# Power laws in market capitalization during the dot-com and Shanghai bubble periods

- 1.6k Downloads

## Abstract

The distributions of market capitalization across stocks listed in the NASDAQ and Shanghai stock exchanges have power law tails. The power law exponents associated with these distributions fluctuate around one, but show a substantial decline during the dot-com bubble in 1997–2000 and the Shanghai bubble in 2007. In this paper, we show that the observed decline in the power law exponents is closely related to the deviation of the market values of stocks from their fundamental values. Specifically, we regress market capitalization of individual stocks on financial variables, such as sales, profits, and asset sizes, using the entire sample period (1990–2015) to identify variables with substantial contributions to fluctuations in fundamentals. Based on the regression results for stocks listed in the NASDAQ, we argue that the fundamental value of a company is well captured by the value of its net asset; therefore, a price-to-book ratio (PBR, P/B Ratio) is a good measure of the deviation from fundamentals. We show that the PBR distribution across stocks listed in the NASDAQ has a much heavier upper tail in 1997 than in the other years, suggesting that stock prices deviate from fundamentals for a limited number of stocks constituting the tail part of the PBR distribution. However, we fail to obtain a similar result for Shanghai stocks.

## Keywords

Power law Zipf’s law Market capitalization Asset bubble Stock market Econophysics## JEL Classification

G010 D300## 1 Introduction

Since B. Mandelbrot identified the fractal structure of price fluctuations in asset markets in 1963 Mandelbrot (1963), statistical physicists have been investigating the economic mechanism through which a fractal structure emerges. Power laws are an important characteristic in the fractal structure. For example, some studies found that the size distribution of asset price fluctuations follows power law Mantegna and Stanley (2000), Mizuno et al. (2003). In addition, it is shown that firm size distribution (e.g., the distribution of sales across firms) also follows power law Stanley et al. (1995), Axtell (2001), Okuyama et al. (1999), Mizuno et al. (2012), Clementi and Gallegati (2016). The power law exponent associated with firm size distributions is close to one over the last 30 years in many countries Mizuno et al. (2002), Fujimoto et al. (2011). The situation in which the exponent is equal to one is special in that it is the critical point between the oligopolistic phase and the pseudo-equal phase Aoyama (2010). If the power law exponent is less than one, the finite number of top firms occupy a dominant share in the market even if there are infinite number of firms.

One of the most important issues along this line of research is power laws associated with asset price fluctuations, and several models describing asset price dynamics were proposed Richmond et al. (2013), Abergel (2016). In particular, asset price bubbles were regarded as an important research topic. For example, the PACK and LPPL models simulated the price fluctuations of a stock during bubble periods Sornette (2004), Takayasu et al. (2010). In economics and finance, asset price bubbles are defined as the deviation of the price of an asset from its fundamental value. However, it is not easy to obtain information about fundamentals. For example, it is often stated by economists that the fundamental stock value of a company equals to the present discounted value of dividends delivered by the company in the years to come. However, it is hard to get a reliable estimate of future dividends; therefore, it is next to impossible to estimate fundamental stock prices. Without accurate information on fundamentals, it is impossible to detect bubbles. This is a serious issue for policy-makers, like governments and central banks, since the emergence and the burst of bubbles often lead to intolerable economic disasters, such as financial crisis.

The purpose of this paper is to propose a method to detect stock price bubbles in a timely manner. Our basic idea is closely related to a method we proposed as a way to detect bubbles in the context of real estate prices Ohnishi et al. (2012). In this study on real estate prices, we looked for houses that are similar in various respects, including the location of a house, the size of a house, and the age of a house. We argued that houses with similar attributes can be regarded as having similar fundamental values; therefore, the prices for these houses should be similar if there are no bubbles in the housing market. Based on this idea, we looked at the distribution of house prices for houses with similar attributes, showing that it is close to a log-normal distribution during normal periods, but it has a heavy upper tail during bubble periods. In the present paper, we apply this idea to stock markets to detect stock price bubbles.

In this paper, we use a dataset compiled by the Thomson Reuters Corporation that covers daily market capitalization and annual income statements of all the listed firms in NASDAQ and SSE from 1990 to 2015. This period includes the 2000 dot-com and 2007 Shanghai bubbles. We focus on the distribution of market capitalization in NASDAQ and the Shanghai stock exchange (SSE) for these bubbles. Kaizoji et al. showed that the upper tail of stock price distribution in the Tokyo stock exchange grew fat during the dot-com bubble period Kaizoji (2006a, b). However, if we accurately investigate the firm size in stock markets, not only the price but also the outstanding shares must be taken into consideration because share consolidation and splitting often occurs in the market.

The rest of the paper is organized as follows. Section 2 examines the power law of market capitalization distribution using an expansion of the Castillo and Puig test Fujimoto et al. (2011), Malevergne et al. (2011), Del Castillo and Puig (1999), Hisano and Mizuno (2010). In Sect. 3, we observe that the power law index fluctuates around one, depending on economic conditions, and tends to become smaller during bubble periods. In Sect. 4, we find that net assets are most reflected in market capitalization for firms listed in NASDAQ during non-bubble periods. The price-to-book ratio (PBR, P/B Ratio) distribution, which is defined as market capitalization divided by net assets, got fat during the bubble periods. These results suggest that speculative money is excessively concentrated on specific stocks during bubble periods. Section 5 concludes the paper.

## 2 Distribution of market capitalization

*x*is the market capitalization, \(\mu\) is a power law index, and \(x_0\) is a threshold. The index estimated with the maximum likelihood method is \(\mu =1.0\) (Fig. 1). Such a power law with \(\mu =1.0\) is called Zipf’s law.

Malevergne et al. Malevergne et al. (2011), who expanded the likelihood ratio test between exponential distribution and the truncated normal distribution introduced by Castillo and Puig Del Castillo and Puig (1999), tested the null hypothesis where, beyond a threshold, a distribution’s upper tail is characterized by a power law distribution against the alternative where the upper tail follows a log-normal distribution beyond the same threshold. This is known as the uniformly most powerful unbiased test. They identified the upper tail that follows a log-normal distribution or a power law distribution to detect a threshold by conducting this test.

*p*value of the significance level is set as 0.1 for Malevergne’s test. Therefore, the upper tail of market capitalization distribution can be well approximated by the power law function. On the other hand, a small range of market capitalization follows the log-normal distribution, as shown in the dashed curve in Fig. 1.

## 3 Power law index of market capitalization distribution

The upper tail of market capitalization distribution gets fat if the speculative money is concentrated on specific stocks. Such concentration of money tends to occur during bubble periods. The power law index became smaller during the 2000 dot-com and 2007 Shanghai bubbles.

The means of market capitalization for all the listed firms in NASDAQ and SSE were, respectively, about \(1.94 \,\times\, 10^9\) dollars on March 9, 2000 and \(3.92 \,\times\, 10^9\) dollars on December 4, 2007 during their bubble periods. On the other hand, the means were, respectively, about \(1.44\, \times\, 10^9\) and \(3.04 \times 10^9\) dollars on March 14, 2011 on the non-bubble periods. The means increased during the bubble periods.

Why does the mean increase during bubble periods? One possibility is that only a few firms increased the market capitalization drastically and raised the whole mean of market capitalization for all the listed firms. The black lines in Fig. 2, respectively, show the market capitalization distributions for firms listed on the NASDAQ during the 2000 dot-com bubble and on the SSE during the 2007 Shanghai bubble. The gray lines express the distributions on March 14, 2011 during non-bubble periods. The cumulative probability on the vertical axis at which the black line intersects with the gray line is \(P_{>} (x=2 \times 10^9 ) \approx \ 0.1\) in the dot-com bubble case. The top 10 % market capitalization in 2000 was higher than that in 2011, although the bottom 90 % market capitalization in 2000 was lower than that in 2011. Such a characteristic was also observed in the Shanghai bubble case (Fig. 2b). The cumulative probability of its crossing point is* P* _{>} (*x* = 1.2 × 10^{10}) ≈ 0.04. These results suggest that speculative funds concentrate on a very small set of stocks, leading to stock price bubbles.

## 4 Fundamental-adjusted market capitalization in NASDAQ

In economics, a financial bubble is defined by the gap between market and fundamental prices. We show that the gap expanded during the 2000 dot-com bubble period.

First, we look for a firm’s fundamentals that are mainly reflected in its market capitalization during non-bubble periods. We chose the following key financial variables, total assets, net assets, total revenue, operating income, net income, operating cash flow, and number of employees, as candidates of firm fundamentals and investigated the correlation between market capitalization and each key financial variable during non-bubble periods. Table 1 expresses the Kendall and Pearson correlation coefficients for firms listed on NASDAQ in 1997 and 2004. The correlation coefficients between market capitalization and net assets are the largest. However, other correlation coefficients are also high, suggesting the possibility of spurious correlation. To cope with this problem, we conduct random forest regression with market capitalization of individual firms as dependent variable and other financial variables as independent variables.

Kendall and Pearson correlation coefficients between market capitalization and each key financial variable in 1997 and 2004 in NASDAQ

Financial variable | 1997 Kendall | Pearson (>0) | 2004 Kendall | Pearson (>0) |
---|---|---|---|---|

Total assets | 0.461 | 0.684 | 0.526 | 0.755 |

Net assets | 0.622 | 0.824 | 0.681 | 0.880 |

Total revenue | 0.457 | 0.632 | 0.523 | 0.680 |

Operating income | 0.473 | 0.820 | 0.452 | 0.841 |

Net income | 0.410 | 0.805 | 0.420 | 0.851 |

Operating cash flow | 0.351 | 0.745 | 0.449 | 0.829 |

Number of employees | 0.396 | 0.582 | 0.466 | 0.658 |

*x*

_{i}(t) is the market capitalization of firm

*i*on the settlement day in year

*t*, and

*A*

_{ i }is its net assets in year

*t*. Fig. 5 shows the distributions of

*PBR*(1997) in the pre-bubble period, of

*PBR*(1997) in the bubble period, and of

*PBR*(2004) in the post-bubble period in NASDAQ. The distribution became fat during the bubble period and returned to its former position after the bubble burst.

## 5 Conclusion

We showed the distributions of market capitalization in NASDAQ and SSE. The upper tails of the distributions follow a power law. The power law index, which fluctuates around one depending on the economic conditions, became small during the 2000 dot-com and 2007 Shanghai bubble periods, suggesting that speculative money was excessively concentrated on a very small set of stocks, leading to stock price bubbles.

In economics and finance, a stock price bubble is defined by the gap between firm sizes in the stock market and in real economies. We used market capitalization and financial variables to estimate the firm sizes in stock markets and real economies. Using the regression coefficient of random forests for market capitalization and financial variables, we found that net assets are most reflected in the market capitalization for NASDAQ firms. For such firms, PBR is defined as market capitalization divided by net assets. The PBR distribution also got fat during the dot-com bubble period. This result means that the gap between firm sizes in asset markets and in real economies widened during the bubble period. This may be a useful tool for policy-makers, like governments and central banks, to detect stock price bubbles. Note that changes in the PBR distribution can be monitored at the daily, or even higher frequencies, so that policy-makers will be able to evaluate the risk of asset price bubbles almost on a real-time basis.

Both net assets and net income are greatly reflected in market capitalization for SSE firms. Market capitalization, divided by net income, becomes extremely big when the net income is close to zero. Therefore, the upper tail of the distribution of divided market capitalization sensitively responds to the fluctuation of net income. One future work will propose market capitalization that is adjusted by net assets and net income to investigate the 2007 Shanghai bubble.

Although market capitalization is made public every day, financial variables are usually announced only quarterly and annually. This difference in timescale complicates the estimation of daily gaps between firm sizes in stock markets and real economies. Another future work will nowcast the key financial variables every day.

## Notes

### Acknowledgments

This work was supported by JSPS KAKENHI Grant Numbers 16H05904 and 25220502. The authors thank the Yukawa Institute for Theoretical Physics at Kyoto University. Discussions during the YITP workshop YITP-W-15-15 on “Econophysics 2015” were useful to complete this work.

## References

- Abergel F et al. (2016) Limit order books. Cambridge University, OxfordGoogle Scholar
- Aoyama H et al. (2010) Econophysics and Companies: statistical life and death in complex business networks. Cambridge University, Cambridge, p 61-63Google Scholar
- Axtell RL (2001) Zipf distribution of US. Firm Sizes. Science 293:1818–1820Google Scholar
- Clementi F, Gallegati M (2016) The distribution of income and wealth: parametric modeling with the k-generalized family, Springer, BerlinGoogle Scholar
- Del Castillo J, Puig P (1999) The best test of exponentiality against singly truncated normal alternatives. J Am Stat Assoc 94(446):529–532CrossRefGoogle Scholar
- Fujimoto S, Ishikawa A, Mizuno T, Watanabe T (2011) A new method for measuring tail exponents of firm size distributions. Economics 5:2011–19Google Scholar
- Hisano R, Mizuno T (2010) Sales distribution of consumer electronics. Physica A 390:309–318CrossRefGoogle Scholar
- Kaizoji T (2006) Power laws and market crashes: empirical laws on bursting bubbles. Prog Theor Phys Suppl 162:165–172CrossRefGoogle Scholar
- Kaizoji T (2006) Precursors of market crashes: empirical laws of the Japan’s internet bubbles. Eur Phys J B 50:123–127CrossRefGoogle Scholar
- Malevergne Y, Pisarenko V, Sornette D (2011) Testing the pareto against the lognormal distributions with the uniformly most powerful unbiased test applied to the distribution of cities. Phys Rev E 83:036111CrossRefGoogle Scholar
- Mandelbrot B (1963) The variation of certain speculative prices. J Bus 36(4):394–419CrossRefGoogle Scholar
- Mantegna RN, Stanley HE (2000) Introduction to econophysics: correlations & complexity in finance. Cambridge University, CambridgeGoogle Scholar
- Mizuno T, Katori M, Takayasu H, Takayasu M (2002) Statistical and stochastic laws in the income of Japanese Companies. Empirical science of financial fluctuations -the advent of econophysics. Springer Verlag, Tokyo, p 321–330Google Scholar
- Mizuno T, Kurihara S, Takayasu M, Takayasu H (2003) Analysis of high-resolution foreign exchange data of USD-JPY for 13 years. Physica A 324:296–302CrossRefGoogle Scholar
- Mizuno T, Ishikawa A, Fujimoto S, Watanabe T (2012) Power laws in firm productivity. Prog Theor Phys Suppl 194:122–134CrossRefGoogle Scholar
- Ohnishi T, Mizuno T, Shimizu C, Watanabe T (2012) Power laws in real estate prices during bubble periods. Int J Mod Phy Conf Ser 16:61–81CrossRefGoogle Scholar
- Okuyama K, Takayasu M, Takayasu H (1999) Zipf’s Law in income distribution of companies. Physica A 269:125–131CrossRefGoogle Scholar
- Richmond P, Mimkes J, Hutzler D (2013) Econophysics and physical economics. Oxford University, OxfordGoogle Scholar
- Sornette D (2004) Critical phenomena in natural sciences: chaos, fractals, self organization, and disorder: concepts and tools. Springer, BerlinGoogle Scholar
- Stanley MHR, Buldyrev SV, Havlin S, Mantegna R, Salinger MA, Stanley HE (1995) Zipf plots and the size distribution of firms. Economics Lett. 49:453–457CrossRefGoogle Scholar
- Takayasu M, Watanabe K, Mizuno T & Takayasu H (2010) Theoretical Base of the PUCK-Model with application to Foreign Exchange Markets. Econophysics Approaches to Large-Scale Business Data and Financial Crisis. Springer:79-100Google Scholar

## Copyright information

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.