Are financial market crashes predictable?

D. Sornette (ETH Zurich)

published translated in Greek by Prof. Dimitrios D. Thomakos, associate professor of economics at the University of Peloponnese, Greece, in a special magazine issue on forecasting financial markets on Athens' biggest daily newspapers (NEA - "The News").

Stock market crashes are momentous financial events that are fascinating to academics and practitioners alike. According to the standard academic textbook world view that markets are efficient, only the revelation of a dramatic piece of information can cause a crash, yet in reality even the most thorough post-mortem analyses are typically inconclusive as to what this piece of information might have been. For traders and investors, the fear of a crash is a perpetual source of stress, and the onset of the event itself always ruins the lives of some of them. Most approaches to explain crashes search for possible mechanisms or effects that operate at very short time scales (hours, days or weeks at most). Other researchers have suggested market crashes may have endogenous origins.

Associated with these questions is the problem of determining if there exist qualifying signatures in the statistical properties of time series of price returns that make crashes, and more generally large losses, different from the rest of the population? Can we show that crashes are outliers or “kings” (in the sense of forming a different statistical population with extreme properties)?

Financial markets are not the only systems with extreme events. Financial markets constitute one among many other systems exhibiting a complex organization and dynamics with similar behaviour. Systems with a large number of mutually interacting parts, often open to their environment, self-organize their internal structure and their dynamics with novel and sometimes surprising macroscopic (”emergent”) properties. The complex system approach, which involves “seeing” inter-connections and relationships, i.e., the whole picture as well as the component parts, is nowadays pervasive in modern control of engineering devices and business management. It is also plays an increasing role in most of the scientific disciplines, including biology (biological networks, ecology, evolution, origin of life, immunology, neurobiology, molecular biology, etc), geology (plate-tectonics, earthquakes and volcanoes, erosion and landscapes, climate and weather, environment, etc.), economy and social sciences (including cognition, distributed learning, interacting agents, etc.). There is a growing recognition that progress in most of these disciplines, in many of the pressing issues for our future welfare as well as for the management of our everyday life, will need such a systemic complex system and multidisciplinary approach. This view tends to replace the previous ”analytical” approach, consisting of decomposing a system in components, such that the detailed understanding of each component was believed to bring understanding in the functioning of the whole.

In a culmination of more than ten years of research on the science of complex system, we have thus challenged the standard economic view that stock markets are both efficient and unpredictable. The main concepts that are needed to understand stock markets are imitation, herding, self-organized cooperativity and positive feedbacks, leading to the development of endogenous instabilities. According to this theory, local effects such as interest raises, new tax laws, new regulations and so on, invoked as the cause of the burst of a given bubble leading to a crash, are only one of the triggering factors but not the fundamental cause of the bubble collapse. We propose that the true origin of a bubble and of its collapse lies in the unsustainable pace of stock market price growth based on self-reinforcing over-optimistic anticipation. As a speculative bubble develops, it becomes more and more unstable and very susceptible to any disturbance.

In a given financial bubble, it is the expectation of future earnings rather than present economic reality that motivates the average investor. History provides any examples of bubbles driven by unrealistic expectations of future earnings followed by crashes. The same basic ingredients are found repeatedly. Markets go through a series of stages, beginning with a market or sector that is successful, with strong fundamentals. Credit expands, and money flows more easily. (Near the peak of Japan's bubble in 1990, Japan's banks were lending money for real estate purchases at more than the value of the property, expecting the value to rise quickly.) As more money is available, prices rise. More investors are drawn in, and expectations for quick profits rise. The bubble expands, and then bursts. In other words, fuelled by initially well-founded economic fundamentals, investors develop a self-fulfilling enthusiasm by an imitative process or crowd behaviour that leads to the building of castles in the air, to paraphrase Malkiel (1990). Furthermore, the causes of the crashes on the US markets in 1929, 1987, 1998 and in 2000 belongs to the same category, the difference being mainly in which sector the bubble was created: in 1929, it was utilities; in 1987, the bubble was supported by a general deregulation of the market with many new private investors entering the market with very high expectations with respect to the profit they would make; in 1998, it was an enormous expectation with respect to the investment opportunities in Russia that collapsed; before 2000, it was extremely high expectations with respect to the Internet, telecommunications, etc., that fuelled the bubble. In 1929, 1987 and 2000, the concept of a ``new economy'' was each time promoted as the rational origin of the upsurge of the prices.

Mathematically, large stock market crashes are the social analogues of so-called critical points studied in the statistical physics community in relation to magnetism, melting, and other phase transformation of solids, liquids, gas and other phases of matter (Sornette, 2000). This theory is based on the existence of a cooperative behaviour of traders imitating each other which leads to progressively increasing build-up of market cooperativity, or effective interactions between investors, often translated into accelerating ascent of the market price over months and years before the crash. According to this theory, a crash occurs because the market has entered an unstable phase and any small disturbance or process may have triggered the instability.

Think of a ruler held up vertically on your finger: this very unstable position will lead eventually to its collapse, as a result of a small (or absence of adequate) motion of your hand or due to any tiny whiff of air. The collapse is fundamentally due to the unstable position; the instantaneous cause of the collapse is secondary. In the same vein, the growth of the sensitivity and the growing instability of the market close to such a critical point might explain why attempts to unravel the local origin of the crash have been so diverse. Essentially, anything would work once the system is ripe. In this view, a crash has fundamentally an endogenous or internal origin and exogenous or external shocks only serve as triggering factors.

As a consequence, the origin of crashes is much more subtle than often thought, as it is constructed progressively by the market as a whole, as a self-organizing process. In this sense, the true cause of a crash could be termed a systemic instability. This leads to the possibility that the market anticipates the crash in a subtle self-organized and cooperative fashion, hence releasing precursory "fingerprints" observable in the stock market prices (Sornette and Johansen, 2001; Sornette, 2003). These "fingerprints" have been modelled by "log-periodic power laws" (LPPL), which are beautiful mathematical patterns associated with the mathematical generalization of the notion of fractals to complex imaginary dimensions (Sornette, 1998). We refer to the book (Sornette, 2003) for a detailed description and the review of many empirical tests and of several forward predictions. In particular, we predicted in January 1999 that Japan's Nikkei index would rise 50 percent by the end of that year, at a time when other economic forecasters expected the Nikkei to continue to fall, and when Japan's economic indicators were declining. The Nikkei rose more than 49 percent during that time. We also successfully predicted several short-term changes of trends in the US market and in the Nikkei. Or course, we are not able to predict stock markets with anything close to 100 percent accuracy - just as weather forecasting cannot say with absolute certainty what the weekend weather will be - but our predictions will become more accurate as we refine our methods.

Our theory of collective behaviour predicts robust signatures of speculative phases of financial markets, both in accelerating bubbles and decreasing prices (see below). These precursory patterns have been documented for essentially all crashes on developed as well as emergent stock markets. Accordingly, the crash of October 1987 is not unique but a representative of an important class of market behaviour, underlying also the crash of October 1929 (Galbraith, 1997) and many others (Kindleberger, 2000; Sornette, 2003).

Stock market crashes are often unforeseen by most people, especially economists. One reason why predicting complex systems is difficult is that we have to look at the forest rather than the trees, and almost nobody does that. Our approach tries to avoid that trap. From the tulip mania, where tulips worth tens of thousands of dollars in present U.S. dollars became worthless a few months later, to the U.S. bubble in 2000, the same patterns occur over the centuries. Today we have electronic commerce, but fear and greed remain the same. Humans remain endowed with basically the same qualities today as they were in the 17th century.

In summary, bubbles and crashes are ubiquitous to human activity: we as humans are rarely satisfied with the Status Quo; we tend to be over-optimistic with respect to future prospects and, as social animals, we herd to find comfort in being (right or wrong) with the crowd. This leads to human activities being punctuated by bubbles and their corrections. The bubbles may come as a result of expectations of the future returns from new technology, such as in the exploration of the solar system, of the human biology or new computer and information technologies. I contend that this trait allows us as specie to take risks to innovate with extraordinary successes which would not arise otherwise. Thus, bubbles and crashes, the hallmark of humans, are perhaps our most constructive collective process. But they may also undermine our quest for stability. We thus have to be prepared and adapted to the systemic instabilities that are part of us, part of our collective organization, ... and which will no doubt recur again perhaps with even more violent effects in the coming decade.