click on chart to enlarge)

The statistical significance of each state is based on a comparison with the efficient state (when -10% < BP < +10%). The statistical significance of the Crisis Market State is questionable at p = 0.1 which is below the 95% confidence level benchmark and approximately the same as found for the Dow Industrial as briefed in Zurich and shown on the briefing slides for that talk. In contrast, the Bull and Bear states are both highly statistically significant. For the Zurich talk, daily returns were annualized and the t-test was based on comparing each state with the buy and hold benchmark (as opposed to the efficient market state).

NASDAQ Leading the Way Out of Crisis Conditions?

Figure 1 summarizes the bifurcation parameter for the NASDAQ Composite Index. The bifurcation parameter has shown steady improvement and has now risen above the -10% threshold. This suggests that the worst of the mean regressive crisis market may be behind us. While the indicator could fluctuate around current levels and create whipsaw results, the big picture is that there has been steady improvement toward a more efficient market state.


Figure 1. The NASDAQ Index is Becoming Less Mean Regressive

The risk and reward profile of the NASDAQ Index is summarized in Figure 2. The Efficient Market State (-10% < BP < +10%) has exhibited an annualized return of 16% with an annualized volatility of 22%. The prior Bull States (when the BP > 10% and R(0) > 0) show a 60% annualized return with moderate risk. The Bear States (when the BP > 10% and R(0) < 0) show a -30% annualized return with 20% annualized risk.


Figure 2. The NASDAQ Index Risk Reward Profiles

To avoid whip saw trading, look for the Bifurcation Parameter to become positive before changing positions in the current environment. While the NASDAQ has improved, the Dow Jones Industrial Average and the S&P Composite Index remain in the crisis state.

Hang Seng Index: Trend Persistent (Bull and Bear States)

Figure 1 summarizes the bifurcation parameter for the Hang Seng Index dating back to 1987. During this period there has been very little mean regressive market action. Even recently during the global credit crisis, the Hang Seng did not show the crisis state behavior that the US equity markets exhibited.


Figure 1. The Hang Seng Index has been Trend Persistent

The risk and reward profile of the Hang Seng Index is summarized in Figure 2. The Bull State (when the BP > 10% and R(0) > 0) shows a greater than 50% annualized return with moderate risk. The Bear State (when the BP > 10% and R(0) < 0) shows a -20% annualized return with 32% annualized risk.


Figure 2. The Hang Seng Index Risk Reward Profiles

A short term trend following strategy would appear to be effective in the current environment.

According to Didier Sornette, et. al.: Chinese Equity Bubble: Ready to Burst

July 10, 2009

Amid the current financial crisis, there has been one equity index beating all others: the Shanghai Composite. Our analysis of this main Chinese equity index shows clear signatures of a bubble build up and we go on to predict its most likely crash date: July 17-27, 2009 (20%/80% quantile confidence interval).

ETH Zurich Workshop Presentation: A Financial Market Bifurcation Parameter

Can financial market crises be predicted? We propose a Bifurcation Parameter in this regard.


BACKGROUND: Weidlich proposes the Ising Model to describe polarization of opinions in social groups. Haken's model includes the Langevin equation of Brownian motion as a special case and references Weidlich's work as an example of more ordered states in social systems. Vaga applies Weidlich and Haken's state transition concepts to formulate the Coherent Market Hypothesis. Vaga and Nawrocki develop a novel bifurcation parameter and analyze coherent, chaotic, efficient and disordered (crisis) market states.


The Coherent Market Hypothesis provides the theoretical basis for defining a quantitative bifurcation parameter, a potential indicator crisis situations in the financial markets.


The empirical daily conditional return map from 1929 to present illustrates bullish and bearish equilibrium states (where the return map crosses zero). The slope of the conditional return map in the neighborhood of moderate returns is positive with high statistical significance.


The slope of the conditional return (CR) map governs the bifurcation process from the linear, disordered state to the more structured bull and bear states.


The bifurcation parameter is the 200 day sum of conditional returns after moderate positive returns minus the 200 day sum of conditional returns after moderate negative returns. This parameter is related to the slope of the CR map.


The Bifurcation Parameter (BP) has dropped well below -10% in crisis markets such the Crash of 1929 and Great Depression Era. In contrast, the BP didn't drop below -10% at all in the post WW II Era (1946-1975). Since the advent of computerized trading and negotiated commissions in the mid-1970s, the BP has indicated a more efficient market, though recently this indicator has fallen to levels not seen since the Great Depression Era.


In the 1929 to 1939 period, the bifurcation parameter fell well below -10% and remained there on three occasions, each of which resulted in significant market declines


In the 1999 to 2009 period there were two large declines in the Bifurcation Parameter below -10%, one coincided with rising stock prices and the other with a large decline to date.


Periods with a negative BP have a significant negative bias in the conditional return map.


Periods with a BP greater than +10% have a higher degree of bull and bear trend persistence.


Market state definitions can be based solely on the Bifurcation Parameter.


Ordered markets, including both coherent and chaotic states, outperform efficient market periods, while disordered (crisis) markets have underperformed by a large degree.


Ordered markets can be decomposed into coherent bull markets (when the prior day return is >0) or chaotic markets when the prior day return is negative.


Coherent, chaotic, efficient and crisis markets have widely varying risk and reward profiles.


The Crash of 1929 and Great Depression Era was highly volatile.


The post World War II Era enjoyed a high degree of trend persistent, coherent and chaotic markets.


Since the advent of negotiated commissions in 1975, the markets have become more efficient on average.


Returns in coherent and chaotic markets are highly statistically significant. Disordered markets (mean regressive reversals after positive returns) are also statistically significant. However due to the high volatility and relatively limited amount of data, crisis market returns are only significant to the 90% level.


The Bifurcation Parameter provides a statistically significant indicator of the coherent and chaotic market states predicted by the Coherent Market Hypothesis. However, due to the extreme volatility and limited number of crisis markets the significance of this state has only been partially established, i.e. reversals of prior day price advances.




BACKUP CHARTS



The NASDAQ Composite Index exhibited a high degree of coherence from 1971 through the year 2000. It is currently in a disordered state.



The S&P500 Index has exhibited large upside reversals in the recent mean regressive market.

Over Reaction, Disordered Market Continues

We introduce an Efficient Market state, defined as -10% < Bifurcation Parameter < +10%. This represents a market where there isn't much over reaction or under reaction to news. We also update prior coherent and chaotic market state definitions, requiring the Bifurcation Parameter to be >= +10%. Therefore the Coherent and Chaotic markets clearly represent under reaction situations and trend persistent states. We also use the prior day return, R(t) to differentiate between coherent (R(t)>=0) and chaotic (R(t)<0) states. These definitions and associated risk and returns since July 1929 are summmarized as follows:

Coherent Bull Markets
Bifurcation Parameter >= +10%
R(t) >= 0 (prior day return is positive)
RETURN 37.94%
RISK 15.05%
% TIME 24.16%

Efficient Markets
-10% < Bifurcation Parameter < +10%
RETURN 6.16%
RISK 14.85%
% TIME 45.25%

Chaotic Markets
Bifurcation Parameter > +10%
R(t) < 0 (prior day return is negative)
RETURN -13.50%
RISK 17.87%
% TIME 22.15%

Disordered Markets
Bifurcation Parameter < -10%
RETURN -17.17%
RISK 36.65%
% TIME 8.43%

International Workshop on Coping with Crises in Complex Socio-Economic Systems

ETH Zurich (Switzerland), June 8-12, 2009
Monday, June 8, 2009:
15:20 - 15:45 CAB G 51
Tonis Vaga: A Financial Market Bifurcation Parameter

Preliminary Program

Poster Presentations

Organizers

Kay Axhausen
Lars-Erik Cederman
Dirk Helbing (Coordinator)
Hans Jürgen Herrmann
Frank Schweitzer
Didier Sornette

Social systems typically feature crises, i.e. unstable and dangerous situations that are characterized by abrupt and large-scale changes. Such disruptions are very hard to predict with any precision and even harder to control. Indeed, crises often convey an impression that key decision makers have lost control and that events unfold in an unstoppable and even catastrophic way. Examples include environmental crises, the collapse of transportation systems, as well as financial and social crises such as poverty, social conflicts or wars.

These and other issues will be addressed during the meeting, which combines elements of an interdisciplinary workshop with a think tank and a summer school for young scientists. Scientists and students interested in participating in this workshop are asked to send an e-mail to Lubos Buzna (lbuzna@ethz.ch) or Amin Mazloumian (amin@gess.ethz.ch) to be included in the e-mail distribution list of this workshop.

I look forward to meeting you!
Dirk Helbing, on behalf of the organizing committee.

CURRENT MARKET: DISORDERED STATE

(click on image to expand)

During the past 12 months the Dow Industrials have had an even lower return and higher risk than the average for prior extremely disordered markets. The high volatility of extremely disordered markets includes large swings both up and down. While the stimulus and bailout programs should provide the credit necessary to eventually restore normal market structure, so far the quantitative evidence is consistent with a disordered market state.

Chaos theory and the current financial crisis

Years ago, in a letter to the editor of Physics Today (February, 1979) we noted that the “market may be considered an open system in which an adequate flow of money will effect a transition from disorder (random walk) to order (cooperative or crowd behavior).” Open systems in the physical sciences require a flow of energy to maintain an ordered state far from thermal equilibrium. For example, a laser requires energy to be pumped continuously to maintain a coherent state. In the financial markets, price stability requires a flow of money or credit. In the Great Depression, credit became scarce as the bubble in stock prices unwound after the "Roaring Twenties." The current credit crisis involves the unwinding of the housing bubble and associated derivative securities.

We define a market “attractor” as a conditional return map, i.e. the average return on the day after a prior day return, R(T-1), that falls into one of five intervals:


small price changes [-0.5% < R(T-1) < +0.5%]
moderate price increases [+0.5% < R(T-1) < +3.5%]
moderate price declines [-0.5% > R(T-1) > -3.5%]
large price increase [+3.5% < R(T-1)]
large price declines [-3.5%] > R(T-1)]

Figure 1 summarizes the average conditional return map for the Dow Jones Industrial Average over the 80 year period from 1929 to 2009. A nonlinear third order polynomial fit is shown and illustrates that the market has been trend persistent on average over this period. The slope of the return map is positive in the region of moderate returns.


Figure 1. Over the past eighty years the Dow Jones Industrial Average has been governed on average by a coherent, trend persistent dynamic.

The conditional return map in Figure 1 illustrates a bistable attractor for the market. Moderate positive returns are followed on average by further positive returns. Similarly, moderate negative returns are followed on average by further negative returns. These drifts are toward dynamic equilibrium points (where the return map crosses zero) far from the market’s long term average daily return.

Next, we identify state transitions from a mean regressive market attractor to a bistable state attractor. First we define a bifurcation parameter as the sum over 200 days of conditional returns following moderate price increases (as defined above), minus the sum over 200 days of conditional returns after moderate price declines. In a mean regressive market, where the return map has a negative slope, this metric is negative whereas in a trend persistent market it is positive. The market attractor bifurcates as this measure crosses zero.

The bifurcation parameter is plotted in Figure 2. The most significant mean regressive markets occurred in the Great Depression era of the 1930s, though there were some wild swings in this indicator. From the 1940s to about 1980, the market was primarily in a trend persistent state and fluctuations of the bifurcation parameter were primarily around a positive mean. The further the bifurcation parameter deviates from zero, the better the opportunities for short term trading: in the Great Depression era a mean reversion strategy would have offered the best chance for success; from 1940 to 1980, a trend following strategy had the odds in its favor. However, more recently with the advent of computerized trading and negotiated commissions, the market has become more efficient, with less opportunity for trading.


Figure 2. The bifurcation parameter is negative in mean reverting market states and positive in trend persistent coherent markets, reflecting the slope of the conditional return map for moderate returns.


Figure 3 illustrates the market return map or attractor for market periods between 1929 and 2009 when the bifurcation parameter is negative. In this situation, moderate positive returns are followed on average by negative returns on the following day; moderate negative returns are followed by positive returns on average.


Figure 3. The market is mean regressive when the slope of the conditional return map and the bifurcation parameter are negative.

Recently the bifurcation parameter has dropped deeply into negative territory. This is an unusual development since this indicator hasn’t fallen this far since the Great Depression era. A short term trading strategy designed to profit from the market’s regression to the mean after moderate returns is appropriate in this market state. A strategy of avoiding equity positions entirely when the bifurcation parameter drops below -10% is illustrated in Figure 4. This straategy would have outperformed a buy and hold both in the Crash of 1929 and also successfully avoided much of the recent market meltdown. However, there is no assurance as to how it will work in the future particularly as it is based on a lagging indicator of market dynamics.


Figure 4. Avoiding mean reverting markets (negative bifurcation parameter) has shown profitable back testing results, but may not work in future markets.

kaoseteooria ja majanduskriis

Leo Võhandu, TTÜ emeriitprofessor



For complete article, click here

Kaos tähendab igapäevakeeles täielikku segadust ja korralagedust. Füüsikas tähendab see mingi süsteemi osiste vastastikust mittelineaarset mõjutamist koos kõigi või peaaegu kõigi süsteemsete liikumiste ebastabiilsusega. Keerulisevõitu väljend on, aga asja olemuse seletab ilusasti ära.

Et füüsikud ja mehaanikud kaoses päris hästi orienteeruvad, siis heietab nii mõnigi lootust, et ehk aitab kaoseteooria meil kriisiolukorrast pääseda. Kiiret lahendust see teooria muidugi pakkuda ei saa, aga üht-teist kasulikku majanduse ja valuutaturgude jaoks võib sealt leida küll.

Kõigepealt märgime, et eesti keeles on ilmunud kahe akadeemiku sulest kaks head ja loetavat raamatut kaoseteooria radadelt. Esimene neist pärineb Tartu Ülikooli mehaanika emeriitprofessori Ülo Lepiku sulest – «Kaos ja kord» (1997). Teise ja hoopis kopsakama raamatu kirjutasid akadeemikud Ülo Lepik ja Jüri Engelbrecht paar aastat hiljem. Selle pealkiri «Kaoseraamat» on küll lühike, aga sisu on see-eest õige huvitav.

...

Kummalisel kombel on just kaks Eestiga tugevalt seotud meest tõestanud, et kaoseteooriast on rikkaks saamise mõttes õige palju kasu.

Veidi vanem neist kahest kannab nime Tõnis Vaga. Usutavasti on ta praegu maailmas kõige tsiteeritum eestlasest majandusteadlane. See on mees, kes 1994. aastal avaldas põhjapaneva ingliskeelse raamatu: Tonis Vaga «Profiting from Chaos» («Kaosest tulu teenimine»). Mul õnnestus see raamat poolkogemata 1995. aastal ühest Tallinna raamatupoest leida ja osta. Alguses arvasin, et on tegu mõne lõunaameeriklasega, kuid järsku taipasin, et selle nime taga võib olla hoopis eestlane Tõnis. Asi sai kohe klaariks, kui vaatasin raamatu pühenduste lehekülge. Üks pühendustest oli tütrele nimega Maie. Nii et oligi eestlane. Pärastine internetikontroll tõestas kah, et tegu on praegu 60-aastase üpris tragi USA eesti kogukonna liikmega.

Vaga on hariduselt füüsik, kuid 1979. aastal avaldas ta ülimalt olulise artikli aktsiaturu hindade kõikumisest ja hiljem ka nn koherentsete turgude teooria, mis mõlemad äratasid majandusteadlaste hulgas suurt tähelepanu. Nii ta asuski oma ideid majanduses realiseerima suurfirma Booz Allen Hamilton vanempartnerina. Muide, Vaga raamatut ei leia te ühestki Eesti raamatukogust, küll aga on see vabalt kõigile kättesaadav Google’i elektronraamatute hulgas.

Teine Eestist pärit mees, kelle kirjutatud raamatud valuutaturgudel kauplemisest USAs õige menukad on, on Alexander Elder. Vagast aasta nooremana sündis ta küll Leningradis, kuid elas Eestis ja õppis Tartus meditsiini. Ta asus tööle kalalaeva arstina. Aafrika ranna lähedal kala püüdes hüppas ta laevalt ära läände ning sai USAs poliitilise varjupaiga. Elder töötas psühhiaatrina New Yorgis ja õpetas muide ka Columbia Ülikoolis.

Psühhiaatrina pakkus valuutaturg talle omamoodi väljakutset ning nüüdseks on ta kirjutanud paraja paki väga menukaid raamatuid valuutaturgudel kauplemisest. Neid raamatuid on tõlgitud 12 keelde. Tartu Ülikooli raamatukogus on Elderi raamatutest olemas viis, Tallinna raamatukogudes mitte ühtegi...

Nonlinear and Chaotic Dynamics and its Application to Historical Financial Markets

Hartmut Kiehling*

For complete article click here.

Abstract: For roughly 15 years, economic research has been involved with chaotic systems. During these years chaos theory took a firm place in science, although the enthusiasm of the first decade was followed by a more subdued kind of consideration. This might be the time to sum up some of the results and to develop some ideas concerning possible applications of chaos theory to economic history (and its theory). Since a good portion of the chaos research that has been done until now deals with financial markets, we will consider that section of economics.

* Address all communications to Hartmut Kiehling, Heerstraße 9, D-81247 München,
Tel. +49-(0)89-8116379, Fax. +49-(0)89-8110189, e-mail: 101520.2007@compu-serve.com, 0898110189@t-online.de.

T. Vaga published his Coherent Market Hypothesis as a nonlinear statistical model. He distinguishes four market phases: random walk, transition, chaotic markets, and coherent markets. Each one is characterized by different kinds of attitudes and the mutual influence of investors. The model follows the psychological theory of social imitation, but is formulated mathematically. 15

Scientific Frontiers and Technical Analysis

Kevin P. Hanley, CMT

Abstract

Are there scientific foundations to Technical Analysis (TA) or is it a pseudo-science? Academia, embracing the Random Walk Theory, the Efficient Market Hypothesis (EMH) and Modern Portfolio Theory (MPT) has argued the latter for some 20 years or more. In fact, according to current orthodoxy, both TA and Fundamental Analysis are fruitless distractions and cannot add value. The advent of Behavioral Science has illuminated some of the flaws in the standard model. Andrew W. Lo’s Adaptive Markets Hypothesis reconciles efficient markets with human behavior by taking an evolutionary perspective. According to Lo, markets are driven by competition, adaptation, and natural selection. What is missing is a more accurate and comprehensive model of the market itself. Chaos and Complex system theories provide a more comprehensive understanding of market behavior. The markets can be seen as chaotic, complex, self-organizing, evolving and adaptive, driven by human behavior and psychology. Patterns in the market are emergent properties. Identifying these patterns has predictive value, but certainties must be left behind; only probabilities remain. TA, shown to be the inductive science of financial markets, is an essential tool for identifying these emergent properties and analyzing their probabilities. Lastly, so that the science of TA may advance, the field must distinguish between scientific, empirically based, market analysis theory and the categories of interpretation and practical trading strategies.

Chapter 17. Hybrid Intelligent Decision Support Systems and Applications for Risk Analysis and Discovery of Evolving Economic Clusters in Europe

N. Kasabov , L. Erzegovesi , M. Fedrizzi , A. Beber , and D. Deng

Dept. of Information Science, Univ. of Otago, Dunedin, New Zealand.
E-mail: nkasabov, ddeng@infoscience.otago.ac.nz
Dept. of Informatics and Faculty of Economics, Univ. of Trento, Italy

For complete paper click here.

The goal of this project is to develop a computational model for analyzing and anticipating signals of abrupt changes of volatility in financial markets. The system will be aimed at assessing the possibility of speculative attacks against specific EMU member countries, prospective EMU members or the EMU area as a whole. Potential users of the system include monetary authorities, asset managers, traders on money, debt, currency and stock markets and corporate financial managers.

The conceptual model underlying the computational model will be derived from a representation of financial markets as complex dynamic systems, whose stochastic behavior is influenced by exogenous shocks and endogenous uncertainty, the latter caused by interaction among market participants (degree of consensus and tendency to crowd behavior). Inspiration for this approach came from a paper by Tonis Vaga ([50]).

Portfolio optimization with a neural network implementation of the coherent market hypothesis

Manfred Steiner and Hans-Georg Wittkemper

Westfälische Wilhelms-Universität Munster, Lehrstuhl für Betriebswirtschaftslehre, Schwerpunkt Finanzierung, Am Stadtgraben 13–15, D-48143, Münster, Germany

Abstract

Capital market research seems to be widely governed by traditional static linear models like arbitrage pricing theory and capital asset pricing model, though there is some evidence that better results can be achieved using nonlinear approaches. In this study we described a portfolio optimization model based on artificial neural networks embedded in the framework of a nonlinear dynamic capital market model, the coherent market hypothesis. The main advantage of this theory is that it drops the premise of rational investors and therefore relaxes the precondition of approximately normally distributed stock returns. Neural networks are used to estimate the return distributions in order to forecast the fundamental situation and the level of group behavior of the specific stocks. On the basis of these forecasts the relative stock performance is predicted and used to manage stock portfolios, In a simulation with out-of-sample data from 1991–1994 a portfolio constructed from the eight best ranked stocks achieved an annual return rate about 25% higher than that of the market portfolio and one built from the eight worst ranked stocks attained a return about 25% lower than the market portfolio's return rate. A hedging strategy based on the two aforementioned portfolios leads to a consistently positive annual return of about 25% regardless of the movements of the market portfolio with only 41% of the risk of a buy and hold strategy in the market portfolio.

Advanced School of Economics Ca’ Foscari_University of Venice

Exploring Information Mirages
in a Simulated Multi‐Agent Stock Market


Paolo Tasca

Advanced School of Economics, Ca’ Foscari University of Venice
Visiting Fellow Chair of Systems Design, ETH Zürich

First Draft: September 2008

For complete paper click here.

1. Introduction

In this paper we analyze the financial price dynamics emerging from the heterogeneous behaviours of traders interacting in an experimental asset market in presence of asymmetric information. The understanding of the behavior of partially informed agents in experimental settings is a critical step toward understanding behavior in real markets.

Access to qualitative private information gives the traders the opportunity to exploit a dominant position when trading with uninformed agents. This in turn motivates not only the search for information but also the communication of misleading information. For the uninformed traders, a situation of general uncertainty may also lead to imitation and ultimately to herding behaviour. As Grossman (1976) has observed, when confidence in fundamentals disappears, naive imitative behaviour may actually be the best option.

According to the theories of information aggregation (Grossman 1976, 1981; Grossman and Stiglitz 1980; Jordan 1982; Diamond and Verecchia 1981; Verecchia 1982), traders have and use different information about the value of assets and through the process of their aggregation, market prices effectively reveal all the information present in the market. Then, in equilibrium traders cannot learn nothing more than prices. In line with the rational expectations (RE) hypothesis, aggregation of diverse information is in general difficult because no single agent possesses full information. Traders can identify the state of nature with certainty only by sharing their individual information in the process of trading. Plott & Sunder (1982) and Forsythe, Palfrey & Plott (1982) study markets with insiders and uninformed traders. They show that the equilibrium prices do reveal insider information after repetition of experiments and conclude that the markets disseminate information efficiently. Plott & Sunder (1982) further show that convergence to the rational expectation equilibrium (REE) occurs in markets that pays diverse dividends to different traders. They attribute the success of the RE model to the fact that traders learn about the equilibrium price and the state simultaneously from market conditions. The results by Plott & Sunder (1988) and Forsythe & Lundholm (1990), on the other hand, show that a market aggregates diverse information efficiently only under certain conditions: identical preferences, common knowledge of the dividend structure, complete contingent claims. These studies provide examples of failure of the RE model and suggest that information aggregation is a more complicated situation. In another related study, O'Brien & Srivastava (1991) find that market efficiency in terms of full information aggregation depends on complexity of the market. In particular, complexity is induced by market parameters such as the number of stocks and the number of periods in the markets.

In a variety of situations the market may actually fail to aggregate information correctly. Salient reasons are information mirages and bubbles (see Camerer C. 1989, Camerer C. and Weigelt, 1991), information traps (see Nöth et al., 1999), and pricemanipulations (see Veiga and Vorsatz, 2008).

In this paper we investigate whether, in a market composed by informed and uninformed agents, uninformed agents may overreact to uninformative trades during the process of information aggregation. Once an agent occurs in such a mistake, she may trade as informed trader causing other traders to wrongly infer that she is an insiders. The misleading path of market prices resulting from such mistakes is what Camerer and Weigelt (1991) has referred to be “price mirages” because prices reveal information which is not really there. Information mirages is an important phenomenon to be analyzed as one explicative cause of some well known stylized facts in financial markets such as excess volatility (Shiller R.J., 1981). As Fisher Black (1986), French and Roll (1984) have considered, volatility of asset prices may be induced by traders overreaction to trades that are not informative, creating self-generated information mirages. We can imagine for example that just by chance, in the first daily trading sessions the most part of the orders are on the sell side. Uninformed traders entering the market later, may reasonable infer that the market sentiment is negative and may be induced to sell. Thus the market price should fall. Others uninformed traders who pay attention on the recent price path may be attracted by the price drop and be induced to enter the market on the sell side as well. This cycle exactly describe what we mean by an information mirage: a sort of mini-bubble which is typically temporary, and possibly small in size. Imitative behaviour may be responsible for a significant proportion of the price volatility observable in real-world asset markets: in inferring information from the trades of others, traders sometimes go wrong and their errors cause others to overreact, creating price paths that falsely reveal information that no one has.

Previous studies (e.g. Camerer and Weigelt, 1991) consider the dynamics of price mirages in the short run (few minutes). Whereas, in this paper we will analyse this phenomenon in the long run (around 1200 trading days): a sufficiently large horizon during which mini-bubbles may grow into big-bubbles.

2. Artificial Financial Market with Noisy and Insider Traders

Information mirages are difficult to detect in natural data because researchers usually do not know what information traders had at any point in time, so it is difficult to know whether prices incorporate all information or not. Instead, in the artificial financial market introduced here we model the flow through which information enter the market allowing the existence of asymmetric information. This arise the problem of what Fischer Black (1986) has referred to as noise trading:

“Noise trading is trading on noise as if it were information. People who trade on noise are willing to trade even though from an objective point of view they would be better off not trading. Perhaps they think the noise they are trading on is information. Or perhaps they just like to trade” (Black F., 1986, p.531)

The experimental approach is ideally suited to investigations of this kind, since it is possible to control both the structure of the market and the signals through which information is disseminated. Two classes of investors will be allow to trade contemporaneously in the market: insider traders and noisy traders. Insider traders, quickly will trade on the received unbiased signals revealing the variation of asset’s true fundamental value. While, noisy traders will behave as boundedly rational agents. They will trade upon indications of external biased signals and will be influenced by other investors’ sentiments. This lead us away from the Efficient Market Hypothesis (EMH) towards the Adaptive Market Hypothesis (AMH)1 and Coherent Market Hypothesis (CMH) of Vaga T. (1990) through the theory of social imitation (Callen,
Shapiro 1974) and the Ising model.

Market prices will be the product of the interplay between insider traders (called “rational arbitrageurs” in Shiller model, 1984) and noisy traders operating under different decision rules. With continuous information flows, the model encourage the interchange of the role between insiders and noisy traders. Insiders, making ex ante rational trades may nevertheless lose money ex post on any given trade. In real financial markets, investors may trade on the right side of the market performing as insider once they receive the right signal. But they can frequently be engaged in noise trades when receiving signals not carrying the true state of nature.

We frame the model into two classes of rules: microstructure rules and behavioral rules. Microstructure rules are all those ones describing the system design and the mechanisms characterizing the functionality of the market. Behavioral rules instead, describe the agents decisions models.

HIPÓTESIS DEL MERCADO COHERENTE

CARACTERÍSTICAS

La HMC fue desarrollada por Vaga (1990) y explica que elmercado puede presentartransiciones de un estado depaseo aleatorio (desordenado)a periodos de comportamientoen masa como en los mercadoscoherentes y caóticos (ordenados). Cerca del umbral críticode transición, el mercado esaltamente susceptible al ruido(noticias de política o deaspectos económicos aleatorios). Así, los mercados vistosa través de la HMC representansistemas complejos 8 y une a lasdos hipótesis vistas ante-riormente: en ocasiones el mercado se podría comportar deforma caótica y en otrassituaciones podría evolucionarde forma aleatoria.