Alternative methods of financial analyses
AbstractClassical methods of analyses assume independence among the market variables and try to isolate the influence they have on one another. Simplifying any of these variables or eliminating the relationship between them can substantionally change the system behavior. These models of independent variables, a normal distribution and a brownian motion fail to model empirical evidence. The EMH, therefore, cannot be a constistent model of the financial markets. The new methods of the financial analyses are based on self-similar patterns on the different time scales that exhibit characteristic of fractal geometry. Self -similarity means that small and large shapes are largely identical except for scale. The major indices S&P500 and DJIA present the substantial difference between expected normal values and data of empirical evidence. The self-similar patterns on time scales (day, week, month and quarter) exhibit the same substantial differences. These differences are closely related to the Cauchy or the lognormal distributions. The Hurst exponent is a convenient measure of the persistence or the antipersistence of the time series. DJIA shows the persistent phenomenon before World War II, however, since 1945 there is the strong antipersistent tendency and turbulence. In contrast to the persistent tendency, which can lead to unforseeable changes, the antipersistent behavior is a mean-reverting process that stabilizes the persistent feature of S&P500 after World Word II. The use of neural networks technology envolves to find the best model of DJIA, S&P500 and PX50. This technology works well in the period of the antipersistent system behavior.
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Bibliographic InfoArticle provided by The Czech Econometric Society in its journal Bulletin of the Czech Econometric Society.
Volume (Year): 10 (2003)
Issue (Month): 18 ()
self-similar patterns; persistence; anitpersistence; EMH; Hurst exponent; Lyapunov exponent; neural networks; DJIA; S&P500; PX50;
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