# Prediction: The Future of the USA Stock Market

**IMPORTANT ANNOUNCEMENT:**

**WE FIND FOR THE FIRST TIME A STRONG PROBABILITY THAT THE ANTIBUBBLE
DOCUMENTED HERE MAY HAS ENDED. THUS, ALL THE PREDICTIONS GIVEN BELOW ARE
CONDITIONED ON THE CONTINUATION OF THE ANTIBUBBLE. THIS IS DIFFERENT FROM
UNCONDITIONAL PREDICTIONS. AT THE END OF THIS UPDATE, WE PRESENT THE EVIDENCE
FOR THE POSSIBLE END OF THE BUBBLE.**

**AGAIN, THIS IS AN EXPERIMENT PERFORMED IN REAL TIME AND WE
WILL CONTINUE UPDATING EVERY MONTH TO STUDY IN DEPTH THIS TRANSITION, IF
CONFIRMED, OR THE RESUMING OF THE ANTIBUBBLE.**

**REMEMBER THAT this analysis is for academic purposes only
and must not be construed as investment or trading advice.**

Based on a theory of cooperative herding and imitation working both in bullish as well as in bearish regimes that we have developed in a series of papers, we have detected the existence of a clear signature of herding in the decay of the US S&P500 index since August 2000 with high statistical significance, in the form of strong log-periodic components.

Please refer to the following paper for a detailed description: D. Sornette and W.-X. Zhou, The US 2000-2002 Market Descent: How Much Longer and Deeper? Quantitative Finance 2 (6), 468-481 (2002) (e-print at http://arXiv.org/abs/cond-mat/0209065).

Why Stock Markets Crash: For a general presentation of the underlying concepts, theory, empirical tests and concrete applications, with a discussion of previous predictions, see the recent book, Why Stock Markets Crash.

__NEW:__ Testing the Stability
of the 2000-2003 US Stock Market Antibubble.

Since August 2000, the USA as well as most other western markets have depreciated
almost in synchrony according to complex patterns of drops and local rebounds.
We have proposed to describe this phenomenon using the concept of a log-periodic
power law (LPPL) antibubble, characterizing behavioral herding between investors
leading to a competition between positive and negative feedbacks in the pricing
process. Here, we test the possible existence of a regime switching in the
US S&P 500 antibubble. First, we find some evidence that the antibubble
might be on its way to cross-over to a shift in log-periodicity described by
a so-called second-order log-periodicity previously documented for the Japanese
Nikkei index in the 1990s (see last figure of this webpage). Second, we develop
a battery of tests to detect a possible end of the antibubble which suggest
that the antibubble is still alive and may still continue well in the future.
Our tests provide quantitative measures to diagnose the end of the antibubble,
when it will come. Such diagnostic is not instantaneous and requires probably
three to six months within the new regime before assessing its existence with
confidence. In conclusion, our prediction that the S&P 500 is going to
plunge progressively from the summer 2003 to bottom in 2004 seems to remain
basically intact, possibly with a few month delay extending almost to the end
of 2003 if the shift to the second-order log-periodicity is confirmed.

Fig. 1 shows 9 years of the evolution of the Japanese Nikkei index and almost 8 years of the USA S&P500 index, compared to each other after a translation described in the update of September 17, 2000 has been performed. The years are written on the horizontal axis (and marked by a tick on the axis where January 1 of that year occurs). This figure illustrates an analogy noted by several observers that our work has made quantitative. The oscillations with decreasing frequency which decorate an overall decrease of the stock markets are observed only in very special stock markets regimes, that we have terms log-periodic "anti-bubbles". By analyzing the mathematical structure of these oscillations, we quantify them into one (or several) mathematical formula(s) that can then be extrapolated to provide the prediction shown in the two following figures. Note that extrapolating is often a risky endeavor and needs to be justified. In our case, the extrapolations, which give the forecasts, are based on the belief that these equations offered below embody the major forces in the market at the macroscopic scale. This leads to the possibility of describing several probable scenarios. We do not believe in the existence of deterministic trajectories but we aim at targeting the most probable future paths.

Fig. 2 shows the predictions of the future of the US S&P 500 index performed on Aug. 24, 2002. The continuous line is the fit and its extrapolation, using our theory capturing investor herding and crowd behavior. The theory takes into account the competition between positive feedback (self-fulfilling sentiment), negative feedbacks (contrariant behavior and fundamental/value analysis) and inertia (everything takes time to adjust). Technically, we use what we call a "super-exponential power-law log-periodic function" derived from a first order Landau expansion of the logarithm of the price. The dashed line is the fit and its extrapolation by including in the function a second log-periodic harmonic. The two fits are performed using the index data from Aug. 9, 2000 to Aug. 24 2002 that are marked as black dots. The blue dots show the daily price evolution from Aug. 25, 2002 to Dec. 16, 2003. The large (respectively small) ticks in the abscissa correspond to January 1st (respectively to the first day of each quarter) of each year.

Fig. 3 shows the new predictions of the future of the US S&P 500 index using all the data from Aug. 9, 2000 to Dec. 16, 2003, illustrated by (continuous and dashed) black lines. Again, the continuous line is the fit and its extrapolation using the super-exponential power-law log-periodic function derived from the first order Landau expansion of the logarithm of the price, while the dashed line is the fit and its extrapolation by including in the function a second log-periodic harmonic. We also present the two previous fits (red lines) performed on Aug. 24, 2002 (shown in Fig. 2) for comparison, so as to provide an estimation of the sensitivity of the prediction and of its robustness as the price evolves. The blue dots show the daily price evolution from Aug. 9, 2000 to Dec. 16, 2003. The large (respectively small) ticks in the abscissa correspond to January 1st (respectively to the first day of each quarter) of each year.

Fig. 4 extends Figs. 2 and 3 by performing a sensitivity analysis on the simple log-periodic formula (continuous lines in Figs. 2 and 3), in order to assess the reliability and range of uncertainty of the prediction. Using the fit shown in black solid lines in Fig. 3, we have generated 10 realizations of an artificial S&P 500 by adding GARCH noise to the black solid line. GARCH means "generalized auto-regressive conditional heteroskedasticity". It is a process often taken as a benchmark in the financial industry and describes the fact that volatility is persistent. The innovations of the used GARCH noise have been drawn from a Student distribution with 3 degrees of freedom with a variance equal to that of the residuals of the fit of the real data to ensure the agreement between the statistical properties of these synthetic time series and the known properties of the empirical distribution of returns. The fits are shown as the bundle of 10 curves in magenta. This bundle of predictions is coherent and suggests a good robustness of the prediction. The typical width of the blue dots gives a sense of the variability that can be expected around this most probable scenario. The real S&P 500 price trajectory is shown as the red wiggly line.

Fig. 5 extends Figs. 2 and 3 by performing a sensitivity analysis on the log-periodic formula with a second log-periodic harmonic (dashed lines in Figs. 2 and 3), in order to assess the reliability and range of uncertainty of the prediction. Using the fit shown in dashed solid lines in Fig. 3, we have generated 10 realizations of an artificial S&P 500 by adding the GARCH noise (described in the previous caption of Fig. 4) to the dashed solid line. We have then fitted each of these 10 synthetic noisy clones of the S&P 500 by our log-periodic formula. This yields the 10 curves shown here in magenta. This test shows that the log-periodic formula with a second log-periodic harmonic (dashed lines in Figs. 2 and 3) is also providing stable scenarios: the precise timing of the highs and lows remain robust with respect to the realization of the noise. The real S&P 500 price trajectory is shown as the red wiggly line.

Fig. 6 analyses the VIX index by fitting it with our simple log-periodic formula. The VIX index is one of the world's most popular measures of investors' expectations about future stock market volatility (that is, risk). Note that a new methodology for constructing the VIX index has been effective on Sep. 22, 2003. See http://www.cboe.com/micro/vix/index.asp. For historical data, see http://www.cboe.com/micro/vix/historical.asp. The (new) VIX time series is shown as the red wiggly curve. We have followed the same procedure as for Figs. 4 and 5: (i) we fit the real VIX data with our simple log-periodic formula; (ii) we then generate 10 synthetic time series by adding GARCH noise to the fit; (iii) we redo a fit of each of the 10 synthetic time series by the simple log-periodic formula and thus obtain the bundle of 10 predictions shown as the magenta lines. Strikingly, we first observe that our log-periodic formula is able to account quite well for the behavior of the VIX index, strengthening the evidence that the market is presently in a strong herding (anti-bubble) phase. Note also the rather good stability of the predictions, suggesting a reasonable reliability.

Fig. 7 compares the fits of S&P 500 from Aug. 9, 2000 to Dec. 16, 2003 using the simple log-periodic formula shown as the continuous red line (which is the same as the continuous black line in Fig. 3) with the fit using the log-periodic formula derived from a second-order Landau expansion shown as the red dashed line. In our paper appeared in the December issue of Quantitative Finance in 2002, we stated that the simple log-periodic formula is enough to fit the S&P 500 antibubble and we thus concluded that the S&P 500 index had not yet entered the second phase in which the angular log-frequency may start its shift to another value, as did the 1990 Nikkei antibubble after about 2.5 years. However, we have found in our paper "Testing the Stability of the 2000-2003 US Stock Market Antibubble" that the market might have started to cross-over from the first-order to the second-order formula. This figure 7 further confirms our announcement that we now detect the occurrence of such a change of regime in log-frequency shift.

We also defined two probabilities, P1 (probability to continue the antibubble) and P2 (probability to have switched to another regime), in the aforementioned paper and calculated their values for seven different future scenarios until Mid-February [*], as shown in Table 2 (PDF document). Although there are still two months to go, it is now very probable that the market is somewhere between scenario (ii) and (iii). If confirmed, all the above updated predictions will turn out to be wrong. We should be able to confirm or deny this definitively in Feb. 2004.

[*]

(i) a random walk taking the S&P 500 to the value 1200;

(ii) a random walk taking the S&P 500 to 1100;

(iii) a random walk taking the S&P 500 to 1000;

(iv) a random walk taking the S&P 500 to 900;

(v) a random walk taking the S&P 500 to 800;

(vi) a continuation of the antibubble with noise obtained by a GARCH process
as described above;

(vii) a continuation of the antibubble with noise obtained by drawing at random
the residuals over six previous months.

**Cautionary note:**

Note that extrapolating is often a risky endeavor and needs to be justified.
In our case, the extrapolations, which give the forecasts, are based on the
belief that the theory and equations used above embody the major forces in
the market at the macroscopic scale. This leads to the possibility of describing
several probable scenarios. We do not believe in the existence of deterministic
trajectories but we aim at targeting the most probable future paths.

Tweet