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通过理论建模和分析个别公司,金融危机已经被广泛的研究。最近的严重的经济危机是否可以用物理界使用的类比法进行分析它是一个开放性且重要问题。尤其重要的是要确定它是一个独特的完全不同于其他大多数经济衰退的危机或是其他经济衰退所共同具备的可量化的特征，这些特征可以被用来进行准确预测。
虽然热力学模拟经济系统有个一个生动的但颇具非议的故事,但是这样的现象学描述可能提供了研究一个行业或经济系统的集体行为的可替代指标和方法。在目前的研究中,我们分析与范式，熵和分布的变化相关的两个指标,这些指标同时被应用于九个不同行业的盈利或亏损的美国公司。虽然我们的时间序列只包含19年的季度数据,我们的分析揭示了“空间”特点,这些特点可能在使用更少的公司数据采用更传统的分析方法无法被发现。相比更传统指标[13],数据分布变化能够提供预测经济衰退方法而忽略供微不足道的假阳性率，捕捉当经济衰退从一个行业蔓延至另一个行业时经济下行趋势变化的总体态势。两者的发现提供额外的证据表明过渡阶段范式可能是一个有用的用于分析经济系统态势的工具。
Financial crises have been studied extensively through theoretical modeling [1], [2] and analysis of individual companies [3]–[6]. It is an open and important question whether the recent gigantic economic crisis can be analyzed using analogies from the physical world. In particular, it is important to determine whether it is a unique crisis entirely different from most other economic recessions or has quantifiable characteristics that are shared by other recessions and can be utilized to accurately predict it.
Phase transition-like behaviors have been reported in many systems outside of traditional thermodynamics, including two-phase behavior in the buying and selling in financial markets [7], and stock crashes [8]–[11]. Although thermodynamic analogies for economic systems have a storied, but contentious history [12], such a phenomenological description may provide clues to alternative metrics and methodologies of studying the collective behavior of an industry or economic systems in general. In the present study, we analyze two metrics related to such a paradigm, entropy and distribution changes, as applied to positive and negative incomes of U.S. companies in nine different sectors. Although our time serieentropy and distribution s only contains quarterly data of 19 years, our analyses reveal “spatial” features of the data that can be otherwise hidden from more traditional analysis using fewer companies. In contrast to more traditional metrics [13], changes are able to provide predictive skill for economic downturns with a negligible false-positive rate and capture the collective or aggregate dynamics of the development of downturns as weakness propagates from one sector to another. Both findings provide additional evidence to suggest that the phase transition paradigm may be a useful tool for analyzing the dynamics of economic systems.
We examine pretax quarterly incomes, from the beginning of 1990 to the end of 2008, of thousands of U.S. companies in 9 sectors: Financial, Consumer Goods, Consumer Services, Basic Materials, Health Care, Industrials, Oil/Gas, Tech-Telecommunications, and Utilities [14]. Prior to 1990, income data for many companies in these sectors are incomplete, and thus meaningful analysis is not feasible. In and after 2009, income data in some of these sectors have been profoundly altered due to strong governmental interference, and therefore objective analysis is not possible.
In the entropy and distribution metrics described below, we consider negative and positive incomes as separate clusters; when the number of companies in each category or cluster is sufficiently large, these metrics are able to characterize the stability of the clusters. Note, however, during certain episodes, such as the early part of the 1990 recession, corporate dislocations due to bankruptcy, mergers, etc., can result in underestimation of the number of companies with negative incomes, which produces a delayed identification of the onset of an economic downturn.
We analyze Shannon entropy for the period preceeding and during the 2008 Financial Crisis, and find that this metric captures the dynamics of the onset of this crisis. Figure 1 shows from the first quarter of 2006 to the fourth quarter of 2008, for positive (black circles) and negative (red squares) incomes in 5 sectors (Financial, Consumer Goods, Consumer Services, Technology, and Health Care). The discrete probabilities in Fig. 1 are computed using a bin size of $15 million. Tests using bin sizes of $5, $10, and $20 million shifted the curves vertically, but the difference between for positive and negative incomes is largely independent of the bin size. The entropy of the distribution of positive incomes is almost constant, for all five sectors examined here. In contrast, the entropy of the distribution of negative incomes varies considerably with time. For example, for the Financial sector, it is markedly smaller than the entropy of positive incomes until the third quarter of 2007, when the entropy rises sharply. By the third quarter of 2007, the difference between the two entropies is almost zero, suggesting that the cluster of financial companies with losses is almost as strong (i.e., this configuration is nearly as stable) as the cluster of profitable financial companies, and signals weakness in the sector. From the third quarter of 2007 on, the entropy of the distribution of negative incomes is noticeably larger than for positive incomes, indicating that the cluster of financial companies with negative incomes is well-established and stronger than the cluster of financial companies with positive incomes, consistent with the progression of the 2008 Financial Crisis.
The time series of entropy for other sectors in the period preceeding and during the 2008 Financial Crisis show behavior consistent with the leading role of the Financial sector in the crisis, and propagation of the crisis from that sector into other sectors of the economy. Consumer Services (Fig. 1(c)) and Technology (Fig. 1(d)), for instance, do not show noticeable weakness until after third quarter of 2008. The temporal variations of entropy for Consumer Goods (Fig. 1(b)) shows similar behavior, though in second quarter of 2007 the negative income entropy nearly surpasses positive income entropy, suggesting the beginning of sector weakness at that time.
The propagation of weakness from one sector to another during the 2008 Financial Crisis, as revealed in positive and negative income entropy, is also seen during other periods of market decline, such as the 1999–2003 technology stock fueled decline (Fig. 2) and the early 1990 recession (Fig. 3). Therefore, at least over the 19 year record examined here, the behaviors seen in entropy are not unique features of the current crisis, but are rather common features shared by other periods of economic recession. Alternatively, we may say that the current crisis, though more serious than other recent recessions, from the entropy perspective differs in degree but not in kind.
To quantitatively assess the skill of entropy in predicting economic downturns, we define the onset of a downturn as when the difference between the entropy of negative and positive incomes first becomes positive. By this criterion, using Figs. 1–3 and 4(a), we identify recession start times as the 4th quarter of 2007, 2nd quarter of 2001, and 1st quarter of 1991, respectively, with no false-positives (Fig. 4(a); note the three positive entropy differences indicated by Consumer Services in early 1990's all belonged to or indicated a continuation of the 1990 recession; none was a false-positive). This compares well with the National Bureau of Economic Research (NBER)'s business cycle contraction onset dating determinations during this period, which are 4th quarter of 2007, 1st quarter of 2001, and 3rd quarter of 1990 [18]; our identification of the downturn times matches the 2007 onset and is delayed by 1 and 2 quarters, respectively, for the 2001 and 1990 onsets. If we relax our criterion for onset of a downturn to be the first time when the entropy of negative income is of positive income entropy, then our identified recession start times for the 2001 and 1990 downturns become 4th quarter of 2000, and 3rd quarter of 1990, with false-positives still zero. With this relaxed criterion, the entropy metric identifies downturn onset 0 quarter ahead, 1 quarter ahead, and 0 quarter ahead of the NBER onsets for the 2007, 2001, and 1990 downturns, respectively. The NBER business cycles determinations are, however, retrospectives. If we compare the dates of our entropy-based downturn onset identifications with the dates the NBER announced their onset identifications, our entropy-based identifications preceed the NBER announcements [18]; for the 2008 crisis, the entropy-based identification occurs 1 year earlier.
Additionally, because the NBER business cycle determinations are retrospective, they are not useful as comparators for evaluating the predictive skill of the entropy metric. To provide such a comparator, we define two “trivial” alternative metrics of recession onset: the time when total pretax income becomes negative, and the time when the ratio of incomes (where denotes year and denotes quarter, so means 1st quarter income in 1990), is smaller than a certain threshold value. The first alternative metric provides a rudimentary snapshot of sector cash flow, while the second metric provides a crude measure of gross domestic product (GDP) contraction/expansion. In the case of the time series of total pretax income, recession times are identified with substantial longer delay than seen in entropy (Fig. 4(b)). (We note as an aside that the variation of the total pretax income seen around the 1990 recession suggests that the delayed identification of that recession by the entropy metric is likely due to underestimation of negative incomes, not the entropy metric itself.) The GDP contraction/expansion-like metric, either identifies similarly delayed recession start times, compared to those provided by total pretax income, or yields a substantial number of false-positives, depending on the threshold used to define downturns (Fig. 4(c)).
The entropy variations shown in Figs. 1–3 and 4(a) suggest a phase transition propagation-like phenomenon where weakness begins in one sector and gradually propagates to others with different time delays. Entropy variations, however, do not yield much insight regarding the mechanism behind this transition-like behavior. Distribution analysis of positive and negative incomes provides tools for further analysis. Specifically, the equal-log-bin technique [17] is used to reliably estimate the CCDF from the sparse data in our time series. We find that like many other economic time series [19]–[22], positive income (denoted as ) typically follows a Pareto distribution described by Eq. (3). The black circles in Figs. 5(a–f) are illustrative of positive income distributions in general, and the straight lines in the tail region of the log-log plots clearly indicate a heavy-tailed distribution. While the shape of the positive income distribution is largely independent of the economic health of the sector, the parameter is not universal; instead, it correlates well with the health of the economy – it attains a larger value when a recession is more serious.
To summarize, the entropy variations shown in Figs. 1–2 suggest a phase transition propagation-like phenomenon where weakness begins in one sector and gradually propagates to others with different time delays. More importantly, it has been shown that the recent gigantic recession differs from earlier ones in degree but not in kind, and that entropy is an effective indicator of economic recessions. The favorable comparison of our entropy based recession predictions with those of NBER suggests that entropy can be en effective indicator for operational monitoring of economic health.
This transition from a largely non-Pareto distributed loss to a Pareto-distributed loss with an even heavier tail than for profits, in conjunction with a sharp contemporaneous increase in the entropy of the loss distribution, demonstrates a change akin to a phase transition in a physical system that undergoes large fluctuations with power-law behavior. (A similar phenomenon has been observed with earthquakes, where aftershocks are known to follow Omori's law [23], which is also a Pareto-like power law distribution under certain conditions.) The transition between these economic phases, as in thermodynamics, may depend on the dynamics of a network of interacting entities [7], [24], which in the economic case consists of companies connected via financial instruments, business relations, etc. The dynamics of the transition from profits to losses would thus be not only a function of external forcing (such as policy changes), but also the structure of the network (and of the negative income cluster in particular). The theory of evolutionary games may also shed light on the nature of the transitions [25].
While the above implications and the ultimate validity of the thermodynamic analogy is an open question, in the present work we have seen that the proposed entropy and distribution metrics appear to offer predictive skill and capture the dynamics of the propagation of weakness between economic sectors, and thus may offer an additional means of monitoring the health of the economy. Finally, the similarity between propagation of economic weakness and propagation in phase transitions suggests that the phase transition paradigm may be useful in understanding the dynamics of economic downturns.
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