Discrete Dynamics in Nature and Society

Volume 2012 (2012), Article ID 120518, 12 pages

doi:10.1155/2012/120518

Research Article

Power Law Analysis of Financial Index Dynamics

J. Tenreiro Machado,¹ Fernando B. Duarte,² and Gonçalo Monteiro Duarte³

¹Department of Electrical Engineering, Institute of Engineering, 4200-072 Porto, Portugal

²Faculty of Engineering and Natural Sciences, Lusofona University, 1749-024 Lisbon, Portugal

³Faculty of Economics and Management, Lusofona University, 1749-024 Lisbon, Portugal

Received 17 February 2012; Accepted 15 May 2012

Academic Editor: Daniele Fournier-Prunaret

Copyright © 2012 J. Tenreiro Machado et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Power law (PL) and fractional calculus are two faces of phenomena with long memory behavior. This paper applies PL description to analyze different periods of the business cycle. With such purpose the evolution of ten important stock market indices (DAX, Dow Jones, NASDAQ, Nikkei, NYSE, S&P500, SSEC, HSI, TWII, and BSE) over time is studied. An evolutionary algorithm is used for the fitting of the PL parameters. It is observed that the PL curve fitting constitutes a good tool for revealing the signal main characteristics leading to the emergence of the global financial dynamic evolution.

1. Introduction

Business cycles are the usual trend behavior found in the economic activity over a significant period of time (i.e., several months or years). Such fluctuations tend to involve shifts between periods of economic growth (expansions) and periods of stagnation or decline (recessions); see Table 1. The complex reasons behind business cycles are studied by macroeconomics and are mainly grounded on the gears of the economic activity (e.g., monetary policy, business sentiment, inflation). In the United States the business cycle is followed by the National Bureau of Economic Research (NBER), a research organization which is dedicated to promote a greater understanding of how the economy works. The NBER’s Business Cycle Dating Committee defines the official US business cycle’s peaks and troughs. An economy expansion corresponds to a period from a trough to a peak, and a recession corresponds to the period from a peak to a trough. For NBER a recession is defined as “a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real Gross Domestic Product (GDP), real income, employment, industrial production, and wholesale-retail sales’’ [1–3]. Since 2000 there has been two recessions (Figure 1), which are now briefly described.

Table 1: Business cycle reference dates, in US, since 1970.

Period
Main characteristics
Main causes

Dec. 1969 to Nov. 1970
(i)  Followed one of the longest economic expansion in the US history (Feb. 1961 to Dec. 1969)

(ii)  Relatively mild recession
(i)  Fiscal tightening to close the budget deficits of the Vietnam War

(ii)  Monetary tightening to face the rising inflation

Nov. 1973 to Mar. 1975
(i)  Simultaneous increase of inflation and unemployment

(ii)  Long and deep recession
(i)  1973 oil crisis

(ii)  Wage and price control policies implemented to mask inflation pressures and fight unemployment

(iii)  Abnormal long decline in productivity growth

(iv)  Emergence of newly industrialized countries

Jan. 1980 to Jul. 1980 and Jul. 1981 to Nov. 1982
(i)  Conjunction of two recessions, separated by a very short expansion (w-shaped)

(ii)  Deepest and longest recession in the postwar period
(i)  Contractionary monetary policy to control high inflation

Jul. 1990 to Mar. 1991
(i)  Hit much of the world—not particularly deep or long
(i)  Fed tightened monetary policy (Feb. 1988 to May 1989) to counter a rising inflation rate

(ii)  Oil price shock after Iraq invaded Kuwait gave momentum to the starting recession

(iii)  Serious solvency problems among thrift institutions due to savings and loan crisis

(iv)  Consumer pessimism

Mar. 2001 to Nov. 2001
(i)  Ended the longest period of growth in the American history

(ii)  Predicted by economists for years

(iii)  Affected all the developed world
(i)  Collapse of the speculative dot-com bubble

(ii)  Fall in business investments

(iii)  September 11th attacks

Since Dec. 2007
(i)  Worst financial crisis since the Great Depression (1930s)

(ii)  Unprecedented responses by governments and central banks (fiscal stimulus, monetary policy expansion, and institutional bailouts)

(iii)  Ongoing

(iv)  Fears of a new w-shaped recession
(i)  Collapse of the housing bubble

(ii)  Financial crisis

(iii)  Return to tight monetary policy

Figure 1: The temporal evolution of the daily closing value for the DAX, DJI, NASDAQ, Nikkei, NYA, BSE, SSEC, TWII, HIS, and S&P 500 indices and the main crisis, from Jan, 2000 to Dec, 2009.

Economy recessions are the primary factor that drives fluctuations in the volatility of stock returns. It is not surprising that changes in economic activity have strong consequences on stock markets.

Stock values are based on corporate earnings which are greatly determined by the business cycle. Therefore, the stock market growth and the GDP tend to correlate quite well. However, it is clear that the correlation is not direct because of the following.

(i)Stock markets tend to behave in a magnified way when compared with the GDP fluctuations. When the GDP falls/increases, the stock market falls/increases even more.(ii)Stock markets are normally faster to react than the economy and are, therefore, considered by many as a leading indicator of the business cycle. Almost without exception, the stock market turns down prior to recessions and rises before economic recoveries. In fact Siegel [4] shows that out of the 46 recessions from 1802, 42 of them (91.3%) have been preceded (or accompanied) by declines of 8 percent or more in the total stock returns index.

Business cycle forecasting is a popular effort in stock markets not because it is successful, but because the potential gains are so large. In fact, such prevision is a very difficult task and most of the times it is not correct, as illustrated by a famous Samuelson [5] quote ‘‘Wall Street indices predicted nine out of the last five recessions!’’. Therefore, although the stock markets normally identify coming recessions, there is a tendency to be many false alarms. The gains of being able to predict the turning points of the economic cycle are enormous. If an investor could identify the turning points of the economic cycle, he would switch stocks for government bonds before the business downturn begins (stocks fall prior to a recession while treasury bills tend to valorize) and return to stocks when prospects for economic recovery are positive. Nevertheless, if the investor lacks forecasting effort and just follows the established business sentiment about economic activity, he will be buying when prices are high (because everyone is optimistic) and selling when they are low (because everyone is pessimistic) resulting in big losses.

The development of mathematical tools for describing, analyzing and forecasting financial markets has been the subject of considerable research during the last decades, in different perspectives such as in the case of statistics, stochastic systems, signal processing, nonlinear dynamics, and chaos. However, only recently intelligent and evolutionary algorithms were considered for this task, but the results seem promising and motivate further work [6–8]. Bearing these ideas in mind, the present work establishes a link between classical methods, namely those of system dynamics, and intelligent algorithms, through the development of a adaptive trendline scheme with genetic algorithms and is expected to contribute to the improvement of business cycle forecasting practices by developing an intelligent method of analysis of the trend.

The remainder of this paper is as follow. Section 2 presents the data, namely the financial indices, the fundamental concepts adopted in the study, and the methodology of analysis. Finally, Section 3 draws the main conclusions.

2. Financial Data and Methodology of Analysis

In this section we analyze the stock market indices from January 2000 to December 2009. Our data comprises daily close values of stock markets to be denoted as , where is time, is the total number of samples, and . The stock markets in study are listed in Table 2. The data is obtained from the Yahoo Finance website [9] and corresponds to indices in local currencies.

Table 2: The ten stock markets adopted in the study.

Stock market index
Abbreviation
Country

1
Deutscher Aktienindex
DAX
Germany

2
Dow Jones Industrial
DJI
USA

3
NASDAQ
NDX
USA

4
New York Stock Exchange
NYA
USA

5
Standard & Poor's
SP500
USA

6
Tokyo Stock Exchange
NIKKEI
Japan

7
Stock market index in Hong Kong
HSI
Hong Kong

8
Bombay Stock Exchange Index
BSE
India

9
Shanghai Stock Exchange
SSEC
China

10
Stock market index in Taipei
TWII
Taiwan

Figure 1 depicts the time evolution, of daily, closing price of the indices versus time with the well-know noisy, and “chaotic-like’’ characteristics [10].

These signals have a strong variability which makes difficult their direct comparisons in the time domain.

In order to examine the behavior of the signal spectrum, we superimpose a trendline over to the Fourier transform (FT); that is, we approximate the modulus of the FT amplitude through the power law in the frequency domain (PL):

where is the Fourier operator, is the frequency, a positive constant that depends on the signal amplitude, and is the trendline slope [11, 12] presented in Table 3. According to the values of , the signals can exhibit an integer or fractional order behavior.

Table 3: Parameter values for the PL (left) and average parameter values for PL, with (right) for the ten stock markets adopted in the study.

_
_
_
_

1

2

3

4

5

6

7

8

9

10

For example, Figure 2 depicts the amplitude of the FT of the Dow Jones and Nikkei indices and the corresponding PL slope values , , respectively.

(a)

(b)

Figure 2: and the power trendline for the indices Dow Jones (a) and Nikkei (b).

We verify that we get a fractional order spectrum in between the white and pink noises, typical in fractional systems, and corresponding to a considerable volatility.

FT is not capable of characterizing signal variations in a limited time window and leads to a portrait of the overall signal characteristics. Therefore, given the signal volatility it is important to develop an intelligent method capable of capturing evolutions and trends in finite width time windows [13, 14].

In order to examine the behavior of the signal, for small time partitions, a power law in the time domain (PL) trendline is calculated according the following equation:

where represents time and , , , are fitting parameters.

It should be noted that, a priori, there is no formal link between expression (2.1) and (2.2), but this study may clarify any dependence in case it exists.

In this approximation the parameter describes the “magnitude,’’ is related with dynamics of evolution, and and coordinate offsets. Therefore, from the point of view of financial dynamics, the parameter is clearly the most relevant one.

We must mention that PL and fractional dynamics may be manifestations of the same type of phenomena, that is to say, of dynamical systems with long memory. In fact, the power law behavior can emerge even in systems lacking such property [15]. Nevertheless, while the relation between the two faces is not yet clearly understood, the mathematical complexities underlying fractional calculus can be softened with the PL approximation [16].

Based on a visual analysis of the pattern of the indices chart, we see that we can subdivide each of them into several different partitions. According to the pattern of the indices chart we decided to adopt a variable number of the trendlines according with a maximum relative error , defined as

Figure 3 shows the number of trendlines , for the Dow Jones and Nikkei indices, versus the value of the maximum relative error . Obviously, the larger the the smaller the .

Figure 3: Number of trendlines   versus   for the Dow Jones and Nikkei indices.

In our case we consider . Having calculated the PL approximations, for each one of the partitions, we superimpose the corresponding values of the PL trendline over the original data. For example, Figure 4 depicts the partitions and the trendlines approximation for the Dow Jones and the Nikkei indices.

(a)

(b)

Figure 4: The temporal evolution and PL trendline for the the Dow Jones (a) and Nikkei (b) indices from Jan. 2000 to Dec. 2009 with .

For the calculation of the parameters in (2.2), it is adopted a genetic Algorithm (GA). GAs are a class of computational techniques to find approximate solutions in optimization and search problems [17, 18]. GAs are simulated through a population of candidates of size that evolve computationally towards better solutions. Once the genetic representation and the fitness function are defined, the GA proceeds to initialize a population randomly and then to improve them through the repetitive application of mutation, crossover, and selection operators. During the successive iterations, a part or the totality of the population is selected to breed a new generation. Individual solutions are selected through a fitness-based process, where fitter solutions (measured by a fitness function) are usually more likely to be selected. The GA terminates when either the maximum number of generations is produced or a satisfactory fitness level has been reached. In the experiments was considered a GA population of elements, the crossover of all population elements and the adoption of elitism, a mutation probability of 5%, and an evolution with iterations. This scheme leads to a fast convergence and reduced computational time.

For the purpose of checking the convergence of the GA towards nonoptimal values, several executions of the algorithm were performed, and the results compared.

Figure 5 shows the charts of the parameters of the Dow Jones and Nikkei indices, for the PL approximation. Again the charts reveal that for small/large values of we get an high/low number of . By other words, the smaller the number of windows the larger the generalization and scope of the conclusions, but the higher the error . Furthermore, we observe that the estimation of the model parameters is robust since we have only volatile results for small values of .

(a)

(b)

(c)

(d)

Figure 5: Locus of the parameters versus (), based on (2.2), for the Dow Jones (left) and Nikkei (right) indices, from Jan. 2000 to Dec. 2009.

From the point of view of dynamics, parameter is the most relevant one. Figure 6 depicts the variation of the PL parameters through time for the Dow Jones, and Nikkei signals, and the cases of . On the other hand, Figure 7 shows the evolution of the average and standard deviation for the parameter over time (,),    versus for the two indices Dow Jones and Nikkei.

(a)

(b)

(c)

(d)

Figure 6: Values of the parameters versus , for the Dow Jones (left) and Nikkei (right) indices, with from Jan. 2000 to Dec. 2009.

(a)

(b)

Figure 7: Average and standard deviation value for parameter versus , for the Dow Jones (a) and Nikkei (b) indices.

We should note that the power law approximation fits well the time evolution of the stock markets. Since we are doing a splitting of the signal in adequate time windows, an interesting question is if any kind of function could be fitted for any time signal. Several experiments with linear approximations, quadratic polynomials and rational fractions revealed that we could get a good fit with a given approximation for a particular financial index. However, when considering the approximation of all indices, numerical experiments demonstrated problems, both in the resulting plots and the GA convergence. Therefore, further research is necessary to explore the type of functions that constitute good approximations, having in mind both the numerical convergence of the fitting procedure and the characteristics of the resulting plots.

Finally, Table 3 shows the PL parameters and average parameter’s values for PL, with for the ten indices. We verify that the PL leads to a much more detailed description of the signal, being capable of adapting to its time variability, while capturing its trend within the time window under analysis. In this perspective the PL establishes a good compromise between time adaptation and trend estimation.

We can note that while formally there is no relationship between the parameters of the PL and PL, there is some degree of correlation as can be seen in Figure 8 that depicts versus . In fact, we verify not only that varies from negative (economic recession) to positive (economic expansion) for but also the sensitivity of the time model that “dilutes’’ the transients into the final result.

Figure 8: Parameter values versus parameter values, for all indices.

3. Conclusions

Economy cycles are the cumulative result of a plethora of different phenomena. Therefore, financial indices reveal a complex behavior, and their dynamical analysis poses problems not usual in other types of systems. In this paper it was studied a PL trendline as a manifestation of the long memory property of systems with fractional dynamics.

For that purpose we developed an intelligent algorithm with a sliding time window having width proportional to a predefined threshold error. Moreover, for the parameter estimation we adopt a genetic algorithm that demonstrates to pose a low computational load while leading to a fast convergence.

The PL trendline proved to constitute a tool capable of retaining the dynamical properties of the economic cycles while providing a global perspective of its evolution.

References

U. S. Department of Commerce, Bureau of Economic Analysis, 2007, http://www.bea.gov/national/index.htm#gdp

NBER, U. S. business cycle expansions and contractions, 2008, http://www.nber.org/cycles.html

M. D. Bordo and J. Landon-Lane, “Determination of the December 2007 peak in economic activity,” Working Paper Series, 2008, http://www.nber.org/dec2008.pdf.

J. J. Siegel, Stocks for the Long Run: The Definitive Guide to Financial Market Returns and Long-Term Investment Strategies, McGraw-Hill, 1994.

P. Samuelson, “Science and Stocks,” Newsweek, p. 92, 1966.

A. P. Chen and Y. C. Hsu, “Dynamic physical behavior analysis for financial trading decision support,” IEEE Computational Intelligence Magazine, vol. 5, no. 4, pp. 19–23, 2010.

J. Seiffertt and D. Wunsch, “Intelligence in markets: asset pricing, mechanism design, and natural computation,” IEEE Computational Intelligence Magazine, vol. 3, no. 4, pp. 27–30, 2008.

A. Brabazon, M. O'Neill, and I. Dempsey, “An introduction to evolutionary computation in finance,” IEEE Computational Intelligence Magazine, vol. 3, no. 4, pp. 42–55, 2008.

2010, http://finance.yahoo.com

F. B. Duarte, J. A. T. MacHado, and G. M. Duarte, “Dynamics of the Dow Jones and the NASDAQ stock indexes,” Nonlinear Dynamics, vol. 61, no. 4, pp. 691–705, 2010.

X. Gabaix, P. Gopikrishnan, V. Plerou, and E. E. Stanley, “A unified econophysics explanation for the power-law exponents of stock market activity,” Physica A, vol. 382, no. 1, pp. 81–88, 2007.

J. T. Machado, F. B. Duarte, and G. M. Duarte, “Multidimensional scaling analysis of stock market values,” in Proceedings of the 3rd Conference on Nonlinear Science and Complexity, D. Baleanu, Ed., Cankaya University, 2010.

J. T. MacHado, G. M. Duarte, and F. B. Duarte, “Identifying economic periods and crisis with the multidimensional scaling,” Nonlinear Dynamics, vol. 63, no. 4, pp. 611–622, 2011.

Z. Q. Jiang, W. X. Zhou, D. Sornette, R. Woodard, K. Bastiaensen, and P. Cauwels, “Bubble diagnosis and prediction of the 2005–2007 and 2008-2009 Chinese stock market bubbles,” Journal of Economic Behavior & Organization, vol. 74, no. 3, pp. 149–162, 2010.

C. Pellicer-Lostao and R. Lopez-Ruiz, “Transition from exponential to power law income distributions in a chaotic market,” International Journal of Modern Physics C, vol. 22, no. 1, pp. 21–33, 2011.

J. A. T. Machado and A. Galhano, “Statistical fractional dynamics,” ASME Journal of Computational and Nonlinear Dynamics, vol. 6, pp. 73–80, 2008.

D. E. Goldberg, Genetic Algorithms in Serach Optimization and Machine Learning, Addison-Wesley, Reading, Mass, USA, 1989.

J. A. T. Machado, A. M. Galhano, A. M. Oliveira, and J. K. Tar, “Optimal approximation of fractional derivatives through discrete-time fractions using genetic algorithms,” Communications in Nonlinear Science and Numerical Simulation, vol. 15, no. 3, pp. 482–490, 2010.