Viscoelasticity and pattern formations in stock market indices

The viscoelastic and thermodynamic properties of four stock indices, namely, DJI, Nasdaq-100, Nasdaq-Composite, and S&P were analyzed for a period of 30 years from 1986 to 2015. The asset values (or index) can be placed into Aristotelian ‘potentiality-actuality’ framework by using scattering diagram. Thus, the index values can be transformed into vectorial forms in a scattering diagram, and each vector can be split into its horizontal and vertical components. According to viscoelastic theory, the horizontal component represents the conservative, and the vertical component represents the dissipative behavior. The related storage and the loss modulus of these components are determined and then work-like and heat-like terms are calculated. It is found that the change of storage and loss modulus with Wiener noise (W) exhibit interesting patterns. The loss modulus shows a featherlike pattern, whereas the storage modulus shows figurative man-like pattern. These patterns are formed due to branchings in the system and imply that stock indices do have a kind of ‘fine-order’ which can be detected when the change of modulus values are plotted with respect to Wiener noise. In theoretical calculations it is shown that the tips of the featherlike patterns stay at negative W values, but get closer to W = 0 as the drift in the system increases. The shift of the tip point from W = 0 indicates that the price change involves higher number of positive Wiener number corrections than the negative Wiener. The work-like and heat-like terms also exhibit patterns but with different appearance than modulus patterns. The decisional changes of people are reflected as the arrows in the scattering diagram and the propagation path of these vectors resemble the path of crack propagation. The distribution of the angle between two subsequent vectors shows a peak at 90°, indicating that the path mostly obeys the crack path occurring in hard objects. Entropy mimics the Wiener noise in the evolution of stock index value although they describe different properties. Entropy fluctuates at fast increase and fast fall of index value, and fluctuation becomes very high at minimum values of the index. The curvature of a circle passing from the two ends of the vector and the point of intersection of its horizontal and vertical components designates the reactivity involved in the market and the radius of circle behaves somehow similar to entropy and Wiener noise. The change of entropy and Wiener noise with radius exhibits patterns with four branches.